WorldWideScience

Sample records for single objective optimization

  1. Optimal scheduling of micro grids based on single objective programming

    Science.gov (United States)

    Chen, Yue

    2018-04-01

    Faced with the growing demand for electricity and the shortage of fossil fuels, how to optimally optimize the micro-grid has become an important research topic to maximize the economic, technological and environmental benefits of the micro-grid. This paper considers the role of the battery and the micro-grid and power grid to allow the exchange of power not exceeding 150kW preconditions, the main study of the economy to load for the goal is to minimize the electricity cost (abandonment of wind), to establish an optimization model, and to solve the problem by genetic algorithm. The optimal scheduling scheme is obtained and the utilization of renewable energy and the impact of the battery involved in regulation are analyzed.

  2. Multi-objective compared to single-objective optimization with application to model validation and uncertainty quantification

    Energy Technology Data Exchange (ETDEWEB)

    Schulze-Riegert, R.; Krosche, M.; Stekolschikov, K. [Scandpower Petroleum Technology GmbH, Hamburg (Germany); Fahimuddin, A. [Technische Univ. Braunschweig (Germany)

    2007-09-13

    History Matching in Reservoir Simulation, well location and production optimization etc. is generally a multi-objective optimization problem. The problem statement of history matching for a realistic field case includes many field and well measurements in time and type, e.g. pressure measurements, fluid rates, events such as water and gas break-throughs, etc. Uncertainty parameters modified as part of the history matching process have varying impact on the improvement of the match criteria. Competing match criteria often reduce the likelihood of finding an acceptable history match. It is an engineering challenge in manual history matching processes to identify competing objectives and to implement the changes required in the simulation model. In production optimization or scenario optimization the focus on one key optimization criterion such as NPV limits the identification of alternatives and potential opportunities, since multiple objectives are summarized in a predefined global objective formulation. Previous works primarily focus on a specific optimization method. Few works actually concentrate on the objective formulation and multi-objective optimization schemes have not yet been applied to reservoir simulations. This paper presents a multi-objective optimization approach applicable to reservoir simulation. It addresses the problem of multi-objective criteria in a history matching study and presents analysis techniques identifying competing match criteria. A Pareto-Optimizer is discussed and the implementation of that multi-objective optimization scheme is applied to a case study. Results are compared to a single-objective optimization method. (orig.)

  3. Design optimization of shell-and-tube heat exchangers using single objective and multiobjective particle swarm optimization

    International Nuclear Information System (INIS)

    Elsays, Mostafa A.; Naguib Aly, M; Badawi, Alya A.

    2010-01-01

    The Particle Swarm Optimization (PSO) algorithm is used to optimize the design of shell-and-tube heat exchangers and determine the optimal feasible solutions so as to eliminate trial-and-error during the design process. The design formulation takes into account the area and the total annual cost of heat exchangers as two objective functions together with operating as well as geometrical constraints. The Nonlinear Constrained Single Objective Particle Swarm Optimization (NCSOPSO) algorithm is used to minimize and find the optimal feasible solution for each of the nonlinear constrained objective functions alone, respectively. Then, a novel Nonlinear Constrained Mult-objective Particle Swarm Optimization (NCMOPSO) algorithm is used to minimize and find the Pareto optimal solutions for both of the nonlinear constrained objective functions together. The experimental results show that the two algorithms are very efficient, fast and can find the accurate optimal feasible solutions of the shell and tube heat exchangers design optimization problem. (orig.)

  4. Analysis and optimization with ecological objective function of irreversible single resonance energy selective electron heat engines

    International Nuclear Information System (INIS)

    Zhou, Junle; Chen, Lingen; Ding, Zemin; Sun, Fengrui

    2016-01-01

    Ecological performance of a single resonance ESE heat engine with heat leakage is conducted by applying finite time thermodynamics. By introducing Nielsen function and numerical calculations, expressions about power output, efficiency, entropy generation rate and ecological objective function are derived; relationships between ecological objective function and power output, between ecological objective function and efficiency as well as between power output and efficiency are demonstrated; influences of system parameters of heat leakage, boundary energy and resonance width on the optimal performances are investigated in detail; a specific range of boundary energy is given as a compromise to make ESE heat engine system work at optimal operation regions. Comparing performance characteristics with different optimization objective functions, the significance of selecting ecological objective function as the design objective is clarified specifically: when changing the design objective from maximum power output into maximum ecological objective function, the improvement of efficiency is 4.56%, while the power output drop is only 2.68%; when changing the design objective from maximum efficiency to maximum ecological objective function, the improvement of power output is 229.13%, and the efficiency drop is only 13.53%. - Highlights: • An irreversible single resonance energy selective electron heat engine is studied. • Heat leakage between two reservoirs is considered. • Power output, efficiency and ecological objective function are derived. • Optimal performance comparison for three objective functions is carried out.

  5. Validity of single-cycle objective functions for multicycle reload design optimization

    International Nuclear Information System (INIS)

    Kropaczek, D.J.; McElroy, J.; Turinsky, P.J.

    1993-01-01

    Beyond the equilibrium cycle scoping calculations used for determining numbers of feed assemblies and enrichment estimates, multicycle reload design currently consists of stagewise optimization of single-cycle core loading patterns, typically extending over a short-term planning horizon of perhaps three reload cycles. Particularly in transition cycles, however, optimizing a loading pattern over a single cycle for a stated objective, such as minimum core leakage, may have an adverse impact on subsequent cycles. The penalties paid may be in the form of reduced thermal margin or an increase in feed enrichment due to insufficient reactivity carryover from the open-quotes optimizedclose quotes cycle. In view of current practices, a study was performed that examined the behavior of the loading pattern as a function of the objective functions selected as implemented in the stagewise optimization of single-cycle core loading patterns from initial transition cycle through equilibrium using the FORMOSA-P code. The objective functions studied were region average discharge burnup maximization (with enrichment search) and feed enrichment minimization. It is noted at the beginning that the maximization of region average discharge has no meaning for the equilibrium cycle because region average discharge burnup is explicitly set by the feed size and cycle length independent of the loading pattern. In the nonequilibrium cycle, however, it was reasoned that this objective would provide the maximum reactivity carryover throughout the transition and thus have a direct effect on minimizing the multicycle levelized fuel cost

  6. An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization

    Directory of Open Access Journals (Sweden)

    Yuksel Celik

    2013-01-01

    Full Text Available Marriage in honey bees optimization (MBO is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm’s performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.

  7. Multi-Objective Optimization of a Turbofan for an Advanced, Single-Aisle Transport

    Science.gov (United States)

    Berton, Jeffrey J.; Guynn, Mark D.

    2012-01-01

    Considerable interest surrounds the design of the next generation of single-aisle commercial transports in the Boeing 737 and Airbus A320 class. Aircraft designers will depend on advanced, next-generation turbofan engines to power these airplanes. The focus of this study is to apply single- and multi-objective optimization algorithms to the conceptual design of ultrahigh bypass turbofan engines for this class of aircraft, using NASA s Subsonic Fixed Wing Project metrics as multidisciplinary objectives for optimization. The independent design variables investigated include three continuous variables: sea level static thrust, wing reference area, and aerodynamic design point fan pressure ratio, and four discrete variables: overall pressure ratio, fan drive system architecture (i.e., direct- or gear-driven), bypass nozzle architecture (i.e., fixed- or variable geometry), and the high- and low-pressure compressor work split. Ramp weight, fuel burn, noise, and emissions are the parameters treated as dependent objective functions. These optimized solutions provide insight to the ultrahigh bypass engine design process and provide information to NASA program management to help guide its technology development efforts.

  8. Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

    Science.gov (United States)

    Rai, Man Mohan

    2006-01-01

    Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more

  9. Single and multiple objective biomass-to-biofuel supply chain optimization considering environmental impacts

    Science.gov (United States)

    Valles Sosa, Claudia Evangelina

    Bioenergy has become an important alternative source of energy to alleviate the reliance on petroleum energy. Bioenergy offers diminishing climate change by reducing Green House Gas Emissions, as well as providing energy security and enhancing rural development. The Energy Independence and Security Act mandate the use of 21 billion gallons of advanced biofuels including 16 billion gallons of cellulosic biofuels by the year 2022. It is clear that Biomass can make a substantial contribution to supply future energy demand in a sustainable way. However, the supply of sustainable energy is one of the main challenges that mankind will face over the coming decades. For instance, many logistical challenges will be faced in order to provide an efficient and reliable supply of quality feedstock to biorefineries. 700 million tons of biomass will be required to be sustainably delivered to biorefineries annually to meet the projected use of biofuels by the year of 2022. Approaching this complex logistic problem as a multi-commodity network flow structure, the present work proposes the use of a genetic algorithm as a single objective optimization problem that considers the maximization of profit and the present work also proposes the use of a Multiple Objective Evolutionary Algorithm to simultaneously maximize profit while minimizing global warming potential. Most transportation optimization problems available in the literature have mostly considered the maximization of profit or the minimization of total travel time as potential objectives to be optimized. However, on this research work, we take a more conscious and sustainable approach for this logistic problem. Planners are increasingly expected to adopt a multi-disciplinary approach, especially due to the rising importance of environmental stewardship. The role of a transportation planner and designer is shifting from simple economic analysis to promoting sustainability through the integration of environmental objectives. To

  10. An Enhanced Memetic Algorithm for Single-Objective Bilevel Optimization Problems.

    Science.gov (United States)

    Islam, Md Monjurul; Singh, Hemant Kumar; Ray, Tapabrata; Sinha, Ankur

    2017-01-01

    Bilevel optimization, as the name reflects, deals with optimization at two interconnected hierarchical levels. The aim is to identify the optimum of an upper-level  leader problem, subject to the optimality of a lower-level follower problem. Several problems from the domain of engineering, logistics, economics, and transportation have an inherent nested structure which requires them to be modeled as bilevel optimization problems. Increasing size and complexity of such problems has prompted active theoretical and practical interest in the design of efficient algorithms for bilevel optimization. Given the nested nature of bilevel problems, the computational effort (number of function evaluations) required to solve them is often quite high. In this article, we explore the use of a Memetic Algorithm (MA) to solve bilevel optimization problems. While MAs have been quite successful in solving single-level optimization problems, there have been relatively few studies exploring their potential for solving bilevel optimization problems. MAs essentially attempt to combine advantages of global and local search strategies to identify optimum solutions with low computational cost (function evaluations). The approach introduced in this article is a nested Bilevel Memetic Algorithm (BLMA). At both upper and lower levels, either a global or a local search method is used during different phases of the search. The performance of BLMA is presented on twenty-five standard test problems and two real-life applications. The results are compared with other established algorithms to demonstrate the efficacy of the proposed approach.

  11. A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

    Energy Technology Data Exchange (ETDEWEB)

    Tumuluru, Jaya Shankar [Idaho National Laboratory; McCulloch, Richard Chet James [Idaho National Laboratory

    2015-07-01

    In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

  12. Retrofitting of heat exchanger networks involving streams with variable heat capacity: Application of single and multi-objective optimization

    International Nuclear Information System (INIS)

    Sreepathi, Bhargava Krishna; Rangaiah, G.P.

    2015-01-01

    Heat exchanger network (HEN) retrofitting improves the energy efficiency of the current process by reducing external utilities. In this work, HEN retrofitting involving streams having variable heat capacity is studied. For this, enthalpy values of a stream are fitted to a continuous cubic polynomial instead of a stepwise approach employed in the previous studies [1,2]. The former methodology is closer to reality as enthalpy or heat capacity changes gradually instead of step changes. Using the polynomial fitting formulation, single objective optimization (SOO) and multi-objective optimization (MOO) of a HEN retrofit problem are investigated. The results obtained show an improvement in the utility savings, and MOO provides many Pareto-optimal solutions to choose from. Also, Pareto-optimal solutions involving area addition in existing heat exchangers only (but no new exchangers and no structural modifications) are found and provided for comparison with those involving new exchangers and structural modifications as well. - Highlights: • HEN retrofitting involving streams with variable heat capacities is studied. • A continuous approach to handle variable heat capacity is proposed and tested. • Better and practical solutions are obtained for HEN retrofitting in process plants. • Pareto-optimal solutions provide many alternate choices for HEN retrofitting

  13. Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review.

    Science.gov (United States)

    Bonyadi, Mohammad Reza; Michalewicz, Zbigniew

    2017-01-01

    This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

  14. Calibration and Validation Parameter of Hydrologic Model HEC-HMS using Particle Swarm Optimization Algorithms – Single Objective

    Directory of Open Access Journals (Sweden)

    R. Garmeh

    2016-02-01

    model that simulates both wet and dry weatherbehavior.Programming of HEC –HMS has been done by MATLAB and techniques such as elite mutation and creating confusion have been used in order to strengthen the algorithm and improve the results. The event-based HEC-HMS model simulatesthe precipitation-runoff process for each set of parameter values generated by PSO. Turbulentand elitism with mutation are also employed to deal with PSO premature convergence. The integrated PSO-HMS model is tested on the Kardeh dam basin located in the Khorasan Razavi province. Results and Discussion: Input parameters of hydrologic models are seldomknown with certainty. Therefore, they are not capable ofdescribing the exact hydrologic processes. Input data andstructural uncertainties related to scale and approximationsin system processes are different sources of uncertainty thatmake it difficult to model exact hydrologic phenomena.In automatic calibration, the parameter values dependon the objective function of the search or optimization algorithm.In characterizing a runoff hydrograph, threecharacteristics of time-to-peak, peak of discharge and totalrunoff volume are of the most importance. It is thereforeimportant that we simulate and observe hydrographs matchas much as possible in terms of those characteristics. Calibration was carried out in single objective cases. Model calibration in single-objective approach with regard to the objective function in the event of NASH and RMSE were conducted separately.The results indicated that the capability of the model was calibrated to an acceptable level of events. Continuing calibration results were evaluated by four different criteria.Finally, to validate the model parameters with those obtained from the calibration, tests perfomed indicated poor results. Although, based on the calibration and verification of individual events one event remains, suggesting set is a possible parameter. Conclusion: All events were evaluated by validations and the

  15. Non-convex multi-objective optimization

    CERN Document Server

    Pardalos, Panos M; Žilinskas, Julius

    2017-01-01

    Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in...

  16. NLP modeling for the optimization of LiBr-H_2O absorption refrigeration systems with exergy loss rate, heat transfer area, and cost as single objective functions

    International Nuclear Information System (INIS)

    Mussati, Sergio F.; Gernaey, Krist V.; Morosuk, Tatiana; Mussati, Miguel C.

    2016-01-01

    Highlights: • A NLP model is used for simultaneous optimization of sizes and operating conditions. • Total exergy loss rate and transfer area are optimized as single objective functions. • Theoretical and practical bounds for cost optimization problems are computed. • A systematic solution strategy is proposed for total annual cost optimization. • Relevance of components is ranked by heat transfer area, exergy loss rate, and cost. - Abstract: Based on a nonlinear mathematical programming model, the sizes and operating conditions of the process units of single-effect absorption refrigeration systems operating with a LiBr–H_2O solution are optimized for a specified cooling capacity by minimizing three single objective functions: the total exergy loss rate, the total heat transfer area, and the total annual cost of the system. It was found that the optimal solution obtained by minimization of the total exergy loss rate provides “theoretical” upper bounds not only for the total heat transfer area of the system but also for each process unit and all stream temperatures, while the optimal solution obtained by minimization of the total heat transfer area provides the lower bounds for these model variables, to solve a cost optimization problem. The minimization of the total exergy loss rate by varying parametrically the available total heat transfer area between these bounds was also performed, allowing to see how the optimal distribution of the available total heat transfer area among the system components, as well as the operating conditions (stream temperature, pressure, composition, and mass flow rate) and heat loads, vary qualitatively and quantitatively with increasing available total heat transfer area. These optimization results allowed to find a “practical” value of the total heat transfer area, i.e. no benefits can be obtained by increasing the available total heat transfer area above this value since the minimal total exergy loss value cannot

  17. Integrated Multidisciplinary Optimization Objects, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — M4 Engineering proposes to implement physics-based, multidisciplinary analysis and optimization objects that will be integrated into a Python, open-source framework...

  18. Multi objective optimization of performance parameters of a single cylinder diesel engine running with hydrogen using a Taguchi-fuzzy based approach

    International Nuclear Information System (INIS)

    Bose, Probir Kumar; Deb, Madhujit; Banerjee, Rahul; Majumder, Arindam

    2013-01-01

    Environmental issues and rapid exhaustion of fossil fuels are the major concerns over the past two decades to search for alternative fuels. Among various alternatives hydrogen is a long-term renewable and least polluting fuel. Its clean burning capability helps to meet the stern emission norms. Full substitution of diesel with hydrogen may not be convenient for the time being but employing of hydrogen in a diesel engine in dual fuel mode is possible. In this experimental investigation a TMI (timed manifold injection) system has been developed using ECU (electronic control unit) with varying injection strategy to deliver hydrogen on to the intake manifold. Through adopting this technique in the existing diesel engine a momentous improvement in performance and combustion parameters has been observed. The study also attempts to explain the application of the fuzzy logic based Taguchi analysis to optimize the performance parameters i.e. BSEC (Brake specific energy consumption), Vol. Eff. (Volumetric efficiency) and BTHE (brake thermal efficiency) for the different hydrogen injection strategies. - Highlights: • A timed manifold injection system has been developed which enhances the BTHE by 31.74% at full load conditions. • Use of hydrogen-diesel dual fuel of BSEC was reduced by a maximum of 68.98% at full load condition compared to diesel. • Τhe Vol. Eff. reduced by 73.14% in dual fuel mode as compared to 77.23% at full load condition with base diesel. • A fuzzy based Taguchi's parameter design technique has been involved in multi objective optimization for prediction. • Predicted optimum combination improved BTHE and Vol. Eff. by 24.04% and 72.87% respectively and reduced BSEC by 59.03%

  19. Scandinavian Object Shift and Optimality Theory

    DEFF Research Database (Denmark)

    Engels, Eva; Vikner, Sten

    This study presents an account of object shift, a word order phenomenon found in most of the Scandinavian languages where an object occurs unexpectedly to the left and not to the right of a sentential adverbial. The book examines object shift across many of the Scandinavian languages and dialects...... and original observations, this book is an important addition to the fields of phonology, optimality theory and theoretical syntax......., and analyses the variation, for example whether object shift is optional or obligatory, whether it applies only to pronouns or other objects as well, and whether it applies to adverbials. The authors show that optimality theory, traditionally used in phonology, is a useful framework for accounting...

  20. Multi objective multi refinery optimization with environmental and catastrophic failure effects objectives

    Science.gov (United States)

    Khogeer, Ahmed Sirag

    2005-11-01

    Petroleum refining is a capital-intensive business. With stringent environmental regulations on the processing industry and declining refining margins, political instability, increased risk of war and terrorist attacks in which refineries and fuel transportation grids may be targeted, higher pressures are exerted on refiners to optimize performance and find the best combination of feed and processes to produce salable products that meet stricter product specifications, while at the same time meeting refinery supply commitments and of course making profit. This is done through multi objective optimization. For corporate refining companies and at the national level, Intea-Refinery and Inter-Refinery optimization is the second step in optimizing the operation of the whole refining chain as a single system. Most refinery-wide optimization methods do not cover multiple objectives such as minimizing environmental impact, avoiding catastrophic failures, or enhancing product spec upgrade effects. This work starts by carrying out a refinery-wide, single objective optimization, and then moves to multi objective-single refinery optimization. The last step is multi objective-multi refinery optimization, the objectives of which are analysis of the effects of economic, environmental, product spec, strategic, and catastrophic failure. Simulation runs were carried out using both MATLAB and ASPEN PIMS utilizing nonlinear techniques to solve the optimization problem. The results addressed the need to debottleneck some refineries or transportation media in order to meet the demand for essential products under partial or total failure scenarios. They also addressed how importing some high spec products can help recover some of the losses and what is needed in order to accomplish this. In addition, the results showed nonlinear relations among local and global objectives for some refineries. The results demonstrate that refineries can have a local multi objective optimum that does not

  1. Multi-objective optimization using genetic algorithms: A tutorial

    International Nuclear Information System (INIS)

    Konak, Abdullah; Coit, David W.; Smith, Alice E.

    2006-01-01

    Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity

  2. NLP modeling for the optimization of LiBr-H2O absorption refrigeration systems with exergy loss rate, heat transfer area, and cost as single objective functions

    DEFF Research Database (Denmark)

    Mussati, Sergio F.; Gernaey, Krist; Morosuk, Tatiana

    2016-01-01

    exergy loss rate, the total heat transfer area, and the total annual cost of the system. It was found that the optimal solution obtained by minimization of the total exergy loss rate provides “theoretical” upper bounds not only for the total heat transfer area of the system but also for each process unit...... and all stream temperatures, while the optimal solution obtained by minimization of the total heat transfer area provides the lower bounds for these model variables, to solve a cost optimization problem. The minimization of the total exergy loss rate by varying parametrically the available total heat...... transfer area between these bounds was also performed, allowing to see how the optimal distribution of the available total heat transfer area among the system components, as well as the operating conditions (stream temperature, pressure, composition, and mass flow rate) and heat loads, vary qualitatively...

  3. Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

    Science.gov (United States)

    Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

    2015-01-01

    The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

  4. Single versus Multiple Objective(s) Decision Making: An Application ...

    African Journals Online (AJOL)

    Rahel

    rather than exception in many real life decision-making circumstances. For example ...... stakeholders' relative importance of various attributes in the utility function. (Steuer 1986). ..... Multiple Criteria Optimization: Theory, Computation and.

  5. Multi-Objective Optimization of Managed Aquifer Recharge.

    Science.gov (United States)

    Fatkhutdinov, Aybulat; Stefan, Catalin

    2018-04-27

    This study demonstrates the utilization of a multi-objective hybrid global/local optimization algorithm for solving managed aquifer recharge (MAR) design problems, in which the decision variables included spatial arrangement of water injection and abstraction wells and time-variant rates of pumping and injection. The objective of the optimization was to maximize the efficiency of the MAR scheme, which includes both quantitative and qualitative aspects. The case study used to demonstrate the capabilities of the proposed approach is based on a published report on designing a real MAR site with defined aquifer properties, chemical groundwater characteristics as well as quality and volumes of injected water. The demonstration problems include steady-state and transient scenarios. The steady-state scenario demonstrates optimization of spatial arrangement of multiple injection and recovery wells, whereas the transient scenario was developed with the purpose of finding optimal regimes of water injection and recovery at a single location. Both problems were defined as multi-objective problems. The scenarios were simulated by applying coupled numerical groundwater flow and solute transport models: MODFLOW-2005 and MT3D-USGS. The applied optimization method was a combination of global - the Non-Dominated Sorting Genetic Algorithm (NSGA-2), and local - the Nelder-Mead Downhill Simplex search algorithms. The analysis of the resulting Pareto optimal solutions led to the discovery of valuable patterns and dependencies between the decision variables, model properties and problem objectives. Additionally, the performance of the traditional global and the hybrid optimization schemes were compared. This article is protected by copyright. All rights reserved.

  6. Many-objective thermodynamic optimization of Stirling heat engine

    International Nuclear Information System (INIS)

    Patel, Vivek; Savsani, Vimal; Mudgal, Anurag

    2017-01-01

    This paper presents a rigorous investigation of many-objective (four-objective) thermodynamic optimization of a Stirling heat engine. Many-objective optimization problem is formed by considering maximization of thermal efficiency, power output, ecological function and exergy efficiency. Multi-objective heat transfer search (MOHTS) algorithm is proposed and applied to obtain a set of Pareto-optimal points. Many objective optimization results form a solution in a four dimensional hyper objective space and for visualization it is represented on a two dimension objective space. Thus, results of four-objective optimization are represented by six Pareto fronts in two dimension objective space. These six Pareto fronts are compared with their corresponding two-objective Pareto fronts. Quantitative assessment of the obtained Pareto solutions is reported in terms of spread and the spacing measures. Different decision making approaches such as LINMAP, TOPSIS and fuzzy are used to select a final optimal solution from Pareto optimal set of many-objective optimization. Finally, to reveal the level of conflict between these objectives, distribution of each decision variable in their allowable range is also shown in two dimensional objective spaces. - Highlights: • Many-objective (i.e. four objective) optimization of Stirling engine is investigated. • MOHTS algorithm is introduced and applied to obtain a set of Pareto points. • Comparative results of many-objective and multi-objectives are presented. • Relationship of design variables in many-objective optimization are obtained. • Optimum solution is selected by using decision making approaches.

  7. Multi-objective optimization of a continuous thermally regenerative electrochemical cycle for waste heat recovery

    International Nuclear Information System (INIS)

    Long, Rui; Li, Baode; Liu, Zhichun; Liu, Wei

    2015-01-01

    An optimization analysis of a continuous TREC (thermally regenerative electrochemical cycle) was conducted with maximum power output and exergy efficiency as the objective functions simultaneously. For comparison, the power output, exergy efficiency, and thermal efficiency under the corresponding single-objective optimization schematics were also calculated. Under different optimization methods it was observed that the power output and the thermal efficiency increase with increasing inlet temperature of the heat source, whereas the exergy efficiency increases with increasing inlet temperature, reaches a maximum value, and then decreases. Results revealed that the optimal power output under the multi-objective optimization turned out to be slightly less than that obtained under the single-objective optimization for power output. However, the exergy and thermal efficiencies were much greater. Furthermore, the thermal exergy and exergy efficiency by single-objective optimization for energy efficiency shows no dominant advantage than that obtained under multi-objective optimization, comparing with the increase amplitude of the power output. This suggests that the multi-objective optimization could coordinate well both the power output and the exergy efficiency of the TREC system, and may serve as a more promising guide for operating and designing TREC systems. - Highlights: • An optimal analysis of a continuous TREC is conducted based on multi-objective optimization. • Performance under corresponding single-objective optimizations has also been calculated and compared. • Power under multi-objective optimization is slightly less than the maximum power. • Exergy and thermal efficiencies are much larger than that under the single-objective optimization.

  8. Optimal Path Determination for Flying Vehicle to Search an Object

    Science.gov (United States)

    Heru Tjahjana, R.; Heri Soelistyo U, R.; Ratnasari, L.; Irawanto, B.

    2018-01-01

    In this paper, a method to determine optimal path for flying vehicle to search an object is proposed. Background of the paper is controlling air vehicle to search an object. Optimal path determination is one of the most popular problem in optimization. This paper describe model of control design for a flying vehicle to search an object, and focus on the optimal path that used to search an object. In this paper, optimal control model is used to control flying vehicle to make the vehicle move in optimal path. If the vehicle move in optimal path, then the path to reach the searched object also optimal. The cost Functional is one of the most important things in optimal control design, in this paper the cost functional make the air vehicle can move as soon as possible to reach the object. The axis reference of flying vehicle uses N-E-D (North-East-Down) coordinate system. The result of this paper are the theorems which say that the cost functional make the control optimal and make the vehicle move in optimal path are proved analytically. The other result of this paper also shows the cost functional which used is convex. The convexity of the cost functional is use for guarantee the existence of optimal control. This paper also expose some simulations to show an optimal path for flying vehicle to search an object. The optimization method which used to find the optimal control and optimal path vehicle in this paper is Pontryagin Minimum Principle.

  9. Multi-objective group scheduling optimization integrated with preventive maintenance

    Science.gov (United States)

    Liao, Wenzhu; Zhang, Xiufang; Jiang, Min

    2017-11-01

    This article proposes a single-machine-based integration model to meet the requirements of production scheduling and preventive maintenance in group production. To describe the production for identical/similar and different jobs, this integrated model considers the learning and forgetting effects. Based on machine degradation, the deterioration effect is also considered. Moreover, perfect maintenance and minimal repair are adopted in this integrated model. The multi-objective of minimizing total completion time and maintenance cost is taken to meet the dual requirements of delivery date and cost. Finally, a genetic algorithm is developed to solve this optimization model, and the computation results demonstrate that this integrated model is effective and reliable.

  10. Multidisciplinary Optimization Object Library, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The development of a library of Common MDO Objects is proposed, in which the software objects will automate a variety of recurring problems in the development of MDO...

  11. Multidisciplinary Optimization Object Library, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — The development of a library of Common MDO Objects is proposed, in which the software objects will automate a variety of recurring problems in the development of MDO...

  12. Optimization for Guitar Fingering on Single Notes

    Science.gov (United States)

    Itoh, Masaru; Hayashida, Takumi

    This paper presents an optimization method for guitar fingering. The fingering is to determine a unique combination of string, fret and finger corresponding to the note. The method aims to generate the best fingering pattern for guitar robots rather than beginners. Furthermore, it can be applied to any musical score on single notes. A fingering action can be decomposed into three motions, that is, a motion of press string, release string and move fretting hand. The cost for moving the hand is estimated on the basis of Manhattan distance which is the sum of distances along fret and string directions. The objective is to minimize the total fingering costs, subject to fret, string and finger constraints. As a sequence of notes on the score forms a line on time series, the optimization for guitar fingering can be resolved into a multistage decision problem. Dynamic programming is exceedingly effective to solve such a problem. A level concept is introduced into rendering states so as to make multiple DP solutions lead a unique one among the DP backward processes. For example, if two fingerings have the same value of cost at different states on a stage, then the low position would be taken precedence over the high position, and the index finger would be over the middle finger.

  13. Dynamic multi-objective optimization using PSO

    CSIR Research Space (South Africa)

    Helbig, M

    2012-01-01

    Full Text Available by replacing objective functions with new ones at specific times. DMOOPs based on the static MOO two- objective ZDT functions [9] and the scalable DTLZ functions [10] was presented by Farina et al. [5]. Some adaptions to these test functions were proposed.... if space in archive 9. add new solutions to archive 10. else 11. remove solutions from archive 12. add new solutions to archive 13. select sentry particles The default configuration of DVEPSO algorithm that is used for this research is as follows...

  14. Collaborative Object Framework for Adaptive System Optimization, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation is to combine traditional and cutting edge optimization techniques into an existing powerful object based organic enterprise decision network...

  15. Conflicting Multi-Objective Compatible Optimization Control

    OpenAIRE

    Xu, Lihong; Hu, Qingsong; Hu, Haigen; Goodman, Erik

    2010-01-01

    Based on ideas developed in addressing practical greenhouse environmental control, we propose a new multi-objective compatible control method. Several detailed algorithms are proposed to meet the requirements of different kinds of problem: 1) A two-layer MOCC framework is presented for problems with a precise model; 2) To deal with situations

  16. Collaborative Object Framework for Adaptive System Optimization, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — TeamVision proposes that we research the feasibility of incorporating an adaptive object based optimization system into an existing multi-user object oriented...

  17. Wireless Sensor Network Optimization: Multi-Objective Paradigm.

    Science.gov (United States)

    Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

    2015-07-20

    Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

  18. Wireless Sensor Network Optimization: Multi-Objective Paradigm

    Science.gov (United States)

    Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

    2015-01-01

    Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

  19. Formulation of optimal international freight transport objective

    Directory of Open Access Journals (Sweden)

    A. Jarašūnienė

    2002-10-01

    Full Text Available To increase the attraction of Lithuania as a transit country striving to promote carriers' border crossing activities and facilitate customs clearance procedures as well as freight delivery to clients it is necessary to identify the main obstacles, to analyse them and to select adequate measures and means for their elimination. Therefore, on the basis of the formulation of transport freight management objective, as well as basing on the assessment of indeterminacy of external impacts, it would be possible to deduce the main causes of idle time of transport means in customs, to estimate the dependence of service time in proportion to transport flow.

  20. Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system

    International Nuclear Information System (INIS)

    Sayyaadi, Hoseyn

    2009-01-01

    Multi-objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the exergetic, economic and environmental aspects have been considered, simultaneously. The thermodynamic modeling has been implemented comprehensively while economic analysis conducted in accordance with the total revenue requirement (TRR) method. The results for the single objective thermoeconomic optimization have been compared with the previous studies in optimization of CGAM problem. In multi-objective optimization of the CGAM problem, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms. This objective has been integrated with the thermoeconomic objective to form a new unique objective function known as a thermoenvironomic objective function. The thermoenvironomic objective has been minimized while the exergetic objective has been maximized. One of the most suitable optimization techniques developed using a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs) has been considered here. This approach which is developed based on the genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of decision-making has been presented and a final optimal solution has been introduced. The sensitivity of the solutions to the interest rate and the fuel cost has been studied

  1. Multi-objective optimization of a vertical ground source heat pump using evolutionary algorithm

    International Nuclear Information System (INIS)

    Sayyaadi, Hoseyn; Amlashi, Emad Hadaddi; Amidpour, Majid

    2009-01-01

    Thermodynamic and thermoeconomic optimization of a vertical ground source heat pump system has been studied. A model based on the energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. The proposed vertical ground source heat pump system including eight decision variables is considered for optimization. An artificial intelligence technique known as evolutionary algorithm (EA) has been utilized as an optimization method. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective, thermoeconomic single objective and multi-objective optimizations are performed. In Multi-objective optimization, both thermodynamic and thermoeconomic objectives are considered, simultaneously. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from available optimal points on Pareto frontier is presented. The results obtained using the various optimization approaches are compared and discussed. Further, the sensitivity of optimized systems to the interest rate, to the annual number of operating hours and to the electricity cost are studied in detail.

  2. Informed multi-objective decision-making in environmental management using Pareto optimality

    Science.gov (United States)

    Maureen C. Kennedy; E. David Ford; Peter Singleton; Mark Finney; James K. Agee

    2008-01-01

    Effective decisionmaking in environmental management requires the consideration of multiple objectives that may conflict. Common optimization methods use weights on the multiple objectives to aggregate them into a single value, neglecting valuable insight into the relationships among the objectives in the management problem.

  3. Extreme Trust Region Policy Optimization for Active Object Recognition.

    Science.gov (United States)

    Liu, Huaping; Wu, Yupei; Sun, Fuchun; Huaping Liu; Yupei Wu; Fuchun Sun; Sun, Fuchun; Liu, Huaping; Wu, Yupei

    2018-06-01

    In this brief, we develop a deep reinforcement learning method to actively recognize objects by choosing a sequence of actions for an active camera that helps to discriminate between the objects. The method is realized using trust region policy optimization, in which the policy is realized by an extreme learning machine and, therefore, leads to efficient optimization algorithm. The experimental results on the publicly available data set show the advantages of the developed extreme trust region optimization method.

  4. Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

    Science.gov (United States)

    Zhang, Jian; Gan, Yang

    2018-04-01

    The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

  5. Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

    OpenAIRE

    Dewancker, Ian; McCourt, Michael; Ainsworth, Samuel

    2016-01-01

    Real-world engineering systems are typically compared and contrasted using multiple metrics. For practical machine learning systems, performance tuning is often more nuanced than minimizing a single expected loss objective, and it may be more realistically discussed as a multi-objective optimization problem. We propose a novel generative model for scalar-valued utility functions to capture human preferences in a multi-objective optimization setting. We also outline an interactive active learn...

  6. Multi-objective three stage design optimization for island microgrids

    International Nuclear Information System (INIS)

    Sachs, Julia; Sawodny, Oliver

    2016-01-01

    Highlights: • An enhanced multi-objective three stage design optimization for microgrids is given. • Use of an optimal control problem for the calculation of the optimal operation. • The inclusion of a detailed battery model with CC/CV charging control. • The determination of a representative profile with optimized number of days. • The proposed method finds its direct application in a design tool for microgids. - Abstract: Hybrid off-grid energy systems enable a cost efficient and reliable energy supply to rural areas around the world. The main potential for a low cost operation and uninterrupted power supply lies in the optimal sizing and operation of such microgrids. In particular, sudden variations in load demand or in the power supply from renewables underline the need for an optimally sized system. This paper presents an efficient multi-objective model based optimization approach for the optimal sizing of all components and the determination of the best power electronic layout. The presented method is divided into three optimization problems to minimize economic and environmental objectives. This design optimization includes detailed components models and an optimized energy dispatch strategy which enables the optimal design of the energy system with respect to an adequate control for the specific configuration. To significantly reduce the computation time without loss of accuracy, the presented method contains the determination of a representative load profile using a k-means clustering method. The k-means algorithm itself is embedded in an optimization problem for the calculation of the optimal number of clusters. The benefits in term of reduced computation time, inclusion of optimal energy dispatch and optimization of power electronic architecture, of the presented optimization method are illustrated using a case study.

  7. A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization.

    Science.gov (United States)

    Yang, Shaofu; Liu, Qingshan; Wang, Jun

    2018-04-01

    This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.

  8. Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

    Directory of Open Access Journals (Sweden)

    Jinjiao Hou

    2018-05-01

    Full Text Available This paper proposes a multi-objective optimization method for the start-up strategy of pumped storage units (PSU for the first time. In the multi-objective optimization method, the speed rise time and the overshoot during the process of the start-up are taken as the objectives. A precise simulation platform is built for simulating the transient process of start-up, and for calculating the objectives based on the process. The Multi-objective Particle Swarm Optimization algorithm (MOPSO is adopted to optimize the widely applied start-up strategies based on one-stage direct guide vane control (DGVC, and two-stage DGVC. Based on the Pareto Front obtained, a multi-objective decision-making method based on the relative objective proximity is used to sort the solutions in the Pareto Front. Start-up strategy optimization for a PSU of a pumped storage power station in Jiangxi Province in China is conducted in experiments. The results show that: (1 compared with the single objective optimization, the proposed multi-objective optimization of start-up strategy not only greatly shortens the speed rise time and the speed overshoot, but also makes the speed curve quickly stabilize; (2 multi-objective optimization of strategy based on two-stage DGVC achieves better solution for a quick and smooth start-up of PSU than that of the strategy based on one-stage DGVC.

  9. Multi-objective optimization of a plate and frame heat exchanger via genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Najafi, Hamidreza; Najafi, Behzad [K. N. Toosi University of Technology, Department of Mechanical Engineering, Tehran (Iran)

    2010-06-15

    In the present paper, a plate and frame heat exchanger is considered. Multi-objective optimization using genetic algorithm is developed in order to obtain a set of geometric design parameters, which lead to minimum pressure drop and the maximum overall heat transfer coefficient. Vividly, considered objective functions are conflicting and no single solution can satisfy both objectives simultaneously. Multi-objective optimization procedure yields a set of optimal solutions, called Pareto front, each of which is a trade-off between objectives and can be selected by the user, regarding the application and the project's limits. The presented work takes care of numerous geometric parameters in the presence of logical constraints. A sensitivity analysis is also carried out to study the effects of different geometric parameters on the considered objective functions. Modeling the system and implementing the multi-objective optimization via genetic algorithm has been performed by MATLAB. (orig.)

  10. Constrained multi-objective optimization of storage ring lattices

    Science.gov (United States)

    Husain, Riyasat; Ghodke, A. D.

    2018-03-01

    The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

  11. Enhanced Multi-Objective Optimization of Groundwater Monitoring Networks

    DEFF Research Database (Denmark)

    Bode, Felix; Binning, Philip John; Nowak, Wolfgang

    Drinking-water well catchments include many sources for potential contaminations like gas stations or agriculture. Finding optimal positions of monitoring wells for such purposes is challenging because there are various parameters (and their uncertainties) that influence the reliability...... and optimality of any suggested monitoring location or monitoring network. The goal of this project is to develop and establish a concept to assess, design, and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: (1) a high...... be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, wrapped up within the framework of formal multi-objective optimization. In order to gain insight into the flow and transport physics...

  12. Multi-objective optimization of inverse planning for accurate radiotherapy

    International Nuclear Information System (INIS)

    Cao Ruifen; Pei Xi; Cheng Mengyun; Li Gui; Hu Liqin; Wu Yican; Jing Jia; Li Guoli

    2011-01-01

    The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dose-volume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set. (authors)

  13. Investigating multi-objective fluence and beam orientation IMRT optimization

    Science.gov (United States)

    Potrebko, Peter S.; Fiege, Jason; Biagioli, Matthew; Poleszczuk, Jan

    2017-07-01

    Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a ‘bird’s-eye-view’ perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird’s-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters

  14. Enhanced Multi-Objective Energy Optimization by a Signaling Method

    OpenAIRE

    Soares, João; Borges, Nuno; Vale, Zita; Oliveira, P.B.

    2016-01-01

    In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensi...

  15. COOMA: AN OBJECT-ORIENTED STOCHASTIC OPTIMIZATION ALGORITHM

    Directory of Open Access Journals (Sweden)

    Stanislav Alexandrovich Tavridovich

    2017-09-01

    Full Text Available Stochastic optimization methods such as genetic algorithm, particle swarm optimization algorithm, and others are successfully used to solve optimization problems. They are all based on similar ideas and need minimal adaptation when being implemented. But several factors complicate the application of stochastic search methods in practice: multimodality of the objective function, optimization with constraints, finding the best parameter configuration of the algorithm, the increasing of the searching space, etc. This paper proposes a new Cascade Object Optimization and Modification Algorithm (COOMA which develops the best ideas of known stochastic optimization methods and can be applied to a wide variety of real-world problems described in the terms of object-oriented models with practically any types of parameters, variables, and associations between objects. The objects of different classes are organized in pools and pools form the hierarchical structure according to the associations between classes. The algorithm is also executed according to the pool structure: the methods of the upper-level pools before changing their objects call the analogous methods of all their subpools. The algorithm starts with initialization step and then passes through a number of iterations during which the objects are modified until the stop criteria are satisfied. The objects are modified using movement, replication and mutation operations. Two-level version of COOMA realizes a built-in self-adaptive mechanism. The optimization statistics for a number of test problems shows that COOMA is able to solve multi-level problems (with objects of different associated classes, problems with multimodal fitness functions and systems of constraints. COOMA source code on Java is available on request.

  16. A Multiple-objective Optimization of Whey Fermentation in Stirred Tank Bioreactors

    Directory of Open Access Journals (Sweden)

    Mitko Petrov

    2006-12-01

    Full Text Available A multiple-objective optimization is applied to find an optimal policy of a fed-batch fermentation process for lactose oxidation from a natural substratum of the strain Kluyveromyces marxianus var. lactis MC5. The optimal policy is consisted of feed flow rate, agitation speed, and gas flow rate. The multiple-objective problem includes: the total price of the biomass production, the second objective functions are the separation cost in downstream processing and the third objective function corresponds to the oxygen mass-transfer in the bioreactor. The multiple-objective optimization are transforming to standard problem for optimization with single-objective function. Local criteria are defined utility function with different weight for single-type vector task. A fuzzy sets method is applied to be solved the maximizing decision problem. A simple combined algorithm guideline to find a satisfactory solution to the general multiple-objective optimization problem. The obtained optimal control results have shown an increase of the process productiveness and a decrease of the residual substrate concentration.

  17. Multi-objective optimal power flow with FACTS devices

    International Nuclear Information System (INIS)

    Basu, M.

    2011-01-01

    This paper presents multi-objective differential evolution to optimize cost of generation, emission and active power transmission loss of flexible ac transmission systems (FACTS) device-equipped power systems. In the proposed approach, optimal power flow problem is formulated as a multi-objective optimization problem. FACTS devices considered include thyristor controlled series capacitor (TCSC) and thyristor controlled phase shifter (TCPS). The proposed approach has been examined and tested on the modified IEEE 30-bus and 57-bus test systems. The results obtained from the proposed approach have been compared with those obtained from nondominated sorting genetic algorithm-II, strength pareto evolutionary algorithm 2 and pareto differential evolution.

  18. MULTI-OBJECTIVE ONLINE OPTIMIZATION OF BEAM LIFETIME AT APS

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Yipeng

    2017-06-25

    In this paper, online optimization of beam lifetime at the APS (Advanced Photon Source) storage ring is presented. A general genetic algorithm (GA) is developed and employed for some online optimizations in the APS storage ring. Sextupole magnets in 40 sectors of the APS storage ring are employed as variables for the online nonlinear beam dynamics optimization. The algorithm employs several optimization objectives and is designed to run with topup mode or beam current decay mode. Up to 50\\% improvement of beam lifetime is demonstrated, without affecting the transverse beam sizes and other relevant parameters. In some cases, the top-up injection efficiency is also improved.

  19. Multi-objective optimization under uncertainty for sheet metal forming

    Directory of Open Access Journals (Sweden)

    Lafon Pascal

    2016-01-01

    Full Text Available Aleatory uncertainties in material properties, blank thickness and friction condition are inherent and irreducible variabilities in sheet metal forming. Optimal design configurations, which are obtained by conventional design optimization methods, are not always able to meet the desired targets due to the effect of uncertainties. This paper proposes a multi-objective robust design optimization that aims to tackle this problem. Results obtained on a U shape draw bending benchmark show that spring-back effect can be controlled by optimizing process parameters.

  20. Multi-objective optimization in computer networks using metaheuristics

    CERN Document Server

    Donoso, Yezid

    2007-01-01

    Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...

  1. Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

    International Nuclear Information System (INIS)

    Salazar, Daniel; Rocco, Claudio M.; Galvan, Blas J.

    2006-01-01

    This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature

  2. Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Salazar, Daniel [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain) and Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: danielsalazaraponte@gmail.com; Rocco, Claudio M. [Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: crocco@reacciun.ve; Galvan, Blas J. [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain)]. E-mail: bgalvan@step.es

    2006-09-15

    This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature.

  3. Multi-Objective Optimization in Physical Synthesis of Integrated Circuits

    CERN Document Server

    A Papa, David

    2013-01-01

    This book introduces techniques that advance the capabilities and strength of modern software tools for physical synthesis, with the ultimate goal to improve the quality of leading-edge semiconductor products.  It provides a comprehensive introduction to physical synthesis and takes the reader methodically from first principles through state-of-the-art optimizations used in cutting edge industrial tools. It explains how to integrate chip optimizations in novel ways to create powerful circuit transformations that help satisfy performance requirements. Broadens the scope of physical synthesis optimization to include accurate transformations operating between the global and local scales; Integrates groups of related transformations to break circular dependencies and increase the number of circuit elements that can be jointly optimized to escape local minima;  Derives several multi-objective optimizations from first observations through complete algorithms and experiments; Describes integrated optimization te...

  4. Calculation of Pareto-optimal solutions to multiple-objective problems using threshold-of-acceptability constraints

    Science.gov (United States)

    Giesy, D. P.

    1978-01-01

    A technique is presented for the calculation of Pareto-optimal solutions to a multiple-objective constrained optimization problem by solving a series of single-objective problems. Threshold-of-acceptability constraints are placed on the objective functions at each stage to both limit the area of search and to mathematically guarantee convergence to a Pareto optimum.

  5. Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

    Directory of Open Access Journals (Sweden)

    Yongpeng Shen

    2016-02-01

    Full Text Available Auxiliary power units (APUs are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC emissions, carbon monoxide (CO emissions and nitric oxide (NO x emissions. Then, the multi-objective particle swarm optimization (MOPSO algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC, Federal test procedure (FTP and high way fuel economy test (HWFET driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

  6. Multi-objective genetic optimization of linear construction projects

    Directory of Open Access Journals (Sweden)

    Fatma A. Agrama

    2012-08-01

    Full Text Available In the real world, the majority cases of optimization problems, met by engineers, are composed of several conflicting objectives. This paper presents an approach for a multi-objective optimization model for scheduling linear construction projects. Linear construction projects have many identical units wherein activities repeat from one unit to another. Highway, pipeline, and tunnels are good examples that exhibit repetitive characteristics. These projects represent a large portion of the construction industry. The present model enables construction planners to generate optimal/near-optimal construction plans that minimize project duration, total work interruptions, and total number of crews. Each of these plans identifies, from a set of feasible alternatives, optimal crew synchronization for each activity and activity interruptions at each unit. This model satisfies the following aspects: (1 it is based on the line of balance technique; (2 it considers non-serial typical activities networks with finish–start relationship and both lag or overlap time between activities is allowed; (3 it utilizes a multi-objective genetic algorithms approach; (4 it is developed as a spreadsheet template that is easy to use. Details of the model with visual charts are presented. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the scheduling of linear construction projects.

  7. Well Field Management Using Multi-Objective Optimization

    DEFF Research Database (Denmark)

    Hansen, Annette Kirstine; Hendricks Franssen, H. J.; Bauer-Gottwein, Peter

    2013-01-01

    with infiltration basins, injection wells and abstraction wells. The two management objectives are to minimize the amount of water needed for infiltration and to minimize the risk of getting contaminated water into the drinking water wells. The management is subject to a daily demand fulfilment constraint. Two...... different optimization methods are tested. Constant scheduling where decision variables are held constant during the time of optimization, and sequential scheduling where the optimization is performed stepwise for daily time steps. The latter is developed to work in a real-time situation. Case study...

  8. Multi-objective optimization design method of radiation shielding

    International Nuclear Information System (INIS)

    Yang Shouhai; Wang Weijin; Lu Daogang; Chen Yixue

    2012-01-01

    Due to the shielding design goals of diversification and uncertain process of many factors, it is necessary to develop an optimization design method of intelligent shielding by which the shielding scheme selection will be achieved automatically and the uncertainties of human impact will be reduced. For economical feasibility to achieve a radiation shielding design for automation, the multi-objective genetic algorithm optimization of screening code which combines the genetic algorithm and discrete-ordinate method was developed to minimize the costs, size, weight, and so on. This work has some practical significance for gaining the optimization design of shielding. (authors)

  9. Many-Objective Distinct Candidates Optimization using Differential Evolution

    DEFF Research Database (Denmark)

    Justesen, Peter; Ursem, Rasmus Kjær

    2010-01-01

    for each objective. The Many-Objective Distinct Candidates Optimization using Differential Evolution (MODCODE) algorithm takes a novel approach by focusing search using a user-defined number of subpopulations each returning a distinct optimal solution within the preferred region of interest. In this paper......, we present the novel MODCODE algorithm incorporating the ROD measure to measure and control candidate distinctiveness. MODCODE is tested against GDE3 on three real world centrifugal pump design problems supplied by Grundfos. Our algorithm outperforms GDE3 on all problems with respect to all...

  10. Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

    Science.gov (United States)

    Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

    2017-07-01

    Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

    International Nuclear Information System (INIS)

    Pang, X.; Rybarcyk, L.J.

    2014-01-01

    Particle swarm optimization (PSO) and genetic algorithm (GA) are both nature-inspired population based optimization methods. Compared to GA, whose long history can trace back to 1975, PSO is a relatively new heuristic search method first proposed in 1995. Due to its fast convergence rate in single objective optimization domain, the PSO method has been extended to optimize multi-objective problems. In this paper, we will introduce the PSO method and its multi-objective extension, the MOPSO, apply it along with the MOGA (mainly the NSGA-II) to simulations of the LANSCE linac and operational set point optimizations. Our tests show that both methods can provide very similar Pareto fronts but the MOPSO converges faster

  12. Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

    Energy Technology Data Exchange (ETDEWEB)

    Pang, X., E-mail: xpang@lanl.gov; Rybarcyk, L.J.

    2014-03-21

    Particle swarm optimization (PSO) and genetic algorithm (GA) are both nature-inspired population based optimization methods. Compared to GA, whose long history can trace back to 1975, PSO is a relatively new heuristic search method first proposed in 1995. Due to its fast convergence rate in single objective optimization domain, the PSO method has been extended to optimize multi-objective problems. In this paper, we will introduce the PSO method and its multi-objective extension, the MOPSO, apply it along with the MOGA (mainly the NSGA-II) to simulations of the LANSCE linac and operational set point optimizations. Our tests show that both methods can provide very similar Pareto fronts but the MOPSO converges faster.

  13. Use of multiple objective evolutionary algorithms in optimizing surveillance requirements

    International Nuclear Information System (INIS)

    Martorell, S.; Carlos, S.; Villanueva, J.F.; Sanchez, A.I; Galvan, B.; Salazar, D.; Cepin, M.

    2006-01-01

    This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability

  14. Optimization of externalities using DTM measures: a Pareto optimal multi objective optimization using the evolutionary algorithm SPEA2+

    NARCIS (Netherlands)

    Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, Michiel; Allkim, T.P.; van Arem, Bart

    2010-01-01

    Multi objective optimization of externalities of traffic is performed solving a network design problem in which Dynamic Traffic Management measures are used. The resulting Pareto optimal set is determined by employing the SPEA2+ evolutionary algorithm.

  15. Optimization of tactical decisions: subjective and objective conditionality

    Directory of Open Access Journals (Sweden)

    Олег Юрійович Булулуков

    2016-06-01

    Full Text Available In the article «human» and «objective» factors are investigated that influencing on optimization of tactical decisions. Attention is accented on dependence of the got information about the circumstances of crime from the acceptance of correct decisions an investigator. Connection between efficiency of investigation and acceptance of optimal tactical decisions is underlined. The declared problem is not investigational in literature in a sufficient measure. Its separate aspects found the reflection in works: D. А. Solodova, S. Yu. Yakushina and others. Some questions related to optimization of investigation and making decision an investigator we discover in works: R. S. Belkin, V. А. Juravel, V. Е. Konovalova, V. L. Sinchuk, B. V. Shur, V. Yu. Shepitko. The aim of the article is determination of term «optimization», as it applies to tactical decisions in criminalistics, and also consideration of influence of human and objective factors on the acceptance of optimal decisions at investigation of crimes. In the article etymology of term is considered «optimization» and interpretation of its is given as it applies to the acceptance of tactical decisions. The types of mark human and objective factors, stipulating optimization of tactical decisions. The last assists efficiency of tactics of investigation of crimes. At consideration of «human factors» of influencing on optimization decisions, attention applies on «psychological traps» can take place at making decision. Among them such are named, as: anchoring; status quo; irreversible expenses; desired and actual; incorrect formulation; conceit; reinsurance; constancy of memory. Underlined, absence of unambiguity in the brought list over of «objective factors» influencing at choice tactical decision. The different understanding of «tactical risk» is argued, as a factor influencing on an acceptance tactical decisions. The analysis of «human» and «objective» factors influencing on

  16. Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

    Science.gov (United States)

    Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

    2002-06-01

    This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

  17. IMRT optimization with pseudo-biologic objective function

    International Nuclear Information System (INIS)

    Yi, B. Y.; Ahn, S. D.; Kim, J. H.; Lee, S. W.; Choi, E. K.

    2002-01-01

    The pseudo-biologic objective function has been proposed for the IMRT optimization. It is similar to the biological objective function in mathematical shape, but uses physical parameters. The pseudo-biologic objective function concept is consisted of the target coverage index (TCI) and the organ score index (OSI), was introduced. The TCI was expressed as the sum of all of the weighted bins of target dose volume histogram (DVH). The weights were given as the normal distribution of which the average is 100 % and the standard deviation is ±. The OSI was expressed as similar way. The average of the normal distribution was 0% of the dose and that of standard deviation was selected as a function of limiting dose and its importance. The objective function could be calculated as the product of the TCI and OSI's. The RTP Tool Box (RTB) was used for this study. The constraints applied in the optimization was intuitively clinical experience based numbers, while the physical objective function asks just numbers which are not necessarily based on the clinic, and the parameters for the biologic objective functions are uncertain. The OSI's from the pseudo-biological function showed better results than from the physical functions, while TCI's showed similar tendency. We could show that the pseudo-biologic function can be used for an IMRT objective function on behalf of the biological objective function

  18. Visualizing Data as Objects by DC (Difference of Convex) Optimization

    DEFF Research Database (Denmark)

    Carrizosa, Emilio; Guerrero, Vanesa; Morales, Dolores Romero

    In this paper we address the problem of visualizing in a bounded region a set of individuals, which has attached a dissimilarity measure and a statistical value. This problem, which extends the standard Multidimensional Scaling Analysis, is written as a global optimization problem whose objective...

  19. Ensemble based multi-objective production optimization of smart wells

    NARCIS (Netherlands)

    Fonseca, R.M.; Leeuwenburgh, O.; Jansen, J.D.

    2012-01-01

    In a recent study two hierarchical multi-objective methods were suggested to include short-term targets in life-cycle production optimization. However this previous study has two limitations: 1) the adjoint formulation is used to obtain gradient information, requiring simulator source code access

  20. Multi-Objective Optimization of Grillages Applying the Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Darius Mačiūnas

    2012-01-01

    Full Text Available The article analyzes the optimization of grillage-type foundations seeking for the least possible reactive forces in the poles for a given number of poles and for the least possible bending moments of absolute values in the connecting beams of the grillage. Therefore, we suggest using a compromise objective function (to be minimized that consists of the maximum reactive force arising in all poles and the maximum bending moment of the absolute value in connecting beams; both components include the given weights. The variables of task design are pole positions under connecting beams. The optimization task is solved applying the algorithm containing all the initial data of the problem. Reactive forces and bending moments are calculated using an original program (finite element method is applied. This program is integrated into the optimization algorithm using the “black-box” principle. The “black-box” finite element program sends back the corresponding value of the objective function. Numerical experiments revealed the optimal quantity of points to compute bending moments. The obtained results show a certain ratio of weights in the objective function where the contribution of reactive forces and bending moments to the objective function are equivalent. This solution can serve as a pilot project for more detailed design.Article in Lithuanian

  1. Multi-objective optimization approach for air traffic flow management

    Directory of Open Access Journals (Sweden)

    Fadil Rabie

    2017-01-01

    The decision-making stage was then performed with the aid of data clustering techniques to reduce the sizeof the Pareto-optimal set and obtain a smaller representation of the multi-objective design space, there by making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.

  2. Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

    Science.gov (United States)

    2009-03-10

    xfar by xint. Else, generate a new individual, using the Sobol pseudo- random sequence generator within the upper and lower bounds of the variables...12. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons. 2002. 13. Sobol , I. M., "Uniformly Distributed Sequences

  3. Recent advances in evolutionary multi-objective optimization

    CERN Document Server

    Datta, Rituparna; Gupta, Abhishek

    2017-01-01

    This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include:< optimization in dynamic environments, multi-objective bilevel programming, handling high ...

  4. Effective multi-objective optimization of Stirling engine systems

    International Nuclear Information System (INIS)

    Punnathanam, Varun; Kotecha, Prakash

    2016-01-01

    Highlights: • Multi-objective optimization of three recent Stirling engine models. • Use of efficient crossover and mutation operators for real coded Genetic Algorithm. • Demonstrated supremacy of the strategy over the conventionally used algorithm. • Improvements of up to 29% in comparison to literature results. - Abstract: In this article we demonstrate the supremacy of the Non-dominated Sorting Genetic Algorithm-II with Simulated Binary Crossover and Polynomial Mutation operators for the multi-objective optimization of Stirling engine systems by providing three examples, viz., (i) finite time thermodynamic model, (ii) Stirling engine thermal model with associated irreversibility and (iii) polytropic finite speed based thermodynamics. The finite time thermodynamic model involves seven decision variables and consists of three objectives: output power, thermal efficiency and rate of entropy generation. In comparison to literature, it was observed that the used strategy provides a better Pareto front and leads to improvements of up to 29%. The performance is also evaluated on a Stirling engine thermal model which considers the associated irreversibility of the cycle and consists of three objectives involving eleven decision variables. The supremacy of the suggested strategy is also demonstrated on the experimentally validated polytropic finite speed thermodynamics based Stirling engine model for optimization involving two objectives and ten decision variables.

  5. Investigation on multi-objective performance optimization algorithm application of fan based on response surface method and entropy method

    Science.gov (United States)

    Zhang, Li; Wu, Kexin; Liu, Yang

    2017-12-01

    A multi-objective performance optimization method is proposed, and the problem that single structural parameters of small fan balance the optimization between the static characteristics and the aerodynamic noise is solved. In this method, three structural parameters are selected as the optimization variables. Besides, the static pressure efficiency and the aerodynamic noise of the fan are regarded as the multi-objective performance. Furthermore, the response surface method and the entropy method are used to establish the optimization function between the optimization variables and the multi-objective performances. Finally, the optimized model is found when the optimization function reaches its maximum value. Experimental data shows that the optimized model not only enhances the static characteristics of the fan but also obviously reduces the noise. The results of the study will provide some reference for the optimization of multi-objective performance of other types of rotating machinery.

  6. Grey Relational Analyses for Multi-Objective Optimization of Turning S45C Carbon Steel

    International Nuclear Information System (INIS)

    Shah, A.H.A.; Azmi, A.I.; Khalil, A.N.M.

    2016-01-01

    The optimization of performance characteristics in turning process can be achieved through selection of proper machining parameters. It is well known that many researchers have successfully reported the optimization of single performance characteristic. Nevertheless, the multi-objective optimization can be difficult and challenging to be studied due to its complexity in analysis. This is because an improvement of one performance characteristic may lead to degradation of other performance characteristic. As a result, the study of multi-objective optimization in CNC turning of S45C carbon steel has been attempted in this paper through Taguchi and Grey Relational Analysis (GRA) method. Through this methodology, the multiple performance characteristics, namely; surface roughness, material removal rate (MRR), tool wear, and power consumption; can be optimized simultaneously. It appears from the experimental results that the multiple performance characteristics in CNC turning was achieved and improved through the methodology employed. (paper)

  7. Multi-objective optimization of GENIE Earth system models.

    Science.gov (United States)

    Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

    2009-07-13

    The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

  8. Visualizing Data as Objects by DC (Difference of Convex) Optimization

    DEFF Research Database (Denmark)

    Carrizosa, Emilio; Guerrero, Vanesa; Morales, Dolores Romero

    2018-01-01

    In this paper we address the problem of visualizing in a bounded region a set of individuals, which has attached a dissimilarity measure and a statistical value, as convex objects. This problem, which extends the standard Multidimensional Scaling Analysis, is written as a global optimization...... problem whose objective is the difference of two convex functions (DC). Suitable DC decompositions allow us to use the Difference of Convex Algorithm (DCA) in a very efficient way. Our algorithmic approach is used to visualize two real-world datasets....

  9. Multi-objective optimization in quantum parameter estimation

    Science.gov (United States)

    Gong, BeiLi; Cui, Wei

    2018-04-01

    We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

  10. Multi-objective optimization of the reactor coolant system

    International Nuclear Information System (INIS)

    Chen Lei; Yan Changqi; Wang Jianjun

    2014-01-01

    Background: Weight and size are important criteria in evaluating the performance of a nuclear power plant. It is of great theoretical value and engineering significance to reduce the weight and volume of the components for a nuclear power plant by the optimization methodology. Purpose: In order to provide a new method for the optimization of nuclear power plant multi-objective, the concept of the non-dominated solution was introduced. Methods: Based on the parameters of Qinshan I nuclear power plant, the mathematical models of the reactor core, the reactor vessel, the main pipe, the pressurizer and the steam generator were built and verified. The sensitivity analyses were carried out to study the influences of the design variables on the objectives. A modified non-dominated sorting genetic algorithm was proposed and employed to optimize the weight and the volume of the reactor coolant system. Results: The results show that the component mathematical models are reliable, the modified non-dominated sorting generic algorithm is effective, and the reactor inlet temperature is the most important variable which influences the distribution of the non-dominated solutions. Conclusion: The optimization results could provide a reference to the design of such reactor coolant system. (authors)

  11. A scalable coevolutionary multi-objective particle swarm optimizer

    Directory of Open Access Journals (Sweden)

    Xiangwei Zheng

    2010-11-01

    Full Text Available Multi-Objective Particle Swarm Optimizers (MOPSOs are easily trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A scalable cooperative coevolution and ?-dominance based MOPSO (CEPSO is proposed to address these issues. In CEPSO, Multi-objective Optimization Problems (MOPs are decomposed in terms of their decision variables and are optimized by cooperative coevolutionary subswarms, and a uniform distribution mutation operator is adopted to avoid premature convergence. All subswarms share an external archive based on ?-dominance, which is also used as a leader set. Collaborators are selected from the archive and used to construct context vectors in order to evaluate particles in a subswarm. CEPSO is tested on several classical MOP benchmark functions and experimental results show that CEPSO can readily escape from local optima and optimize both low and high dimensional problems, but the number of function evaluations only increases linearly with respect to the number of decision variables. Therefore, CEPSO is competitive in solving various MOPs.

  12. Multi-objective optimal dispatch of distributed energy resources

    Science.gov (United States)

    Longe, Ayomide

    This thesis is composed of two papers which investigate the optimal dispatch for distributed energy resources. In the first paper, an economic dispatch problem for a community microgrid is studied. In this microgrid, each agent pursues an economic dispatch for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, a simple market structure is introduced as a framework for energy trades in a small community microgrid such as the Solar Village. It was found that both sellers and buyers benefited by participating in this market. In the second paper, Semidefinite Programming (SDP) for convex relaxation of power flow equations is used for optimal active and reactive dispatch for Distributed Energy Resources (DER). Various objective functions including voltage regulation, reduced transmission line power losses, and minimized reactive power charges for a microgrid are introduced. Combinations of these goals are attained by solving a multiobjective optimization for the proposed ORPD problem. Also, both centralized and distributed versions of this optimal dispatch are investigated. It was found that SDP made the optimal dispatch faster and distributed solution allowed for scalability.

  13. Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm

    International Nuclear Information System (INIS)

    Duan, Chen; Wang, Xinggang; Shu, Shuiming; Jing, Changwei; Chang, Huawei

    2014-01-01

    Highlights: • An improved thermodynamic model taking into account irreversibility parameter was developed. • A multi-objective optimization method for designing Stirling engine was investigated. • Multi-objective particle swarm optimization algorithm was adopted in the area of Stirling engine for the first time. - Abstract: In the recent years, the interest in Stirling engine has remarkably increased due to its ability to use any heat source from outside including solar energy, fossil fuels and biomass. A large number of studies have been done on Stirling cycle analysis. In the present study, a mathematical model based on thermodynamic analysis of Stirling engine considering regenerative losses and internal irreversibilities has been developed. Power output, thermal efficiency and the cycle irreversibility parameter of Stirling engine are optimized simultaneously using Particle Swarm Optimization (PSO) algorithm, which is more effective than traditional genetic algorithms. In this optimization problem, some important parameters of Stirling engine are considered as decision variables, such as temperatures of the working fluid both in the high temperature isothermal process and in the low temperature isothermal process, dead volume ratios of each heat exchanger, volumes of each working spaces, effectiveness of the regenerator, and the system charge pressure. The Pareto optimal frontier is obtained and the final design solution has been selected by Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP). Results show that the proposed multi-objective optimization approach can significantly outperform traditional single objective approaches

  14. Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

    Science.gov (United States)

    Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

    2015-12-01

    For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

  15. Multi-objective optimization of an underwater compressed air energy storage system using genetic algorithm

    International Nuclear Information System (INIS)

    Cheung, Brian C.; Carriveau, Rupp; Ting, David S.K.

    2014-01-01

    This paper presents the findings from a multi-objective genetic algorithm optimization study on the design parameters of an underwater compressed air energy storage system (UWCAES). A 4 MWh UWCAES system was numerically simulated and its energy, exergy, and exergoeconomics were analysed. Optimal system configurations were determined that maximized the UWCAES system round-trip efficiency and operating profit, and minimized the cost rate of exergy destruction and capital expenditures. The optimal solutions obtained from the multi-objective optimization model formed a Pareto-optimal front, and a single preferred solution was selected using the pseudo-weight vector multi-criteria decision making approach. A sensitivity analysis was performed on interest rates to gauge its impact on preferred system designs. Results showed similar preferred system designs for all interest rates in the studied range. The round-trip efficiency and operating profit of the preferred system designs were approximately 68.5% and $53.5/cycle, respectively. The cost rate of the system increased with interest rates. - Highlights: • UWCAES system configurations were developed using multi-objective optimization. • System was optimized for energy efficiency, exergy, and exergoeconomics • Pareto-optimal solution surfaces were developed at different interest rates. • Similar preferred system configurations were found at all interest rates studied

  16. An effective docking strategy for virtual screening based on multi-objective optimization algorithm

    Directory of Open Access Journals (Sweden)

    Kang Ling

    2009-02-01

    Full Text Available Abstract Background Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. Results In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected. Conclusion The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.

  17. Reinforcement active learning in the vibrissae system: optimal object localization.

    Science.gov (United States)

    Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

    2013-01-01

    Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. Uncertain and multi-objective programming models for crop planting structure optimization

    Directory of Open Access Journals (Sweden)

    Mo LI,Ping GUO,Liudong ZHANG,Chenglong ZHANG

    2016-03-01

    Full Text Available Crop planting structure optimization is a significant way to increase agricultural economic benefits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic profits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study, three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming (IFCCP model and an inexact fuzzy linear programming (IFLP model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimization-theory-based fuzzy linear multi-objective programming model was developed, which is capable of reflecting both uncertainties and multi-objective. In addition, a multi-objective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic benefits and the denominator representing minimum crop planting area allocation. These models better reflect actual situations, considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in Minqin County, north-west China. The advantages, the applicable conditions and the solution methods

  19. Reliability of a single objective measure in assessing sleepiness.

    Science.gov (United States)

    Sunwoo, Bernie Y; Jackson, Nicholas; Maislin, Greg; Gurubhagavatula, Indira; George, Charles F; Pack, Allan I

    2012-01-01

    To evaluate reliability of single objective tests in assessing sleepiness. Subjects who completed polysomnography underwent a 4-nap multiple sleep latency test (MSLT) the following day. Prior to each nap opportunity on MSLT, subjects performed the psychomotor vigilance test (PVT) and divided attention driving task (DADT). Results of single versus multiple test administrations were compared using the intraclass correlation coefficient (ICC) and adjusted for test administration order effects to explore time of day effects. Measures were explored as continuous and binary (i.e., impaired or not impaired). Community-based sample evaluated at a tertiary, university-based sleep center. 372 adult commercial vehicle operators oversampled for increased obstructive sleep apnea risk. N/A. AS CONTINUOUS MEASURES, ICC WERE AS FOLLOWS: MSLT 0.45, PVT median response time 0.69, PVT number of lapses 0.51, 10-min DADT tracking error 0.87, 20-min DADT tracking error 0.90. Based on binary outcomes, ICC were: MSLT 0.63, PVT number of lapses 0.85, 10-min DADT 0.95, 20-min DADT 0.96. Statistically significant time of day effects were seen in both the MSLT and PVT but not the DADT. Correlation between ESS and different objective tests was strongest for MSLT, range [-0.270 to -0.195] and persisted across all time points. Single DADT and PVT administrations are reliable measures of sleepiness. A single MSLT administration can reasonably discriminate individuals with MSL < 8 minutes. These results support the use of a single administration of some objective tests of sleepiness when performed under controlled conditions in routine clinical care.

  20. PARETO OPTIMAL SOLUTIONS FOR MULTI-OBJECTIVE GENERALIZED ASSIGNMENT PROBLEM

    Directory of Open Access Journals (Sweden)

    S. Prakash

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: The Multi-Objective Generalized Assignment Problem (MGAP with two objectives, where one objective is linear and the other one is non-linear, has been considered, with the constraints that a job is assigned to only one worker – though he may be assigned more than one job, depending upon the time available to him. An algorithm is proposed to find the set of Pareto optimal solutions of the problem, determining assignments of jobs to workers with two objectives without setting priorities for them. The two objectives are to minimise the total cost of the assignment and to reduce the time taken to complete all the jobs.

    AFRIKAANSE OPSOMMING: ‘n Multi-doelwit veralgemeende toekenningsprobleem (“multi-objective generalised assignment problem – MGAP” met twee doelwitte, waar die een lineêr en die ander nielineêr is nie, word bestudeer, met die randvoorwaarde dat ‘n taak slegs toegedeel word aan een werker – alhoewel meer as een taak aan hom toegedeel kan word sou die tyd beskikbaar wees. ‘n Algoritme word voorgestel om die stel Pareto-optimale oplossings te vind wat die taaktoedelings aan werkers onderhewig aan die twee doelwitte doen sonder dat prioriteite toegeken word. Die twee doelwitte is om die totale koste van die opdrag te minimiseer en om die tyd te verminder om al die take te voltooi.

  1. Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Ranjan [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: ranjan.k@ks3.ecs.kyoto-u.ac.jp; Izui, Kazuhiro [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: izui@prec.kyoto-u.ac.jp; Yoshimura, Masataka [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: yoshimura@prec.kyoto-u.ac.jp; Nishiwaki, Shinji [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: shinji@prec.kyoto-u.ac.jp

    2009-04-15

    Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.

  2. Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

    International Nuclear Information System (INIS)

    Kumar, Ranjan; Izui, Kazuhiro; Yoshimura, Masataka; Nishiwaki, Shinji

    2009-01-01

    Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets

  3. Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

    Directory of Open Access Journals (Sweden)

    Yan Sun

    2015-09-01

    Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

  4. Linear and ultrafast nonlinear plasmonics of single nano-objects

    Science.gov (United States)

    Crut, Aurélien; Maioli, Paolo; Vallée, Fabrice; Del Fatti, Natalia

    2017-03-01

    Single-particle optical investigations have greatly improved our understanding of the fundamental properties of nano-objects, avoiding the spurious inhomogeneous effects that affect ensemble experiments. Correlation with high-resolution imaging techniques providing morphological information (e.g. electron microscopy) allows a quantitative interpretation of the optical measurements by means of analytical models and numerical simulations. In this topical review, we first briefly recall the principles underlying some of the most commonly used single-particle optical techniques: near-field, dark-field, spatial modulation and photothermal microscopies/spectroscopies. We then focus on the quantitative investigation of the surface plasmon resonance (SPR) of metallic nano-objects using linear and ultrafast optical techniques. While measured SPR positions and spectral areas are found in good agreement with predictions based on Maxwell’s equations, SPR widths are strongly influenced by quantum confinement (or, from a classical standpoint, surface-induced electron scattering) and, for small nano-objects, cannot be reproduced using the dielectric functions of bulk materials. Linear measurements on single nano-objects (silver nanospheres and gold nanorods) allow a quantification of the size and geometry dependences of these effects in confined metals. Addressing the ultrafast response of an individual nano-object is also a powerful tool to elucidate the physical mechanisms at the origin of their optical nonlinearities, and their electronic, vibrational and thermal relaxation processes. Experimental investigations of the dynamical response of gold nanorods are shown to be quantitatively modeled in terms of modifications of the metal dielectric function enhanced by plasmonic effects. Ultrafast spectroscopy can also be exploited to unveil hidden physical properties of more complex nanosystems. In this context, two-color femtosecond pump-probe experiments performed on individual

  5. Determination of Pareto frontier in multi-objective maintenance optimization

    International Nuclear Information System (INIS)

    Certa, Antonella; Galante, Giacomo; Lupo, Toni; Passannanti, Gianfranco

    2011-01-01

    The objective of a maintenance policy generally is the global maintenance cost minimization that involves not only the direct costs for both the maintenance actions and the spare parts, but also those ones due to the system stop for preventive maintenance and the downtime for failure. For some operating systems, the failure event can be dangerous so that they are asked to operate assuring a very high reliability level between two consecutive fixed stops. The present paper attempts to individuate the set of elements on which performing maintenance actions so that the system can assure the required reliability level until the next fixed stop for maintenance, minimizing both the global maintenance cost and the total maintenance time. In order to solve the previous constrained multi-objective optimization problem, an effective approach is proposed to obtain the best solutions (that is the Pareto optimal frontier) among which the decision maker will choose the more suitable one. As well known, describing the whole Pareto optimal frontier generally is a troublesome task. The paper proposes an algorithm able to rapidly overcome this problem and its effectiveness is shown by an application to a case study regarding a complex series-parallel system.

  6. Multi-Objective Design Of Optimal Greenhouse Gas Observation Networks

    Science.gov (United States)

    Lucas, D. D.; Bergmann, D. J.; Cameron-Smith, P. J.; Gard, E.; Guilderson, T. P.; Rotman, D.; Stolaroff, J. K.

    2010-12-01

    One of the primary scientific functions of a Greenhouse Gas Information System (GHGIS) is to infer GHG source emission rates and their uncertainties by combining measurements from an observational network with atmospheric transport modeling. Certain features of the observational networks that serve as inputs to a GHGIS --for example, sampling location and frequency-- can greatly impact the accuracy of the retrieved GHG emissions. Observation System Simulation Experiments (OSSEs) provide a framework to characterize emission uncertainties associated with a given network configuration. By minimizing these uncertainties, OSSEs can be used to determine optimal sampling strategies. Designing a real-world GHGIS observing network, however, will involve multiple, conflicting objectives; there will be trade-offs between sampling density, coverage and measurement costs. To address these issues, we have added multi-objective optimization capabilities to OSSEs. We demonstrate these capabilities by quantifying the trade-offs between retrieval error and measurement costs for a prototype GHGIS, and deriving GHG observing networks that are Pareto optimal. [LLNL-ABS-452333: This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

  7. Multi-Objective Optimization of the Hedging Model for reservoir Operation Using Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    sadegh sadeghitabas

    2015-12-01

    Full Text Available Multi-objective problems rarely ever provide a single optimal solution, rather they yield an optimal set of outputs (Pareto fronts. Solving these problems was previously accomplished by using some simplifier methods such as the weighting coefficient method used for converting a multi-objective problem to a single objective function. However, such robust tools as multi-objective meta-heuristic algorithms have been recently developed for solving these problems. The hedging model is one of the classic problems for reservoir operation that is generally employed for mitigating drought impacts in water resources management. According to this method, although it is possible to supply the total planned demands, only portions of the demands are met to save water by allowing small deficits in the current conditions in order to avoid or reduce severe deficits in future. The approach heavily depends on economic and social considerations. In the present study, the meta-heuristic algorithms of NSGA-II, MOPSO, SPEA-II, and AMALGAM are used toward the multi-objective optimization of the hedging model. For this purpose, the rationing factors involved in Taleghan dam operation are optimized over a 35-year statistical period of inflow. There are two objective functions: a minimizing the modified shortage index, and b maximizing the reliability index (i.e., two opposite objectives. The results show that the above algorithms are applicable to a wide range of optimal solutions. Among the algorithms, AMALGAM is found to produce a better Pareto front for the values of the objective function, indicating its more satisfactory performance.

  8. Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

    Science.gov (United States)

    Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

    2010-01-01

    This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

  9. Optimization of Support Vector Machine (SVM) for Object Classification

    Science.gov (United States)

    Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

    2012-01-01

    The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

  10. Multi-objective optimization of a joule cycle for re-liquefaction of the Liquefied Natural Gas

    International Nuclear Information System (INIS)

    Sayyaadi, Hoseyn; Babaelahi, M.

    2011-01-01

    Highlights: → A typical LNG boil off gas re-liquefaction plant system is optimized. → Objective functions based on thermodynamic and thermoeconomic analysis are obtained. → The cost of the system product and the exergetic efficiency are optimized, simultaneously. → A decision-making process for selection of the final optimal design is introduced. → Results obtained using various optimization scenarios are compared and discussed. - Abstract: A LNG re-liquefaction plant is optimized with a multi-objective approach which simultaneously considers exergetic and exergoeconomic objectives. In this regard, optimization is performed in order to maximize the exergetic efficiency of plant and minimize the unit cost of the system product (refrigeration effect), simultaneously. Thermodynamic modeling is performed based on energy and exergy analyses, while an exergoeconomic model based on the total revenue requirement (TRR) are developed. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms namely NSGA-II. This approach which is based on the Genetic Algorithm is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained and a final optimal solution is selected in a decision-making process. An example of decision-making process for selection of the final solution from the available optimal points of the Pareto frontier is presented here. The feature of selected final optimal system is compared with corresponding features of the base case and exergoeconomic single-objective optimized systems and discussed.

  11. Efficient exact optimization of multi-objective redundancy allocation problems in series-parallel systems

    International Nuclear Information System (INIS)

    Cao, Dingzhou; Murat, Alper; Chinnam, Ratna Babu

    2013-01-01

    This paper proposes a decomposition-based approach to exactly solve the multi-objective Redundancy Allocation Problem for series-parallel systems. Redundancy allocation problem is a form of reliability optimization and has been the subject of many prior studies. The majority of these earlier studies treat redundancy allocation problem as a single objective problem maximizing the system reliability or minimizing the cost given certain constraints. The few studies that treated redundancy allocation problem as a multi-objective optimization problem relied on meta-heuristic solution approaches. However, meta-heuristic approaches have significant limitations: they do not guarantee that Pareto points are optimal and, more importantly, they may not identify all the Pareto-optimal points. In this paper, we treat redundancy allocation problem as a multi-objective problem, as is typical in practice. We decompose the original problem into several multi-objective sub-problems, efficiently and exactly solve sub-problems, and then systematically combine the solutions. The decomposition-based approach can efficiently generate all the Pareto-optimal solutions for redundancy allocation problems. Experimental results demonstrate the effectiveness and efficiency of the proposed method over meta-heuristic methods on a numerical example taken from the literature.

  12. Improved multi-objective clustering algorithm using particle swarm optimization.

    Directory of Open Access Journals (Sweden)

    Congcong Gong

    Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

  13. Improved multi-objective clustering algorithm using particle swarm optimization.

    Science.gov (United States)

    Gong, Congcong; Chen, Haisong; He, Weixiong; Zhang, Zhanliang

    2017-01-01

    Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

  14. Development of a multi-objective PBIL evolutionary algorithm applied to a nuclear reactor core reload optimization problem

    International Nuclear Information System (INIS)

    Machado, Marcelo D.; Dchirru, Roberto

    2005-01-01

    The nuclear reactor core reload optimization problem consists in finding a pattern of partially burned-up and fresh fuels that optimizes the plant's next operation cycle. This optimization problem has been traditionally solved using an expert's knowledge, but recently artificial intelligence techniques have also been applied successfully. The artificial intelligence optimization techniques generally have a single objective. However, most real-world engineering problems, including nuclear core reload optimization, have more than one objective (multi-objective) and these objectives are usually conflicting. The aim of this work is to develop a tool to solve multi-objective problems based on the Population-Based Incremental Learning (PBIL) algorithm. The new tool is applied to solve the Angra 1 PWR core reload optimization problem with the purpose of creating a Pareto surface, so that a pattern selected from this surface can be applied for the plant's next operation cycle. (author)

  15. Multi-objective evacuation routing optimization for toxic cloud releases

    International Nuclear Information System (INIS)

    Gai, Wen-mei; Deng, Yun-feng; Jiang, Zhong-an; Li, Jing; Du, Yan

    2017-01-01

    This paper develops a model for assessing the risks associated with the evacuation process in response to potential chemical accidents, based on which a multi-objective evacuation routing model for toxic cloud releases is proposed taking into account that the travel speed on each arc will be affected by disaster extension. The objectives of the evacuation routing model are to minimize travel time and individual evacuation risk along a path respectively. Two heuristic algorithms are proposed to solve the multi-objective evacuation routing model. Simulation results show the effectiveness and feasibility of the model and algorithms presented in this paper. And, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency route selection in other cases (fires, nuclear accidents). - Highlights: • A model for assessing and visualizing the risks is developed. • A multi-objective evacuation routing model is proposed for toxic cloud releases. • A modified Dijkstra algorithm is designed to obtain an solution of the model. • Two heuristic algorithms have been developed as the optimization tool.

  16. Pareto-Optimal Multi-objective Inversion of Geophysical Data

    Science.gov (United States)

    Schnaidt, Sebastian; Conway, Dennis; Krieger, Lars; Heinson, Graham

    2018-01-01

    In the process of modelling geophysical properties, jointly inverting different data sets can greatly improve model results, provided that the data sets are compatible, i.e., sensitive to similar features. Such a joint inversion requires a relationship between the different data sets, which can either be analytic or structural. Classically, the joint problem is expressed as a scalar objective function that combines the misfit functions of multiple data sets and a joint term which accounts for the assumed connection between the data sets. This approach suffers from two major disadvantages: first, it can be difficult to assess the compatibility of the data sets and second, the aggregation of misfit terms introduces a weighting of the data sets. We present a pareto-optimal multi-objective joint inversion approach based on an existing genetic algorithm. The algorithm treats each data set as a separate objective, avoiding forced weighting and generating curves of the trade-off between the different objectives. These curves are analysed by their shape and evolution to evaluate data set compatibility. Furthermore, the statistical analysis of the generated solution population provides valuable estimates of model uncertainty.

  17. Multi-objective evolutionary emergency response optimization for major accidents

    International Nuclear Information System (INIS)

    Georgiadou, Paraskevi S.; Papazoglou, Ioannis A.; Kiranoudis, Chris T.; Markatos, Nikolaos C.

    2010-01-01

    Emergency response planning in case of a major accident (hazardous material event, nuclear accident) is very important for the protection of the public and workers' safety and health. In this context, several protective actions can be performed, such as, evacuation of an area; protection of the population in buildings; and use of personal protective equipment. The best solution is not unique when multiple criteria are taken into consideration (e.g. health consequences, social disruption, economic cost). This paper presents a methodology for multi-objective optimization of emergency response planning in case of a major accident. The emergency policy with regards to protective actions to be implemented is optimized. An evolutionary algorithm has been used as the optimization tool. Case studies demonstrating the methodology and its application in emergency response decision-making in case of accidents related to hazardous materials installations are presented. However, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency response procedures in other cases (nuclear accidents, transportation of hazardous materials) or for land-use planning issues.

  18. Multi-objective optimal operation of smart reconfigurable distribution grids

    Directory of Open Access Journals (Sweden)

    Abdollah Kavousi-Fard

    2016-02-01

    Full Text Available Reconfiguration is a valuable technique that can support the distribution grid from different aspects such as operation cost and loss reduction, reliability improvement, and voltage stability enhancement. An intelligent and efficient optimization framework, however, is required to reach the desired efficiency through the reconfiguration strategy. This paper proposes a new multi-objective optimization model to make use of the reconfiguration strategy for minimizing the power losses, improving the voltage profile, and enhancing the load balance in distribution grids. The proposed model employs the min-max fuzzy approach to find the most satisfying solution from a set of nondominated solutions in the problem space. Due to the high complexity and the discrete nature of the proposed model, a new optimization method based on harmony search (HS algorithm is further proposed. Moreover, a new modification method is suggested to increase the harmony memory diversity in the improvisation stage and increase the convergence ability of the algorithm. The feasibility and satisfying performance of the proposed model are examined on the IEEE 32-bus distribution system.

  19. Vanpool trip planning based on evolutionary multiple objective optimization

    Science.gov (United States)

    Zhao, Ming; Yang, Disheng; Feng, Shibing; Liu, Hengchang

    2017-08-01

    Carpool and vanpool draw a lot of researchers’ attention, which is the emphasis of this paper. A concrete vanpool operation definition is given, based on the given definition, this paper tackles vanpool operation optimization using user experience decline index(UEDI). This paper is focused on making each user having identical UEDI and the system having minimum sum of all users’ UEDI. Three contributions are made, the first contribution is a vanpool operation scheme diagram, each component of the scheme is explained in detail. The second contribution is getting all customer’s UEDI as a set, standard deviation and sum of all users’ UEDI set are used as objectives in multiple objective optimization to decide trip start address, trip start time and trip destination address. The third contribution is a trip planning algorithm, which tries to minimize the sum of all users’ UEDI. Geographical distribution of the charging stations and utilization rate of the charging stations are considered in the trip planning process.

  20. From tomography to FWI with a single objective function

    KAUST Repository

    Alkhalifah, Tariq Ali

    2013-06-10

    Reflections in our seismic data induce serious nonlinear behavior in the objective function of full waveform inversion (FWI). Thus, without a good initial velocity model, that can produce the reflections within a cycle of the frequency used in the inversion, convergence to the solution becomes hard. Such velocity models are usually extracted from migration velocity analysis or traveltime tomography, among other means, that are not guaranteed to adhere to the FWI requirements. As such, we promote an objective function based on the misfit in the instantaneous traveltime between the observed and modeled data. This phase based attribute of the wavefield, along with its phase unwrapping features, provide a frequency dependent traveltime function. With strong damping of the of the synthetic, potentially low frequency, data, this attribute admits first arrival traveltime that could be compared with picked ones from the observed data, like in wave equation tomography. As we relax the damping on the synthetic and observed data, the objective function measures the misfit in the phase, however unwrapped in an FWI type inversion. It, thus, provides a single objective function and a natural transition from traveltime tomography to full waveform inversion. A Marmousi example demonstrates the effectiveness of the approach.

  1. Fast, multiple optimizations of quadratic dose objective functions in IMRT

    International Nuclear Information System (INIS)

    Breedveld, Sebastiaan; Storchi, Pascal R M; Keijzer, Marleen; Heijmen, Ben J M

    2006-01-01

    Inverse treatment planning for intensity-modulated radiotherapy may include time consuming, multiple minimizations of an objective function. In this paper, methods are presented to speed up the process of (repeated) minimization of the well-known quadratic dose objective function, extended with a smoothing term that ensures generation of clinically acceptable beam profiles. In between two subsequent optimizations, the voxel-dependent importance factors of the quadratic terms will generally be adjusted, based on an intermediate plan evaluation. The objective function has been written in matrix-vector format, facilitating the use of a recently published, fast quadratic minimization algorithm, instead of commonly applied gradient-based methods. This format also reduces the calculation time in between subsequent minimizations, related to adjustment of the voxel-dependent importance factors. Sparse matrices are used to limit the required amount of computer memory. For three patients, comparisons have been made with a gradient method. Mean speed improvements of up to a factor of 37 have been achieved

  2. Evolution strategies and multi-objective optimization of permanent magnet motor

    DEFF Research Database (Denmark)

    Andersen, Søren Bøgh; Santos, Ilmar

    2012-01-01

    When designing a permanent magnet motor, several geometry and material parameters are to be defined. This is not an easy task, as material properties and magnetic fields are highly non-linear and the design of a motor is therefore often an iterative process. From an engineering point of view, we...... of evolution strategies, ES to effectively design and optimize parameters of permanent magnet motors. Single as well as multi-objective optimization procedures are carried out. A modified way of creating the strategy parameters for the ES algorithm is also proposed and has together with the standard ES...

  3. Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs

    Directory of Open Access Journals (Sweden)

    Jiajun Liu

    2017-10-01

    Full Text Available Energy storage systems (ESS play an important role in the performance of mining vehicles. A hybrid ESS combining both batteries (BTs and supercapacitors (SCs is one of the most promising solutions. As a case study, this paper discusses the optimal hybrid ESS sizing and energy management strategy (EMS of 14-ton underground load-haul-dump vehicles (LHDs. Three novel contributions are added to the relevant literature. First, a multi-objective optimization is formulated regarding energy consumption and the total cost of a hybrid ESS, which are the key factors of LHDs, and a battery capacity degradation model is used. During the process, dynamic programming (DP-based EMS is employed to obtain the optimal energy consumption and hybrid ESS power profiles. Second, a 10-year life cycle cost model of a hybrid ESS for LHDs is established to calculate the total cost, including capital cost, operating cost, and replacement cost. According to the optimization results, three solutions chosen from the Pareto front are compared comprehensively, and the optimal one is selected. Finally, the optimal and battery-only options are compared quantitatively using the same objectives, and the hybrid ESS is found to be a more economical and efficient option.

  4. Removing defocused objects from single focal plane scans of cytological slides

    Directory of Open Access Journals (Sweden)

    David Friedrich

    2016-01-01

    Full Text Available Background: Virtual microscopy and automated processing of cytological slides are more challenging compared to histological slides. Since cytological slides exhibit a three-dimensional surface and the required microscope objectives with high resolution have a low depth of field, these cannot capture all objects of a single field of view in focus. One solution would be to scan multiple focal planes; however, the increase in processing time and storage requirements are often prohibitive for clinical routine. Materials and Methods: In this paper, we show that it is a reasonable trade-off to scan a single focal plane and automatically reject defocused objects from the analysis. To this end, we have developed machine learning solutions for the automated identification of defocused objects. Our approach includes creating novel features, systematically optimizing their parameters, selecting adequate classifier algorithms, and identifying the correct decision boundary between focused and defocused objects. We validated our approach for computer-assisted DNA image cytometry. Results and Conclusions: We reach an overall sensitivity of 96.08% and a specificity of 99.63% for identifying defocused objects. Applied on ninety cytological slides, the developed classifiers automatically removed 2.50% of the objects acquired during scanning, which otherwise would have interfered the examination. Even if not all objects are acquired in focus, computer-assisted DNA image cytometry still identified more diagnostically or prognostically relevant objects compared to manual DNA image cytometry. At the same time, the workload for the expert is reduced dramatically.

  5. Optimization design of energy deposition on single expansion ramp nozzle

    Science.gov (United States)

    Ju, Shengjun; Yan, Chao; Wang, Xiaoyong; Qin, Yupei; Ye, Zhifei

    2017-11-01

    Optimization design has been widely used in the aerodynamic design process of scramjets. The single expansion ramp nozzle is an important component for scramjets to produces most of thrust force. A new concept of increasing the aerodynamics of the scramjet nozzle with energy deposition is presented. The essence of the method is to create a heated region in the inner flow field of the scramjet nozzle. In the current study, the two-dimensional coupled implicit compressible Reynolds Averaged Navier-Stokes and Menter's shear stress transport turbulence model have been applied to numerically simulate the flow fields of the single expansion ramp nozzle with and without energy deposition. The numerical results show that the proposal of energy deposition can be an effective method to increase force characteristics of the scramjet nozzle, the thrust coefficient CT increase by 6.94% and lift coefficient CN decrease by 26.89%. Further, the non-dominated sorting genetic algorithm coupled with the Radial Basis Function neural network surrogate model has been employed to determine optimum location and density of the energy deposition. The thrust coefficient CT and lift coefficient CN are selected as objective functions, and the sampling points are obtained numerically by using a Latin hypercube design method. The optimized thrust coefficient CT further increase by 1.94%, meanwhile, the optimized lift coefficient CN further decrease by 15.02% respectively. At the same time, the optimized performances are in good and reasonable agreement with the numerical predictions. The findings suggest that scramjet nozzle design and performance can benefit from the application of energy deposition.

  6. PRIVACY PRESERVING DATA MINING USING MULTIPLE OBJECTIVE OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    V. Shyamala Susan

    2016-10-01

    Full Text Available Privacy preservation is that the most targeted issue in information publication, because the sensitive data shouldn't be leaked. For this sake, several privacy preservation data mining algorithms are proposed. In this work, feature selection using evolutionary algorithm and data masking coupled with slicing is treated as a multiple objective optimisation to preserve privacy. To start with, Genetic Algorithm (GA is carried out over the datasets to perceive the sensitive attributes and prioritise the attributes for treatment as per their determined sensitive level. In the next phase, to distort the data, noise is added to the higher level sensitive value using Hybrid Data Transformation (HDT method. In the following phase slicing algorithm groups the correlated attributes organized and by this means reduces the dimensionality by retaining the Advanced Clustering Algorithm (ACA. With the aim of getting the optimal dimensions of buckets, tuple segregating is accomplished by Metaheuristic Firefly Algorithm (MFA. The investigational consequences imply that the anticipated technique can reserve confidentiality and therefore the information utility is additionally high. Slicing algorithm allows the protection of association and usefulness in which effects in decreasing the information dimensionality and information loss. Performance analysis is created over OCC 7 and OCC 15 and our optimization method proves its effectiveness over two totally different datasets by showing 92.98% and 96.92% respectively.

  7. Shape optimization of high power centrifugal compressor using multi-objective optimal method

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Hyun Soo; Lee, Jeong Min; Kim, Youn Jea [School of Mechanical Engineering, Sungkyunkwan University, Seoul (Korea, Republic of)

    2015-03-15

    In this study, a method for optimal design of impeller and diffuser blades in the centrifugal compressor using response surface method (RSM) and multi-objective genetic algorithm (MOGA) was evaluated. A numerical simulation was conducted using ANSYS CFX with various values of impeller and diffuser parameters, which consist of leading edge (LE) angle, trailing edge (TE) angle, and blade thickness. Each of the parameters was divided into three levels. A total of 45 design points were planned using central composite design (CCD), which is one of the design of experiment (DOE) techniques. Response surfaces that were generated on the basis of the results of DOE were used to determine the optimal shape of impeller and diffuser blade. The entire process of optimization was conducted using ANSYS Design Xplorer (DX). Through the optimization, isentropic efficiency and pressure recovery coefficient, which are the main performance parameters of the centrifugal compressor, were increased by 0.3 and 5, respectively.

  8. Shape optimization of high power centrifugal compressor using multi-objective optimal method

    International Nuclear Information System (INIS)

    Kang, Hyun Soo; Lee, Jeong Min; Kim, Youn Jea

    2015-01-01

    In this study, a method for optimal design of impeller and diffuser blades in the centrifugal compressor using response surface method (RSM) and multi-objective genetic algorithm (MOGA) was evaluated. A numerical simulation was conducted using ANSYS CFX with various values of impeller and diffuser parameters, which consist of leading edge (LE) angle, trailing edge (TE) angle, and blade thickness. Each of the parameters was divided into three levels. A total of 45 design points were planned using central composite design (CCD), which is one of the design of experiment (DOE) techniques. Response surfaces that were generated on the basis of the results of DOE were used to determine the optimal shape of impeller and diffuser blade. The entire process of optimization was conducted using ANSYS Design Xplorer (DX). Through the optimization, isentropic efficiency and pressure recovery coefficient, which are the main performance parameters of the centrifugal compressor, were increased by 0.3 and 5, respectively

  9. Diversity comparison of Pareto front approximations in many-objective optimization.

    Science.gov (United States)

    Li, Miqing; Yang, Shengxiang; Liu, Xiaohui

    2014-12-01

    Diversity assessment of Pareto front approximations is an important issue in the stochastic multiobjective optimization community. Most of the diversity indicators in the literature were designed to work for any number of objectives of Pareto front approximations in principle, but in practice many of these indicators are infeasible or not workable when the number of objectives is large. In this paper, we propose a diversity comparison indicator (DCI) to assess the diversity of Pareto front approximations in many-objective optimization. DCI evaluates relative quality of different Pareto front approximations rather than provides an absolute measure of distribution for a single approximation. In DCI, all the concerned approximations are put into a grid environment so that there are some hyperboxes containing one or more solutions. The proposed indicator only considers the contribution of different approximations to nonempty hyperboxes. Therefore, the computational cost does not increase exponentially with the number of objectives. In fact, the implementation of DCI is of quadratic time complexity, which is fully independent of the number of divisions used in grid. Systematic experiments are conducted using three groups of artificial Pareto front approximations and seven groups of real Pareto front approximations with different numbers of objectives to verify the effectiveness of DCI. Moreover, a comparison with two diversity indicators used widely in many-objective optimization is made analytically and empirically. Finally, a parametric investigation reveals interesting insights of the division number in grid and also offers some suggested settings to the users with different preferences.

  10. Loading pattern optimization by multi-objective simulated annealing with screening technique

    International Nuclear Information System (INIS)

    Tong, K. P.; Hyun, C. L.; Hyung, K. J.; Chang, H. K.

    2006-01-01

    This paper presents a new multi-objective function which is made up of the main objective term as well as penalty terms related to the constraints. All the terms are represented in the same functional form and the coefficient of each term is normalized so that each term has equal weighting in the subsequent simulated annealing optimization calculations. The screening technique introduced in the previous work is also adopted in order to save computer time in 3-D neutronics evaluation of trial loading patterns. For numerical test of the new multi-objective function in the loading pattern optimization, the optimum loading patterns for the initial and the cycle 7 reload PWR core of Yonggwang Unit 4 are calculated by the simulated annealing algorithm with screening technique. A total of 10 optimum loading patterns are obtained for the initial core through 10 independent simulated annealing optimization runs. For the cycle 7 reload core one optimum loading pattern has been obtained from a single simulated annealing optimization run. More SA optimization runs will be conducted to optimum loading patterns for the cycle 7 reload core and results will be presented in the further work. (authors)

  11. Many-Objective Particle Swarm Optimization Using Two-Stage Strategy and Parallel Cell Coordinate System.

    Science.gov (United States)

    Hu, Wang; Yen, Gary G; Luo, Guangchun

    2017-06-01

    It is a daunting challenge to balance the convergence and diversity of an approximate Pareto front in a many-objective optimization evolutionary algorithm. A novel algorithm, named many-objective particle swarm optimization with the two-stage strategy and parallel cell coordinate system (PCCS), is proposed in this paper to improve the comprehensive performance in terms of the convergence and diversity. In the proposed two-stage strategy, the convergence and diversity are separately emphasized at different stages by a single-objective optimizer and a many-objective optimizer, respectively. A PCCS is exploited to manage the diversity, such as maintaining a diverse archive, identifying the dominance resistant solutions, and selecting the diversified solutions. In addition, a leader group is used for selecting the global best solutions to balance the exploitation and exploration of a population. The experimental results illustrate that the proposed algorithm outperforms six chosen state-of-the-art designs in terms of the inverted generational distance and hypervolume over the DTLZ test suite.

  12. A linear bi-level multi-objective program for optimal allocation of water resources.

    Directory of Open Access Journals (Sweden)

    Ijaz Ahmad

    Full Text Available This paper presents a simple bi-level multi-objective linear program (BLMOLP with a hierarchical structure consisting of reservoir managers and several water use sectors under a multi-objective framework for the optimal allocation of limited water resources. Being the upper level decision makers (i.e., leader in the hierarchy, the reservoir managers control the water allocation system and tend to create a balance among the competing water users thereby maximizing the total benefits to the society. On the other hand, the competing water use sectors, being the lower level decision makers (i.e., followers in the hierarchy, aim only to maximize individual sectoral benefits. This multi-objective bi-level optimization problem can be solved using the simultaneous compromise constraint (SICCON technique which creates a compromise between upper and lower level decision makers (DMs, and transforms the multi-objective function into a single decision-making problem. The bi-level model developed in this study has been applied to the Swat River basin in Pakistan for the optimal allocation of water resources among competing water demand sectors and different scenarios have been developed. The application of the model in this study shows that the SICCON is a simple, applicable and feasible approach to solve the BLMOLP problem. Finally, the comparisons of the model results show that the optimization model is practical and efficient when it is applied to different conditions with priorities assigned to various water users.

  13. Multi-objective optimization of coal-fired power plants using differential evolution

    International Nuclear Information System (INIS)

    Wang, Ligang; Yang, Yongping; Dong, Changqing; Morosuk, Tatiana; Tsatsaronis, George

    2014-01-01

    Highlights: • Multi-objective optimization of large-scale coal-fired power plants using differential evolution. • A newly-proposed algorithm for searching the fronts of decision space in a single run. • A reduction of cost of electricity by 2–4% with an optimal efficiency increase up to 2% points. • The uncertainty comes mainly from temperature- and reheat-related cost factors of steam generator. • An exergoeconomic analysis and comparison between optimal designs and one real industrial design. - Abstract: The design trade-offs between thermodynamics and economics for thermal systems can be studied with the aid of multi-objective optimization techniques. The investment costs usually increase with increasing thermodynamic performance of a system. In this paper, an enhanced differential evolution with diversity-preserving and density-adjusting mechanisms, and a newly-proposed algorithm for searching the decision space frontier in a single run were used, to conduct the multi-objective optimization of large-scale, supercritical coal-fired plants. The uncertainties associated with cost functions were discussed by analyzing the sensitivity of the decision space frontier to some significant parameters involved in cost functions. Comparisons made with the aid of an exergoeconomic analysis between the cost minimum designs and a real industrial design demonstrated how the plant improvement was achieved. It is concluded that the cost of electricity could be reduced by a 2–4%, whereas the efficiency could be increased by up to two percentage points. The largest uncertainty is introduced by the temperature-related and reheat-related cost coefficients of the steam generator. More reliable data on the price prediction of future advanced materials should be used to obtain more accurate fronts of the objective space

  14. Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Patel G.C.M.

    2016-09-01

    Full Text Available The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.. It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA, particle swarm optimization (PSO and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.

  15. Optimal mechanisms for single machine scheduling

    NARCIS (Netherlands)

    Heydenreich, B.; Mishra, D.; Müller, R.; Uetz, Marc Jochen; Papadimitriou, C.; Zhang, S.

    2008-01-01

    We study the design of optimal mechanisms in a setting here job-agents compete for being processed by a service provider that can handle one job at a time. Each job has a processing time and incurs a waiting cost. Jobs need to be compensated for waiting. We consider two models, one where only the

  16. Bi and tri-objective optimization in the deterministic network interdiction problem

    International Nuclear Information System (INIS)

    Rocco S, Claudio M.; Emmanuel Ramirez-Marquez, Jose; Salazar A, Daniel E.

    2010-01-01

    Solution approaches to the deterministic network interdiction problem have previously been developed for optimizing a single figure-of-merit of the network configuration (i.e. flow that can be transmitted between a source node and a sink node for a fixed network design) under constraints related to limited amount of resources available to interdict network links. These approaches work under the assumption that: (1) nominal capacity of each link is completely reduced when interdicted and (2) there is a single criterion to optimize. This paper presents a newly developed evolutionary algorithm that for the first time allows solving multi-objective optimization models for the design of network interdiction strategies that take into account a variety of figures-of-merit. The algorithm provides an approximation to the optimal Pareto frontier using: (a) techniques in Monte Carlo simulation to generate potential network interdiction strategies, (b) graph theory to analyze strategies' maximum source-sink flow and (c) an evolutionary search that is driven by the probability that a link will belong to the optimal Pareto set. Examples for different sizes of networks and network behavior are used throughout the paper to illustrate and validate the approach.

  17. Multi Objective Optimization Using Genetic Algorithm of a Pneumatic Connector

    Science.gov (United States)

    Salaam, HA; Taha, Zahari; Ya, TMYS Tuan

    2018-03-01

    The concept of sustainability was first introduced by Dr Harlem Brutland in the 1980’s promoting the need to preserve today’s natural environment for the sake of future generations. Based on this concept, John Elkington proposed an approach to measure sustainability known as Triple Bottom Line (TBL). There are three evaluation criteria’s involved in the TBL approach; namely economics, environmental integrity and social equity. In manufacturing industry the manufacturing costs measure the economic sustainability of a company in a long term. Environmental integrity is a measure of the impact of manufacturing activities on the environment. Social equity is complicated to evaluate; but when the focus is at the production floor level, the production operator health can be considered. In this paper, the TBL approach is applied in the manufacturing of a pneumatic nipple hose. The evaluation criteria used are manufacturing costs, environmental impact, ergonomics impact and also energy used for manufacturing. This study involves multi objective optimization by using genetic algorithm of several possible alternatives for material used in the manufacturing of the pneumatic nipple.

  18. Optimization of Single-Layer Braced Domes

    Directory of Open Access Journals (Sweden)

    Grzywiński Maksym

    2017-06-01

    Full Text Available The paper deals with discussion of optimization problem in civil engineering structural space design. Minimization of mass should satisfy the limit state capacity and serviceability conditions. The cross-sectional areas of bars and structural dimensions are taken as design variables. Variables are used in the form of continuous and discrete. The analysis is done using the Structural and Design of Experiments modules of Ansys Workbench v17.2. As result of the method a mass reduction of 46,6 % is achieved.

  19. Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies

    International Nuclear Information System (INIS)

    Safari, Jalal

    2012-01-01

    This paper proposes a variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a novel mathematical model for multi-objective redundancy allocation problems (MORAP). Most researchers about redundancy allocation problem (RAP) have focused on single objective optimization, while there has been some limited research which addresses multi-objective optimization. Also all mathematical multi-objective models of general RAP assume that the type of redundancy strategy for each subsystem is predetermined and known a priori. In general, active redundancy has traditionally received greater attention; however, in practice both active and cold-standby redundancies may be used within a particular system design. The choice of redundancy strategy then becomes an additional decision variable. Thus, the proposed model and solution method are to select the best redundancy strategy, type of components, and levels of redundancy for each subsystem that maximizes the system reliability and minimize total system cost under system-level constraints. This problem belongs to the NP-hard class. This paper presents a second-generation Multiple-Objective Evolutionary Algorithm (MOEA), named NSGA-II to find the best solution for the given problem. The proposed algorithm demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker (DM) with a complete picture of the optimal solution space. After finding the Pareto front, a procedure is used to select the best solution from the Pareto front. Finally, the advantages of the presented multi-objective model and of the proposed algorithm are illustrated by solving test problems taken from the literature and the robustness of the proposed NSGA-II is discussed.

  20. Optimization of object tracking based on enhanced imperialist ...

    African Journals Online (AJOL)

    . Tracking moving object(s) in video/image frame sequences in cluttered scenes usually results in complications and hence performance degradation. This is attributable to complexity in partial and full object occlusions and scene illumination ...

  1. Optimal power system generation scheduling by multi-objective genetic algorithms with preferences

    International Nuclear Information System (INIS)

    Zio, E.; Baraldi, P.; Pedroni, N.

    2009-01-01

    Power system generation scheduling is an important issue both from the economical and environmental safety viewpoints. The scheduling involves decisions with regards to the units start-up and shut-down times and to the assignment of the load demands to the committed generating units for minimizing the system operation costs and the emission of atmospheric pollutants. As many other real-world engineering problems, power system generation scheduling involves multiple, conflicting optimization criteria for which there exists no single best solution with respect to all criteria considered. Multi-objective optimization algorithms, based on the principle of Pareto optimality, can then be designed to search for the set of nondominated scheduling solutions from which the decision-maker (DM) must a posteriori choose the preferred alternative. On the other hand, often, information is available a priori regarding the preference values of the DM with respect to the objectives. When possible, it is important to exploit this information during the search so as to focus it on the region of preference of the Pareto-optimal set. In this paper, ways are explored to use this preference information for driving a multi-objective genetic algorithm towards the preferential region of the Pareto-optimal front. Two methods are considered: the first one extends the concept of Pareto dominance by biasing the chromosome replacement step of the algorithm by means of numerical weights that express the DM' s preferences; the second one drives the search algorithm by changing the shape of the dominance region according to linear trade-off functions specified by the DM. The effectiveness of the proposed approaches is first compared on a case study of literature. Then, a nonlinear, constrained, two-objective power generation scheduling problem is effectively tackled

  2. A modified teaching–learning based optimization for multi-objective optimal power flow problem

    International Nuclear Information System (INIS)

    Shabanpour-Haghighi, Amin; Seifi, Ali Reza; Niknam, Taher

    2014-01-01

    Highlights: • A new modified teaching–learning based algorithm is proposed. • A self-adaptive wavelet mutation strategy is used to enhance the performance. • To avoid reaching a large repository size, a fuzzy clustering technique is used. • An efficiently smart population selection is utilized. • Simulations show the superiority of this algorithm compared with other ones. - Abstract: In this paper, a modified teaching–learning based optimization algorithm is analyzed to solve the multi-objective optimal power flow problem considering the total fuel cost and total emission of the units. The modified phase of the optimization algorithm utilizes a self-adapting wavelet mutation strategy. Moreover, a fuzzy clustering technique is proposed to avoid extremely large repository size besides a smart population selection for the next iteration. These techniques make the algorithm searching a larger space to find the optimal solutions while speed of the convergence remains good. The IEEE 30-Bus and 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures. It is verified that the proposed approach has better performance over other techniques

  3. Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Aristeidis Antonakis

    2017-04-01

    Full Text Available In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft–engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude–Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct interfaces between the optimizer and aircraft/engine performance codes. A two-level optimization scheme is employed and is shown to improve search performance compared to the basic PSO algorithm. The effectiveness of the proposed methodology is demonstrated in a hypothetic engine upgrade scenario for the F-4 aircraft considering the replacement of the aircraft’s J79 engine with the EJ200; a clear advantage of the EJ200-equipped configuration is unveiled, resulting, on average, in 15% faster climbs with 20% less fuel.

  4. Irreversibility analysis for optimization design of plate fin heat exchangers using a multi-objective cuckoo search algorithm

    International Nuclear Information System (INIS)

    Wang, Zhe; Li, Yanzhong

    2015-01-01

    Highlights: • The first application of IMOCS for plate-fin heat exchanger design. • Irreversibility degrees of heat transfer and fluid friction are minimized. • Trade-off of efficiency, total cost and pumping power is achieved. • Both EGM and EDM methods have been compared in the optimization of PFHE. • This study has superiority over other single-objective optimization design. - Abstract: This paper introduces and applies an improved multi-objective cuckoo search (IMOCS) algorithm, a novel met-heuristic optimization algorithm based on cuckoo breeding behavior, for the multi-objective optimization design of plate-fin heat exchangers (PFHEs). A modified irreversibility degree of the PFHE is separated into heat transfer and fluid friction irreversibility degrees which are adopted as two initial objective functions to be minimized simultaneously for narrowing the search scope of the design. The maximization efficiency, minimization of pumping power, and total annual cost are considered final objective functions. Results obtained from a two dimensional normalized Pareto-optimal frontier clearly demonstrate the trade-off between heat transfer and fluid friction irreversibility. Moreover, a three dimensional Pareto-optimal frontier reveals a relationship between efficiency, total annual cost, and pumping power in the PFHE design. Three examples presented here further demonstrate that the presented method is able to obtain optimum solutions with higher accuracy, lower irreversibility, and fewer iterations as compared to the previous methods and single-objective design approaches

  5. Multi-objective optimization of an industrial penicillin V bioreactor train using non-dominated sorting genetic algorithm.

    Science.gov (United States)

    Lee, Fook Choon; Rangaiah, Gade Pandu; Ray, Ajay Kumar

    2007-10-15

    Bulk of the penicillin produced is used as raw material for semi-synthetic penicillin (such as amoxicillin and ampicillin) and semi-synthetic cephalosporins (such as cephalexin and cefadroxil). In the present paper, an industrial penicillin V bioreactor train is optimized for multiple objectives simultaneously. An industrial train, comprising a bank of identical bioreactors, is run semi-continuously in a synchronous fashion. The fermentation taking place in a bioreactor is modeled using a morphologically structured mechanism. For multi-objective optimization for two and three objectives, the elitist non-dominated sorting genetic algorithm (NSGA-II) is chosen. Instead of a single optimum as in the traditional optimization, a wide range of optimal design and operating conditions depicting trade-offs of key performance indicators such as batch cycle time, yield, profit and penicillin concentration, is successfully obtained. The effects of design and operating variables on the optimal solutions are discussed in detail. Copyright 2007 Wiley Periodicals, Inc.

  6. Multi-objective optimization of two alkali catalyzed processes for biodiesel from waste cooking oil

    International Nuclear Information System (INIS)

    Patle, Dipesh S.; Sharma, Shivom; Ahmad, Z.; Rangaiah, G.P.

    2014-01-01

    Highlights: • Biodiesel processes use waste cooking oil and are close to industrial practice. • Detailed constituents of waste cooking oil and detailed kinetics are used. • Two complete processes are optimized for economic and environmental objectives. • Obtained trade-offs provide deeper understanding and alternative optimal solutions. - Abstract: In view of the finite availability and environmental concerns of fossil fuels, biodiesel is one of the promising fuel alternatives. This study considers waste cooking palm oil with 6% free fatty acids (FFA) as feed-stock, which facilitates its better utilization and promotes sustainability. Two biodiesel production processes (both involving esterification catalyzed by sulfuric acid and trans-esterification catalyzed by sodium hydroxide) are compared for economic and environmental objectives. Firstly, these processes are simulated, considering detailed constituents of palm oil and also detailed kinetics for both esterification and trans-esterification, in Aspen Plus simulator. Subsequently, both the processes are optimized considering profit, heat duty and organic waste as objectives, and using an Excel-based multi-objective optimization (EMOO) program for the elitist non-dominated sorting genetic algorithm-II (NSGA-II). The results show that the profit improves with the increase in heat duty, and that the profit increase is accompanied by larger amount of organic waste. Process 1 having three trans-esterification reactors produces significantly lower organic waste (by 32%), requires lower heat duty (by 39%) and slightly more profitable (by 1.6%) compared to Process 2 having a single trans-esterification reactor and also a different separation sequence. Overall, the obtained quantitative trade-offs between objectives enable better decision making about the process design for biodiesel production from waste cooking oil

  7. Multi-objective optimization in the presence of practical constraints using non-dominated sorting hybrid cuckoo search algorithm

    Directory of Open Access Journals (Sweden)

    M. Balasubbareddy

    2015-12-01

    Full Text Available A novel optimization algorithm is proposed to solve single and multi-objective optimization problems with generation fuel cost, emission, and total power losses as objectives. The proposed method is a hybridization of the conventional cuckoo search algorithm and arithmetic crossover operations. Thus, the non-linear, non-convex objective function can be solved under practical constraints. The effectiveness of the proposed algorithm is analyzed for various cases to illustrate the effect of practical constraints on the objectives' optimization. Two and three objective multi-objective optimization problems are formulated and solved using the proposed non-dominated sorting-based hybrid cuckoo search algorithm. The effectiveness of the proposed method in confining the Pareto front solutions in the solution region is analyzed. The results for single and multi-objective optimization problems are physically interpreted on standard test functions as well as the IEEE-30 bus test system with supporting numerical and graphical results and also validated against existing methods.

  8. Optimal segmentation scale parameter selection for object-oriented ...

    African Journals Online (AJOL)

    ikkguy001

    2013-08-05

    Aug 5, 2013 ... hierarchy levels that facilitate their accurate extraction. .... digitized in ArcGIS from the available aerial photography) and image objects ..... Driven Shareholding for Knowledge-based landslide Detection by Object-based Image ...

  9. Optimize Etching Based Single Mode Fiber Optic Temperature Sensor

    OpenAIRE

    Ajay Kumar; Dr. Pramod Kumar

    2014-01-01

    This paper presents a description of etching process for fabrication single mode optical fiber sensors. The process of fabrication demonstrates an optimized etching based method to fabricate single mode fiber (SMF) optic sensors in specified constant time and temperature. We propose a single mode optical fiber based temperature sensor, where the temperature sensing region is obtained by etching its cladding diameter over small length to a critical value. It is observed that th...

  10. Pareto-optimal multi-objective design of airplane control systems

    Science.gov (United States)

    Schy, A. A.; Johnson, K. G.; Giesy, D. P.

    1980-01-01

    A constrained minimization algorithm for the computer aided design of airplane control systems to meet many requirements over a set of flight conditions is generalized using the concept of Pareto-optimization. The new algorithm yields solutions on the boundary of the achievable domain in objective space in a single run, whereas the older method required a sequence of runs to approximate such a limiting solution. However, Pareto-optimality does not guarantee a satisfactory design, since such solutions may emphasize some objectives at the expense of others. The designer must still interact with the program to obtain a well-balanced set of objectives. Using the example of a fighter lateral stability augmentation system (SAS) design over five flight conditions, several effective techniques are developed for obtaining well-balanced Pareto-optimal solutions. For comparison, one of these techniques is also used in a recently developed algorithm of Kreisselmeier and Steinhauser, which replaces the hard constraints with soft constraints, using a special penalty function. It is shown that comparable results can be obtained.

  11. Thermo-economic and environmental analyses based multi-objective optimization of vapor compression–absorption cascaded refrigeration system using NSGA-II technique

    International Nuclear Information System (INIS)

    Jain, Vaibhav; Sachdeva, Gulshan; Kachhwaha, Surendra Singh; Patel, Bhavesh

    2016-01-01

    Highlights: • It addresses multi-objective optimization study on cascaded refrigeration system. • Cascaded system is a promising decarburizing and energy efficient technology. • NSGA-II technique is used for multi-objective optimization. • Total annual product cost and irreversibility rate are simultaneously optimized. - Abstract: Present work optimizes the performance of 170 kW vapor compression–absorption cascaded refrigeration system (VCACRS) based on combined thermodynamic, economic and environmental parameters using Non-dominated Sort Genetic Algorithm-II (NSGA-II) technique. Two objective functions including the total irreversibility rate (as a thermodynamic criterion) and the total product cost (as an economic criterion) of the system are considered simultaneously for multi-objective optimization of VCACRS. The capital and maintenance costs of the system components, the operational cost, and the penalty cost due to CO_2 emission are included in the total product cost of the system. Three optimized systems including a single-objective thermodynamic optimized, a single-objective economic optimized and a multi-objective optimized are analyzed and compared. The results showed that the multi-objective design considers the combined thermodynamic and total product cost criteria better than the two individual single-objective thermodynamic and total product cost optimized designs.

  12. Multi-Objective Two-Dimensional Truss Optimization by using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Harun Alrasyid

    2011-05-01

    Full Text Available During last three decade, many mathematical programming methods have been develop for solving optimization problems. However, no single method has been found to be entirely efficient and robust for the wide range of engineering optimization problems. Most design application in civil engineering involve selecting values for a set of design variables that best describe the behavior and performance of the particular problem while satisfying the requirements and specifications imposed by codes of practice. The introduction of Genetic Algorithm (GA into the field of structural optimization has opened new avenues for research because they have been successful applied while traditional methods have failed. GAs is efficient and broadly applicable global search procedure based on stochastic approach which relies on “survival of the fittest” strategy. GAs are search algorithms that are based on the concepts of natural selection and natural genetics. On this research Multi-objective sizing and configuration optimization of the two-dimensional truss has been conducted using a genetic algorithm. Some preliminary runs of the GA were conducted to determine the best combinations of GA parameters such as population size and probability of mutation so as to get better scaling for rest of the runs. Comparing the results from sizing and sizing– configuration optimization, can obtained a significant reduction in the weight and deflection. Sizing–configuration optimization produces lighter weight and small displacement than sizing optimization. The results were obtained by using a GA with relative ease (computationally and these results are very competitive compared to those obtained from other methods of truss optimization.

  13. Introduction to WMOST v3 and Multi-Objective Optimization

    Science.gov (United States)

    Version 3 of EPA’s Watershed Management Optimization Support Tool (WMOST) will be released in early 2018 (https://www.epa.gov/exposure-assessment-models/wmost). WMOST is designed to facilitate integrated water management among communities, utilities, watershed organization...

  14. optimization of object tracking based on enhanced imperialist ...

    African Journals Online (AJOL)

    Damuut and Dogara

    A typical example is the Roman Empire which had influence or control over ... the Enhance Imperialist Competitive Algorithm (EICA) in optimizing the generated ... segment the video frame into a number of regions based on visual features like ...

  15. Multi-objective parametric optimization of powder mixed electro ...

    Indian Academy of Sciences (India)

    Multiple linear regression models have ... surface optimization scheme to select the parameters in powder mixed EDM process. Keskin ... Genetic algorithm (GA) is a subclass of population based stochastic search procedure which is.

  16. Balancing relevance criteria through multi-objective optimization

    NARCIS (Netherlands)

    van Doorn, J.; Odijk, D.; Roijers, D.M.; de Rijke, M.

    2016-01-01

    Offline evaluation of information retrieval systems typically focuses on a single effectiveness measure that models the utility for a typical user. Such a measure usually combines a behavior-based rank discount with a notion of document utility that captures the single relevance criterion of

  17. An experimental analysis of design choices of multi-objective ant colony optimization algorithms

    OpenAIRE

    Lopez-Ibanez, Manuel; Stutzle, Thomas

    2012-01-01

    There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multi-objective ant colony optimization (MOACO) algorithms. This formulation is based on adding specific algorithm components for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives using pheromone and heuris...

  18. Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

    Science.gov (United States)

    Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

    2017-01-01

    Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

  19. Multi-disciplinary design optimization and performance evaluation of a single stage transonic axial compressor

    International Nuclear Information System (INIS)

    Lee, Sae Il; Lee, Dong Ho; Kim, Kyu Hong; Park, Tae Choon; Lim, Byeung Jun; Kang, Young Seok

    2013-01-01

    The multidisciplinary design optimization method, which integrates aerodynamic performance and structural stability, was utilized in the development of a single-stage transonic axial compressor. An approximation model was created using artificial neural network for global optimization within given ranges of variables and several design constraints. The genetic algorithm was used for the exploration of the Pareto front to find the maximum objective function value. The final design was chosen after a second stage gradient-based optimization process to improve the accuracy of the optimization. To validate the design procedure, numerical simulations and compressor tests were carried out to evaluate the aerodynamic performance and safety factor of the optimized compressor. Comparison between numerical optimal results and experimental data are well matched. The optimum shape of the compressor blade is obtained and compared to the baseline design. The proposed optimization framework improves the aerodynamic efficiency and the safety factor.

  20. Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems

    DEFF Research Database (Denmark)

    Vlachogiannis, Ioannis (John); Lee, K Y

    2009-01-01

    In this paper the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective optimization problems are represented. We emphasize in those, the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analysed and applied in a multi-objective problem...

  1. From tomography to FWI with a single objective function

    KAUST Repository

    Alkhalifah, Tariq Ali; Choi, Yun Seok

    2013-01-01

    Reflections in our seismic data induce serious nonlinear behavior in the objective function of full waveform inversion (FWI). Thus, without a good initial velocity model, that can produce the reflections within a cycle of the frequency used

  2. Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem

    Directory of Open Access Journals (Sweden)

    Susanta Dutta

    2018-05-01

    Full Text Available This paper presents an efficient quasi-oppositional chemical reaction optimization (QOCRO technique to find the feasible optimal solution of the multi objective optimal reactive power dispatch (RPD problem with flexible AC transmission system (FACTS device. The quasi-oppositional based learning (QOBL is incorporated in conventional chemical reaction optimization (CRO, to improve the solution quality and the convergence speed. To check the superiority of the proposed method, it is applied on IEEE 14-bus and 30-bus systems and the simulation results of the proposed approach are compared to those reported in the literature. The computational results reveal that the proposed algorithm has excellent convergence characteristics and is superior to other multi objective optimization algorithms. Keywords: Quasi-oppositional chemical reaction optimization (QOCRO, Reactive power dispatch (RPD, TCSC, SVC, Multi-objective optimization

  3. Multi-objective parametric optimization of powder mixed electro ...

    Indian Academy of Sciences (India)

    Researchers are now focusing on employment of artificial intelligence (AI) techniques viz. ANN, GA, fuzzy logic, etc. for the process modelling and optimization of manufacturing processes which are expected to overcome some of the limitations of conventional process mod- elling techniques. Fenggou & Dayong (2004) ...

  4. Multi objective optimization of foam-filled circular tubes for quasi-static and dynamic responses

    Directory of Open Access Journals (Sweden)

    Fauzan Djamaluddin

    Full Text Available AbstractFuel consumption and safety are currently key aspects in automobile design. The foam-filled thin-walled aluminium tube represents a potentially effective material for use in the automotive industry, due to its energy absorption capability and light weight. Multi-objective crashworthiness design optimization for foam-filled double cylindrical tubes is presented in this paper. The double structures are impacted by a rigid wall simulating quasi-static and dynamic loadings. The optimal parameters under consideration are the minimum peak crushing force and maximum specific energy absorption, using the non-dominated sorting genetic algorithm-II (NSGA-II technique. Radial basis functions (RBF and D-Optimal are adopted to determine the more complex crashworthiness functional objectives. The comparison is performed by finite element analysis of the impact crashworthiness characteristics in tubes under static and dynamic loads. Finally, the optimum crashworthiness performance of empty and foam-filled double tubes is investigated and compared to the traditional single foam-filled tube. The results indicate that the foam-filled double aluminium circular tube can be recommended for crashworthy structures.

  5. Multi-objective optimization of distributed generation with voltage ...

    African Journals Online (AJOL)

    DR OKE

    1*Department of Electrical Engineering, Kamla Nehru Institute of Technology Sultanpurr, ... of DG in distribution systems for different voltage dependent load models and .... The evaluation of the objective function depends only on location, size ...

  6. Adaptive multi-objective Optimization scheme for cognitive radio resource management

    KAUST Repository

    Alqerm, Ismail; Shihada, Basem

    2014-01-01

    configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance

  7. Multi-objective/loading optimization for rotating composite flexbeams

    Science.gov (United States)

    Hamilton, Brian K.; Peters, James R.

    1989-01-01

    With the evolution of advanced composites, the feasibility of designing bearingless rotor systems for high speed, demanding maneuver envelopes, and high aircraft gross weights has become a reality. These systems eliminate the need for hinges and heavily loaded bearings by incorporating a composite flexbeam structure which accommodates flapping, lead-lag, and feathering motions by bending and twisting while reacting full blade centrifugal force. The flight characteristics of a bearingless rotor system are largely dependent on hub design, and the principal element in this type of system is the composite flexbeam. As in any hub design, trade off studies must be performed in order to optimize performance, dynamics (stability), handling qualities, and stresses. However, since the flexbeam structure is the primary component which will determine the balance of these characteristics, its design and fabrication are not straightforward. It was concluded that: pitchcase and snubber damper representations are required in the flexbeam model for proper sizing resulting from dynamic requirements; optimization is necessary for flexbeam design, since it reduces the design iteration time and results in an improved design; and inclusion of multiple flight conditions and their corresponding fatigue allowables is necessary for the optimization procedure.

  8. Multi-objective PID Optimization for Speed Control of an Isolated Steam Turbine using Gentic Algorithm

    OpenAIRE

    Sanjay Kr. Singh; D. Boolchandani; S.G. Modani; Nitish Katal

    2014-01-01

    This study focuses on multi-objective optimization of the PID controllers for optimal speed control for an isolated steam turbine. In complex operations, optimal tuning plays an imperative role in maintaining the product quality and process safety. This study focuses on the comparison of the optimal PID tuning using Multi-objective Genetic Algorithm (NSGA-II) against normal genetic algorithm and Ziegler Nichols methods for the speed control of an isolated steam turbine. Isolated steam turbine...

  9. A procedure for multi-objective optimization of tire design parameters

    OpenAIRE

    Nikola Korunović; Miloš Madić; Miroslav Trajanović; Miroslav Radovanović

    2015-01-01

    The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zo...

  10. Optimal Waste Load Allocation Using Multi-Objective Optimization and Multi-Criteria Decision Analysis

    Directory of Open Access Journals (Sweden)

    L. Saberi

    2016-10-01

    Full Text Available Introduction: Increasing demand for water, depletion of resources of acceptable quality, and excessive water pollution due to agricultural and industrial developments has caused intensive social and environmental problems all over the world. Given the environmental importance of rivers, complexity and extent of pollution factors and physical, chemical and biological processes in these systems, optimal waste-load allocation in river systems has been given considerable attention in the literature in the past decades. The overall objective of planning and quality management of river systems is to develop and implement a coordinated set of strategies and policies to reduce or allocate of pollution entering the rivers so that the water quality matches by proposing environmental standards with an acceptable reliability. In such matters, often there are several different decision makers with different utilities which lead to conflicts. Methods/Materials: In this research, a conflict resolution framework for optimal waste load allocation in river systems is proposed, considering the total treatment cost and the Biological Oxygen Demand (BOD violation characteristics. There are two decision-makers inclusive waste load discharges coalition and environmentalists who have conflicting objectives. This framework consists of an embedded river water quality simulator, which simulates the transport process including reaction kinetics. The trade-off curve between objectives is obtained using the Multi-objective Particle Swarm Optimization Algorithm which these objectives are minimization of the total cost of treatment and penalties that must be paid by discharges and a violation of water quality standards considering BOD parameter which is controlled by environmentalists. Thus, the basic policy of river’s water quality management is formulated in such a way that the decision-makers are ensured their benefits will be provided as far as possible. By using MOPSO

  11. A Tool for Optimizing Observation Planning for Faint Moving Objects

    Science.gov (United States)

    Arredondo, Anicia; Bosh, Amanda S.; Levine, Stephen

    2016-10-01

    Observations of small solar system bodies such as trans-Neptunian objects and Centaurs are vital for understanding the basic properties of these small members of our solar system. Because these objects are often very faint, large telescopes and long exposures may be necessary, which can result in crowded fields in which the target of interest may be blended with a field star. For accurate photometry and astrometry, observations must be planned to occur when the target is free of background stars; this restriction results in limited observing windows. We have created a tool that can be used to plan observations of faint moving objects. Features of the tool include estimates of best times to observe (when the object is not too near another object), a finder chart output, a list of possible astrometric and photometric reference stars, and an exposure time calculator. This work makes use of the USNOFS Image and Catalogue Archive operated by the United States Naval Observatory, Flagstaff Station (S.E. Levine and D.G. Monet 2000), the JPL Horizons online ephemeris service (Giorgini et al. 1996), the Minor Planet Center's MPChecker (http://cgi.minorplanetcenter.net/cgi-bin/checkmp.cgi), and source extraction software SExtractor (Bertin & Arnouts 1996). Support for this work was provided by NASA SSO grant NNX15AJ82G.

  12. Multi-objective Reactive Power Optimization Based on Improved Particle Swarm Algorithm

    Science.gov (United States)

    Cui, Xue; Gao, Jian; Feng, Yunbin; Zou, Chenlu; Liu, Huanlei

    2018-01-01

    In this paper, an optimization model with the minimum active power loss and minimum voltage deviation of node and maximum static voltage stability margin as the optimization objective is proposed for the reactive power optimization problems. By defining the index value of reactive power compensation, the optimal reactive power compensation node was selected. The particle swarm optimization algorithm was improved, and the selection pool of global optimal and the global optimal of probability (p-gbest) were introduced. A set of Pareto optimal solution sets is obtained by this algorithm. And by calculating the fuzzy membership value of the pareto optimal solution sets, individuals with the smallest fuzzy membership value were selected as the final optimization results. The above improved algorithm is used to optimize the reactive power of IEEE14 standard node system. Through the comparison and analysis of the results, it has been proven that the optimization effect of this algorithm was very good.

  13. Research on connection structure of aluminumbody bus using multi-objective topology optimization

    Science.gov (United States)

    Peng, Q.; Ni, X.; Han, F.; Rhaman, K.; Ulianov, C.; Fang, X.

    2018-01-01

    For connecting Aluminum Alloy bus body aluminum components often occur the problem of failure, a new aluminum alloy connection structure is designed based on multi-objective topology optimization method. Determining the shape of the outer contour of the connection structure with topography optimization, establishing a topology optimization model of connections based on SIMP density interpolation method, going on multi-objective topology optimization, and improving the design of the connecting piece according to the optimization results. The results show that the quality of the aluminum alloy connector after topology optimization is reduced by 18%, and the first six natural frequencies are improved and the strength performance and stiffness performance are obviously improved.

  14. Optimizing an objective function under a bivariate probability model

    NARCIS (Netherlands)

    X. Brusset; N.M. Temme (Nico)

    2007-01-01

    htmlabstractThe motivation of this paper is to obtain an analytical closed form of a quadratic objective function arising from a stochastic decision process with bivariate exponential probability distribution functions that may be dependent. This method is applicable when results need to be

  15. Optimal unit sizing for small-scale integrated energy systems using multi-objective interval optimization and evidential reasoning approach

    International Nuclear Information System (INIS)

    Wei, F.; Wu, Q.H.; Jing, Z.X.; Chen, J.J.; Zhou, X.X.

    2016-01-01

    This paper proposes a comprehensive framework including a multi-objective interval optimization model and evidential reasoning (ER) approach to solve the unit sizing problem of small-scale integrated energy systems, with uncertain wind and solar energies integrated. In the multi-objective interval optimization model, interval variables are introduced to tackle the uncertainties of the optimization problem. Aiming at simultaneously considering the cost and risk of a business investment, the average and deviation of life cycle cost (LCC) of the integrated energy system are formulated. In order to solve the problem, a novel multi-objective optimization algorithm, MGSOACC (multi-objective group search optimizer with adaptive covariance matrix and chaotic search), is developed, employing adaptive covariance matrix to make the search strategy adaptive and applying chaotic search to maintain the diversity of group. Furthermore, ER approach is applied to deal with multiple interests of an investor at the business decision making stage and to determine the final unit sizing solution from the Pareto-optimal solutions. This paper reports on the simulation results obtained using a small-scale direct district heating system (DH) and a small-scale district heating and cooling system (DHC) optimized by the proposed framework. The results demonstrate the superiority of the multi-objective interval optimization model and ER approach in tackling the unit sizing problem of integrated energy systems considering the integration of uncertian wind and solar energies. - Highlights: • Cost and risk of investment in small-scale integrated energy systems are considered. • A multi-objective interval optimization model is presented. • A novel multi-objective optimization algorithm (MGSOACC) is proposed. • The evidential reasoning (ER) approach is used to obtain the final optimal solution. • The MGSOACC and ER can tackle the unit sizing problem efficiently.

  16. Optimization of magnetic switches for single particle and cell transport

    Energy Technology Data Exchange (ETDEWEB)

    Abedini-Nassab, Roozbeh; Yellen, Benjamin B., E-mail: yellen@duke.edu [Department of Mechanical Engineering and Materials Science, Duke University, Box 90300 Hudson Hall, Durham, North Carolina 27708 (United States); Joint Institute, University of Michigan—Shanghai Jiao Tong University, Shanghai Jiao Tong University, Shanghai 200240 (China); Murdoch, David M. [Department of Medicine, Duke University, Durham, North Carolina 27708 (United States); Kim, CheolGi [Department of Emerging Materials Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 711-873 (Korea, Republic of)

    2014-06-28

    The ability to manipulate an ensemble of single particles and cells is a key aim of lab-on-a-chip research; however, the control mechanisms must be optimized for minimal power consumption to enable future large-scale implementation. Recently, we demonstrated a matter transport platform, which uses overlaid patterns of magnetic films and metallic current lines to control magnetic particles and magnetic-nanoparticle-labeled cells; however, we have made no prior attempts to optimize the device geometry and power consumption. Here, we provide an optimization analysis of particle-switching devices based on stochastic variation in the particle's size and magnetic content. These results are immediately applicable to the design of robust, multiplexed platforms capable of transporting, sorting, and storing single cells in large arrays with low power and high efficiency.

  17. Dew Point modelling using GEP based multi objective optimization

    OpenAIRE

    Shroff, Siddharth; Dabhi, Vipul

    2013-01-01

    Different techniques are used to model the relationship between temperatures, dew point and relative humidity. Gene expression programming is capable of modelling complex realities with great accuracy, allowing at the same time, the extraction of knowledge from the evolved models compared to other learning algorithms. We aim to use Gene Expression Programming for modelling of dew point. Generally, accuracy of the model is the only objective used by selection mechanism of GEP. This will evolve...

  18. Single-objective vs. multi-objective autocalibration in modelling total suspended solids and phosphorus in a small agricultural watershed with SWAT.

    Science.gov (United States)

    Rasolomanana, Santatriniaina Denise; Lessard, Paul; Vanrolleghem, Peter A

    2012-01-01

    To obtain greater precision in modelling small agricultural watersheds, a shorter simulation time step is beneficial. A daily time step better represents the dynamics of pollutants in the river and provides more realistic simulation results. However, with a daily evaluation performance, good fits are rarely obtained. With the Shuffled Complex Evolution (SCE) method embedded in the Soil and Water Assessment Tool (SWAT), two calibration approaches are available, single-objective or multi-objective optimization. The goal of the present study is to evaluate which approach can improve the daily performance with SWAT, in modelling flow (Q), total suspended solids (TSS) and total phosphorus (TP). The influence of weights assigned to the different variables included in the objective function has also been tested. The results showed that: (i) the model performance depends not only on the choice of calibration approach, but essentially on the influential parameters; (ii) the multi-objective calibration estimating at once all parameters related to all measured variables is the best approach to model Q, TSS and TP; (iii) changing weights does not improve model performance; and (iv) with a single-objective optimization, an excellent water quality modelling performance may hide a loss of performance of predicting flows and unbalanced internal model components.

  19. Study on hybrid multi-objective optimization algorithm for inverse treatment planning of radiation therapy

    International Nuclear Information System (INIS)

    Li Guoli; Song Gang; Wu Yican

    2007-01-01

    Inverse treatment planning for radiation therapy is a multi-objective optimization process. The hybrid multi-objective optimization algorithm is studied by combining the simulated annealing(SA) and genetic algorithm(GA). Test functions are used to analyze the efficiency of algorithms. The hybrid multi-objective optimization SA algorithm, which displacement is based on the evolutionary strategy of GA: crossover and mutation, is implemented in inverse planning of external beam radiation therapy by using two kinds of objective functions, namely the average dose distribution based and the hybrid dose-volume constraints based objective functions. The test calculations demonstrate that excellent converge speed can be achieved. (authors)

  20. Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization

    International Nuclear Information System (INIS)

    Punnathanam, Varun; Kotecha, Prakash

    2017-01-01

    Highlights: • Efficient multi-objective optimization algorithm F-YYPO demonstrated. • Three Stirling engine applications with a total of eight cases. • Improvements in the objective function values of up to 30%. • Superior to the popularly used gamultiobj of MATLAB. • F-YYPO has extremely low time complexity. - Abstract: In this work, we demonstrate the performance of Front-based Yin-Yang-Pair Optimization (F-YYPO) to solve multi-objective problems related to Stirling engine systems. The performance of F-YYPO is compared with that of (i) a recently proposed multi-objective optimization algorithm (Multi-Objective Grey Wolf Optimizer) and (ii) an algorithm popularly employed in literature due to its easy accessibility (MATLAB’s inbuilt multi-objective Genetic Algorithm function: gamultiobj). We consider three Stirling engine based optimization problems: (i) the solar-dish Stirling engine system which considers objectives of output power, thermal efficiency and rate of entropy generation; (ii) Stirling engine thermal model which considers the associated irreversibility of the cycle with objectives of output power, thermal efficiency and pressure drop; and finally (iii) an experimentally validated polytropic finite speed thermodynamics based Stirling engine model also with objectives of output power and pressure drop. We observe F-YYPO to be significantly more effective as compared to its competitors in solving the problems, while requiring only a fraction of the computational time required by the other algorithms.

  1. An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning

    Directory of Open Access Journals (Sweden)

    Nizar Hadi Abbas

    2016-07-01

    Full Text Available This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In order to evaluate the proposed algorithm in term of finding the best solution, six benchmark test functions are used to make a comparison between AMOPSO and the standard MOPSO. The results show that the AMOPSO has a better ability to get away from local optimums with a quickest convergence than the MOPSO. The simulation results using Matlab 2014a, indicate that this methodology is extremely valuable for every robot in multi-robot framework to discover its own particular proper pa‌th from the start to the destination position with minimum distance and time.

  2. Daily dose and shielding optimization in work performance at 'Ukrytie' object

    International Nuclear Information System (INIS)

    Batij, V.G.; Derengovskij, V.V.; Egorov, V.V.; Kuz'menko, V.A.; Rud'ko, V.M.; Sizov, A.A.; Stoyanov, A.I.

    2000-01-01

    The procedure of daily dose and shielding optimization in work performance at 'Ukryttia' object is offered. The recommendations allowing reducing collective effective doze according to the optimization principle are submitted. The technique of shielding optimization is given at stabilization works realization. The optimum shielding calculation example for the strengthening support is given

  3. Multi-objective random search algorithm for simultaneously optimizing wind farm layout and number of turbines

    DEFF Research Database (Denmark)

    Feng, Ju; Shen, Wen Zhong; Xu, Chang

    2016-01-01

    A new algorithm for multi-objective wind farm layout optimization is presented. It formulates the wind turbine locations as continuous variables and is capable of optimizing the number of turbines and their locations in the wind farm simultaneously. Two objectives are considered. One is to maximi...

  4. Optimization of the Single Staggered Wire and Tube Heat Exchanger

    Directory of Open Access Journals (Sweden)

    Arsana I Made

    2016-01-01

    Full Text Available Wire and tube heat exchanger consists of a coiled tube, and wire is welded on the two sides of it in normal direction of the tube. Generally,wire and tube heat exchanger uses inline wire arrangement between the two sides, whereas in this study, it used staggered wire arrangement that reduces the restriction of convection heat transfer. This study performed the optimization of single staggered wire and tube heat exchanger to increase the capacity and reduce the mass of the heat exchanger. Optimization was conducted with the Hooke-Jeeves method, which aims to optimize the geometry of the heat exchanger, especially on the diameter (dw and the distance between wires (pw. The model developed to present heat transfer correlations on single staggered wire and tube heat exchanger was valid. The maximum optimization factor obtained when the diameter wire was 0.9 mm and the distance between wires (pw was 11 mm with the fref value = 1.5837. It means that the optimized design only using mass of 59,10 % and could transfer heat about 98,5 % from the basis design.

  5. Design of a centrifugal compressor impeller using multi-objective optimization algorithm

    International Nuclear Information System (INIS)

    Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong; Choi, Jae Ho

    2009-01-01

    This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

  6. Design of a centrifugal compressor impeller using multi-objective optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of); Choi, Jae Ho [Samsung Techwin Co., Ltd., Changwon (Korea, Republic of)

    2009-07-01

    This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with {epsilon}-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

  7. Multi-objective synthesis of work and heat exchange networks: Optimal balance between economic and environmental performance

    International Nuclear Information System (INIS)

    Onishi, Viviani C.; Ravagnani, Mauro A.S.S.; Jiménez, Laureano; Caballero, José A.

    2017-01-01

    Highlights: • New multi-objective optimization model for the simultaneous WHEN synthesis. • A multistage superstructure allows power and thermal integration of process streams. • Simultaneous minimization of environmental impacts and total annualized cost. • Alternative set of Pareto solutions is presented to support decision-makers. - Abstract: Sustainable and efficient energy use is crucial for lessening carbon dioxide emissions in industrial plants. This paper introduces a new multi-objective optimization model for the synthesis of work and heat exchange networks (WHENs), aiming to obtain the optimal balance between economic and environmental performance. The proposed multistage superstructure allows power and thermal integration of process gaseous streams, through the simultaneous minimization of total annualized cost (TAC) and environmental impacts (EI). The latter objective is determined by environmental indicators that follow the life cycle assessment (LCA) principles. The WHEN superstructure is optimized as a multi-objective mixed-integer nonlinear programming (moMINLP) model and solved with the GAMS software. Results show a decrease of ∼79% in the heat transfer area and ∼32% in the capital cost between the solutions found for single problem optimizations. These results represent a diminution of ∼23.5% in the TAC, while EI is increased in ∼99.2%. As these solutions can be impractical for economic or environmental reasons, we present a set of alternative Pareto-optimal solutions to support decision-makers towards the implementation of more environment-friendly and cost-effective WHENs.

  8. Replica Analysis for Portfolio Optimization with Single-Factor Model

    Science.gov (United States)

    Shinzato, Takashi

    2017-06-01

    In this paper, we use replica analysis to investigate the influence of correlation among the return rates of assets on the solution of the portfolio optimization problem. We consider the behavior of an optimal solution for the case where the return rate is described with a single-factor model and compare the findings obtained from our proposed methods with correlated return rates with those obtained with independent return rates. We then analytically assess the increase in the investment risk when correlation is included. Furthermore, we also compare our approach with analytical procedures for minimizing the investment risk from operations research.

  9. Improving package structure of object-oriented software using multi-objective optimization and weighted class connections

    Directory of Open Access Journals (Sweden)

    Amarjeet

    2017-07-01

    Full Text Available The software maintenance activities performed without following the original design decisions about the package structure usually deteriorate the quality of software modularization, leading to decay of the quality of the system. One of the main reasons for such structural deterioration is inappropriate grouping of source code classes in software packages. To improve such grouping/modular-structure, previous researchers formulated the software remodularization problem as an optimization problem and solved it using search-based meta-heuristic techniques. These optimization approaches aimed at improving the quality metrics values of the structure without considering the original package design decisions, often resulting into a totally new software modularization. The entirely changed software modularization becomes costly to realize as well as difficult to understand for the developers/maintainers. To alleviate this issue, we propose a multi-objective optimization approach to improve the modularization quality of an object-oriented system with minimum possible movement of classes between existing packages of original software modularization. The optimization is performed using NSGA-II, a widely-accepted multi-objective evolutionary algorithm. In order to ensure minimum modification of original package structure, a new approach of computing class relations using weighted strengths has been proposed here. The weights of relations among different classes are computed on the basis of the original package structure. A new objective function has been formulated using these weighted class relations. This objective function drives the optimization process toward better modularization quality simultaneously ensuring preservation of original structure. To evaluate the results of the proposed approach, a series of experiments are conducted over four real-worlds and two random software applications. The experimental results clearly indicate the effectiveness

  10. Designing optimal degradation tests via multi-objective genetic algorithms

    International Nuclear Information System (INIS)

    Marseguerra, Marzio; Zio, Enrico; Cipollone, Maurizio

    2003-01-01

    The experimental determination of the failure time probability distribution of highly reliable components, such as those used in nuclear and aerospace applications, is intrinsically difficult due to the lack, or scarce significance, of failure data which can be collected during the relatively short test periods. A possibility to overcome this difficulty is to resort to the so-called degradation tests, in which measurements of components' degradation are used to infer the failure time distribution. To design such tests, parameters like the number of tests to be run, their frequency and duration, must be set so as to obtain an accurate estimate of the distribution statistics, under the existing limitations of budget. The optimisation problem which results is a non-linear one. In this work, we propose a method, based on multi-objective genetic algorithms for determining the values of the test parameters which optimise both the accuracy in the estimate of the failure time distribution percentiles and the testing costs. The method has been validated on a degradation model of literature

  11. Exergoeconomic multi objective optimization and sensitivity analysis of a regenerative Brayton cycle

    International Nuclear Information System (INIS)

    Naserian, Mohammad Mahdi; Farahat, Said; Sarhaddi, Faramarz

    2016-01-01

    Highlights: • Finite time exergoeconomic multi objective optimization of a Brayton cycle. • Comparing the exergoeconomic and the ecological function optimization results. • Inserting the cost of fluid streams concept into finite-time thermodynamics. • Exergoeconomic sensitivity analysis of a regenerative Brayton cycle. • Suggesting the cycle performance curve drawing and utilization. - Abstract: In this study, the optimal performance of a regenerative Brayton cycle is sought through power maximization and then exergoeconomic optimization using finite-time thermodynamic concept and finite-size components. Optimizations are performed using genetic algorithm. In order to take into account the finite-time and finite-size concepts in current problem, a dimensionless mass-flow parameter is used deploying time variations. The decision variables for the optimum state (of multi objective exergoeconomic optimization) are compared to the maximum power state. One can see that the multi objective exergoeconomic optimization results in a better performance than that obtained with the maximum power state. The results demonstrate that system performance at optimum point of multi objective optimization yields 71% of the maximum power, but only with exergy destruction as 24% of the amount that is produced at the maximum power state and 67% lower total cost rate than that of the maximum power state. In order to assess the impact of the variation of the decision variables on the objective functions, sensitivity analysis is conducted. Finally, the cycle performance curve drawing according to exergoeconomic multi objective optimization results and its utilization, are suggested.

  12. Multi-objective optimization of water quality, pumps operation, and storage sizing of water distribution systems.

    Science.gov (United States)

    Kurek, Wojciech; Ostfeld, Avi

    2013-01-30

    A multi-objective methodology utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) linked to EPANET for trading-off pumping costs, water quality, and tanks sizing of water distribution systems is developed and demonstrated. The model integrates variable speed pumps for modeling the pumps operation, two water quality objectives (one based on chlorine disinfectant concentrations and one on water age), and tanks sizing cost which are assumed to vary with location and diameter. The water distribution system is subject to extended period simulations, variable energy tariffs, Kirchhoff's laws 1 and 2 for continuity of flow and pressure, tanks water level closure constraints, and storage-reliability requirements. EPANET Example 3 is employed for demonstrating the methodology on two multi-objective models, which differ in the imposed water quality objective (i.e., either with disinfectant or water age considerations). Three-fold Pareto optimal fronts are presented. Sensitivity analysis on the storage-reliability constraint, its influence on pumping cost, water quality, and tank sizing are explored. The contribution of this study is in tailoring design (tank sizing), pumps operational costs, water quality of two types, and reliability through residual storage requirements, in a single multi-objective framework. The model was found to be stable in generating multi-objective three-fold Pareto fronts, while producing explainable engineering outcomes. The model can be used as a decision tool for both pumps operation, water quality, required storage for reliability considerations, and tank sizing decision-making. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. Multi-objective optimization design and experimental investigation of centrifugal fan performance

    Science.gov (United States)

    Zhang, Lei; Wang, Songling; Hu, Chenxing; Zhang, Qian

    2013-11-01

    Current studies of fan performance optimization mainly focus on two aspects: one is to improve the blade profile, and another is only to consider the influence of single impeller structural parameter on fan performance. However, there are few studies on the comprehensive effect of the key parameters such as blade number, exit stagger angle of blade and the impeller outlet width on the fan performance. The G4-73 backward centrifugal fan widely used in power plants is selected as the research object. Based on orthogonal design and BP neural network, a model for predicting the centrifugal fan performance parameters is established, and the maximum relative errors of the total pressure and efficiency are 0.974% and 0.333%, respectively. Multi-objective optimization of total pressure and efficiency of the fan is conducted with genetic algorithm, and the optimum combination of impeller structural parameters is proposed. The optimized parameters of blade number, exit stagger angle of blade and the impeller outlet width are seperately 14, 43.9°, and 21 cm. The experiments on centrifugal fan performance and noise are conducted before and after the installation of the new impeller. The experimental results show that with the new impeller, the total pressure of fan increases significantly in total range of the flow rate, and the fan efficiency is improved when the relative flow is above 75%, also the high efficiency area is broadened. Additionally, in 65% -100% relative flow, the fan noise is reduced. Under the design operating condition, total pressure and efficiency of the fan are improved by 6.91% and 0.5%, respectively. This research sheds light on the considering of comprehensive effect of impeller structrual parameters on fan performance, and a new impeller can be designed to satisfy the engineering demand such as energy-saving, noise reduction or solving air pressure insufficiency for power plants.

  14. Optimized Free Energies from Bidirectional Single-Molecule Force Spectroscopy

    Science.gov (United States)

    Minh, David D. L.; Adib, Artur B.

    2008-05-01

    An optimized method for estimating path-ensemble averages using data from processes driven in opposite directions is presented. Based on this estimator, bidirectional expressions for reconstructing free energies and potentials of mean force from single-molecule force spectroscopy—valid for biasing potentials of arbitrary stiffness—are developed. Numerical simulations on a model potential indicate that these methods perform better than unidirectional strategies.

  15. Multi-objective optimization of a bottoming Organic Rankine Cycle (ORC) of gasoline engine using swash-plate expander

    International Nuclear Information System (INIS)

    Galindo, J.; Climent, H.; Dolz, V.; Royo-Pascual, L.

    2016-01-01

    Highlights: • A thermo-economic and sizing model of an ORC in a gasoline engine is carried out. • A multi-objective optimization method to design an ORC for vehicle WHR is presented. • A multiple attribute decision-making method is implemented to select the solution. - Abstract: This paper presents a mathematical model of a bottoming Organic Rankine Cycle coupled to a 2 l turbocharged gasoline engine to optimize the cycle from a thermo-economic and sizing point of view. These criteria were optimized with different cycle values. Therefore, a methodology to optimize the ORC coupled to Waste Heat Recovery systems in vehicle applications is presented using a multi-objective optimization algorithm. Multi-objective optimization results show that the optimum solution depend on the importance of each objective to the final solution. Considering thermo-economic criteria as the main objective, greater sizes will be required. Considering sizing criteria as the main objective, higher thermo-economic parameters will be obtained. Therefore, in order to select a single-solution from the Pareto frontier, a multiple attribute decision-making method (TOPSIS) was implemented in order to take into account the preferences of the Decision Maker. Considering the weight factors 0.5 for Specific Investment Cost (SIC), 0.3 for the area of the heat exchangers (A tot ) and 0.2 for Volume Coefficient (VC) and the boundaries of this particular application, the result is optimized with values of 0.48 m 2 (A tot ), 2515 €/kW (SIC) and 2.62 MJ/m 3 (VC). Moreover, the profitability of the project by means of the Net Present Value and the Payback has been estimated.

  16. Multi-objective optimization for generating a weighted multi-model ensemble

    Science.gov (United States)

    Lee, H.

    2017-12-01

    Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

  17. Optimization of Thermal Object Nonlinear Control Systems by Energy Efficiency Criterion.

    Science.gov (United States)

    Velichkin, Vladimir A.; Zavyalov, Vladimir A.

    2018-03-01

    This article presents the results of thermal object functioning control analysis (heat exchanger, dryer, heat treatment chamber, etc.). The results were used to determine a mathematical model of the generalized thermal control object. The appropriate optimality criterion was chosen to make the control more energy-efficient. The mathematical programming task was formulated based on the chosen optimality criterion, control object mathematical model and technological constraints. The “maximum energy efficiency” criterion helped avoid solving a system of nonlinear differential equations and solve the formulated problem of mathematical programming in an analytical way. It should be noted that in the case under review the search for optimal control and optimal trajectory reduces to solving an algebraic system of equations. In addition, it is shown that the optimal trajectory does not depend on the dynamic characteristics of the control object.

  18. Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    L. DJEROU,

    2012-01-01

    Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

  19. A procedure for multi-objective optimization of tire design parameters

    Directory of Open Access Journals (Sweden)

    Nikola Korunović

    2015-04-01

    Full Text Available The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

  20. Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Kangji Li

    2017-02-01

    Full Text Available Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II, multi-objective particle swarm optimization (MOPSO, the multi-objective genetic algorithm (MOGA and multi-objective differential evolution (MODE, are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.

  1. Multi-objective optimal strategy for generating and bidding in the power market

    International Nuclear Information System (INIS)

    Peng Chunhua; Sun Huijuan; Guo Jianfeng; Liu Gang

    2012-01-01

    Highlights: ► A new benefit/risk/emission comprehensive generation optimization model is established. ► A hybrid multi-objective differential evolution optimization algorithm is designed. ► Fuzzy set theory and entropy weighting method are employed to extract the general best solution. ► The proposed approach of generating and bidding is efficient for maximizing profit and minimizing both risk and emissions. - Abstract: Based on the coordinated interaction between units output and electricity market prices, the benefit/risk/emission comprehensive generation optimization model with objectives of maximal profit and minimal bidding risk and emissions is established. A hybrid multi-objective differential evolution optimization algorithm, which successfully integrates Pareto non-dominated sorting with differential evolution algorithm and improves individual crowding distance mechanism and mutation strategy to avoid premature and unevenly search, is designed to achieve Pareto optimal set of this model. Moreover, fuzzy set theory and entropy weighting method are employed to extract one of the Pareto optimal solutions as the general best solution. Several optimization runs have been carried out on different cases of generation bidding and scheduling. The results confirm the potential and effectiveness of the proposed approach in solving the multi-objective optimization problem of generation bidding and scheduling. In addition, the comparison with the classical optimization algorithms demonstrates the superiorities of the proposed algorithm such as integrality of Pareto front, well-distributed Pareto-optimal solutions, high search speed.

  2. Seeking urbanization security and sustainability: Multi-objective optimization of rainwater harvesting systems in China

    Science.gov (United States)

    Li, Yi; Ye, Quanliang; Liu, An; Meng, Fangang; Zhang, Wenlong; Xiong, Wei; Wang, Peifang; Wang, Chao

    2017-07-01

    Urban rainwater management need to achieve an optimal compromise among water resource augmentation, water loggings alleviation, economic investment and pollutants reduction. Rainwater harvesting (RWH) systems, such as green rooftops, porous pavements, and green lands, have been successfully implemented as viable approaches to alleviate water-logging disasters and water scarcity problems caused by rapid urbanization. However, there is limited guidance to determine the construction areas of RWH systems, especially for stormwater runoff control due to increasing extreme precipitation. This study firstly developed a multi-objective model to optimize the construction areas of green rooftops, porous pavements and green lands, considering the trade-offs among 24 h-interval RWH volume, stormwater runoff volume control ratio (R), economic cost, and rainfall runoff pollutant reduction. Pareto fronts of RWH system areas for 31 provinces of China were obtained through nondominated sorting genetic algorithm. On the national level, the control strategies for the construction rate (the ratio between the area of single RWH system and the total areas of RWH systems) of green rooftops (ηGR), porous pavements (ηPP) and green lands (ηGL) were 12%, 26% and 62%, and the corresponding RWH volume and total suspended solids reduction was 14.84 billion m3 and 228.19 kilotons, respectively. Optimal ηGR , ηPP and ηGL in different regions varied from 1 to 33%, 6 to 54%, and 30 to 89%, respectively. Particularly, green lands were the most important RWH system in 25 provinces with ηGL more than 50%, ηGR mainly less than 15%, and ηPP mainly between 10 and 30%. Results also indicated whether considering the objective MaxR made a non-significant difference for RWH system areas whereas exerted a great influence on the result of stormwater runoff control. Maximum daily rainfall under control increased, exceeding 200% after the construction of the optimal RWH system compared with that before

  3. SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

    International Nuclear Information System (INIS)

    Zhou, Z; Folkert, M; Wang, J

    2016-01-01

    Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

  4. SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

  5. Analysis of process parameters in surface grinding using single objective Taguchi and multi-objective grey relational grade

    Directory of Open Access Journals (Sweden)

    Prashant J. Patil

    2016-09-01

    Full Text Available Close tolerance and good surface finish are achieved by means of grinding process. This study was carried out for multi-objective optimization of MQL grinding process parameters. Water based Al2O3 and CuO nanofluids of various concentrations are used as lubricant for MQL system. Grinding experiments were carried out on instrumented surface grinding machine. For experimentation purpose Taguchi's method was used. Important process parameters that affect the G ratio and surface finish in MQL grinding are depth of cut, type of lubricant, feed rate, grinding wheel speed, coolant flow rate, and nanoparticle size. Grinding performance was calculated by the measurement G ratio and surface finish. For improvement of grinding process a multi-objective process parameter optimization is performed by use of Taguchi based grey relational analysis. To identify most significant factor of process analysis of variance (ANOVA has been used.

  6. Multi-objective PSO based optimal placement of solar power DG in radial distribution system

    Directory of Open Access Journals (Sweden)

    Mahesh Kumar

    2017-06-01

    Full Text Available Ever increasing trend of electricity demand, fossil fuel depletion and environmental issues request the integration of renewable energy into the distribution system. The optimal planning of renewable distributed generation (DG is much essential for ensuring maximum benefits. Hence, this paper proposes the optimal placement of probabilistic based solar power DG into the distribution system. The two objective functions such as power loss reduction and voltage stability index improvement are optimized. The power balance and voltage limits are kept as constraints of the problem. The non-sorting pare to-front based multi-objective particle swarm optimization (MOPSO technique is proposed on standard IEEE 33 radial distribution test system.

  7. An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

    Science.gov (United States)

    Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

    2016-01-01

    This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

  8. Multiple-objective optimization in precision laser cutting of different thermoplastics

    Science.gov (United States)

    Tamrin, K. F.; Nukman, Y.; Choudhury, I. A.; Shirley, S.

    2015-04-01

    Thermoplastics are increasingly being used in biomedical, automotive and electronics industries due to their excellent physical and chemical properties. Due to the localized and non-contact process, use of lasers for cutting could result in precise cut with small heat-affected zone (HAZ). Precision laser cutting involving various materials is important in high-volume manufacturing processes to minimize operational cost, error reduction and improve product quality. This study uses grey relational analysis to determine a single optimized set of cutting parameters for three different thermoplastics. The set of the optimized processing parameters is determined based on the highest relational grade and was found at low laser power (200 W), high cutting speed (0.4 m/min) and low compressed air pressure (2.5 bar). The result matches with the objective set in the present study. Analysis of variance (ANOVA) is then carried out to ascertain the relative influence of process parameters on the cutting characteristics. It was found that the laser power has dominant effect on HAZ for all thermoplastics.

  9. Design optimization of single mixed refrigerant LNG process using a hybrid modified coordinate descent algorithm

    Science.gov (United States)

    Qyyum, Muhammad Abdul; Long, Nguyen Van Duc; Minh, Le Quang; Lee, Moonyong

    2018-01-01

    Design optimization of the single mixed refrigerant (SMR) natural gas liquefaction (LNG) process involves highly non-linear interactions between decision variables, constraints, and the objective function. These non-linear interactions lead to an irreversibility, which deteriorates the energy efficiency of the LNG process. In this study, a simple and highly efficient hybrid modified coordinate descent (HMCD) algorithm was proposed to cope with the optimization of the natural gas liquefaction process. The single mixed refrigerant process was modeled in Aspen Hysys® and then connected to a Microsoft Visual Studio environment. The proposed optimization algorithm provided an improved result compared to the other existing methodologies to find the optimal condition of the complex mixed refrigerant natural gas liquefaction process. By applying the proposed optimization algorithm, the SMR process can be designed with the 0.2555 kW specific compression power which is equivalent to 44.3% energy saving as compared to the base case. Furthermore, in terms of coefficient of performance (COP), it can be enhanced up to 34.7% as compared to the base case. The proposed optimization algorithm provides a deep understanding of the optimization of the liquefaction process in both technical and numerical perspectives. In addition, the HMCD algorithm can be employed to any mixed refrigerant based liquefaction process in the natural gas industry.

  10. Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Mengjun Ming

    2017-05-01

    Full Text Available Due to the scarcity of conventional energy resources and the greenhouse effect, renewable energies have gained more attention. This paper proposes methods for multi-objective optimal design of hybrid renewable energy system (HRES in both isolated-island and grid-connected modes. In each mode, the optimal design aims to find suitable configurations of photovoltaic (PV panels, wind turbines, batteries and diesel generators in HRES such that the system cost and the fuel emission are minimized, and the system reliability/renewable ability (corresponding to different modes is maximized. To effectively solve this multi-objective problem (MOP, the multi-objective evolutionary algorithm based on decomposition (MOEA/D using localized penalty-based boundary intersection (LPBI method is proposed. The algorithm denoted as MOEA/D-LPBI is demonstrated to outperform its competitors on the HRES model as well as a set of benchmarks. Moreover, it effectively obtains a good approximation of Pareto optimal HRES configurations. By further considering a decision maker’s preference, the most satisfied configuration of the HRES can be identified.

  11. Robust Optimization Using Supremum of the Objective Function for Nonlinear Programming Problems

    International Nuclear Information System (INIS)

    Lee, Se Jung; Park, Gyung Jin

    2014-01-01

    In the robust optimization field, the robustness of the objective function emphasizes an insensitive design. In general, the robustness of the objective function can be achieved by reducing the change of the objective function with respect to the variation of the design variables and parameters. However, in conventional methods, when an insensitive design is emphasized, the performance of the objective function can be deteriorated. Besides, if the numbers of the design variables are increased, the numerical cost is quite high in robust optimization for nonlinear programming problems. In this research, the robustness index for the objective function and a process of robust optimization are proposed. Moreover, a method using the supremum of linearized functions is also proposed to reduce the computational cost. Mathematical examples are solved for the verification of the proposed method and the results are compared with those from the conventional methods. The proposed approach improves the performance of the objective function and its efficiency

  12. Optimal control predicts human performance on objects with internal degrees of freedom.

    Directory of Open Access Journals (Sweden)

    Arne J Nagengast

    2009-06-01

    Full Text Available On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

  13. Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    Energy Technology Data Exchange (ETDEWEB)

    Vrugt, Jasper A [Los Alamos National Laboratory; Wohling, Thomas [NON LANL

    2008-01-01

    Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  14. Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Li Ran

    2017-01-01

    Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.

  15. Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

    Science.gov (United States)

    Cao, Jia; Yan, Zheng; He, Guangyu

    2016-06-01

    This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

  16. Multi-Objective Design Optimization of an Over-Constrained Flexure-Based Amplifier

    Directory of Open Access Journals (Sweden)

    Yuan Ni

    2015-07-01

    Full Text Available The optimizing design for enhancement of the micro performance of manipulator based on analytical models is investigated in this paper. By utilizing the established uncanonical linear homogeneous equations, the quasi-static analytical model of the micro-manipulator is built, and the theoretical calculation results are tested by FEA simulations. To provide a theoretical basis for a micro-manipulator being used in high-precision engineering applications, this paper investigates the modal property based on the analytical model. Based on the finite element method, with multipoint constraint equations, the model is built and the results have a good match with the simulation. The following parametric influences studied show that the influences of other objectives on one objective are complicated.  Consequently, the multi-objective optimization by the derived analytical models is carried out to find out the optimal solutions of the manipulator. Besides the inner relationships among these design objectives during the optimization process are discussed.

  17. Multi-Objective Optimization of an In situ Bioremediation Technology to Treat Perchlorate-Contaminated Groundwater

    Science.gov (United States)

    The presentation shows how a multi-objective optimization method is integrated into a transport simulator (MT3D) for estimating parameters and cost of in-situ bioremediation technology to treat perchlorate-contaminated groundwater.

  18. Multi-Objective Optimization for Solid Amine CO2 Removal Assembly in Manned Spacecraft

    Directory of Open Access Journals (Sweden)

    Rong A

    2017-07-01

    Full Text Available Carbon Dioxide Removal Assembly (CDRA is one of the most important systems in the Environmental Control and Life Support System (ECLSS for a manned spacecraft. With the development of adsorbent and CDRA technology, solid amine is increasingly paid attention due to its obvious advantages. However, a manned spacecraft is launched far from the Earth, and its resources and energy are restricted seriously. These limitations increase the design difficulty of solid amine CDRA. The purpose of this paper is to seek optimal design parameters for the solid amine CDRA. Based on a preliminary structure of solid amine CDRA, its heat and mass transfer models are built to reflect some features of the special solid amine adsorbent, Polyethylenepolyamine adsorbent. A multi-objective optimization for the design of solid amine CDRA is discussed further in this paper. In this study, the cabin CO2 concentration, system power consumption and entropy production are chosen as the optimization objectives. The optimization variables consist of adsorption cycle time, solid amine loading mass, adsorption bed length, power consumption and system entropy production. The Improved Non-dominated Sorting Genetic Algorithm (NSGA-II is used to solve this multi-objective optimization and to obtain optimal solution set. A design example of solid amine CDRA in a manned space station is used to show the optimal procedure. The optimal combinations of design parameters can be located on the Pareto Optimal Front (POF. Finally, Design 971 is selected as the best combination of design parameters. The optimal results indicate that the multi-objective optimization plays a significant role in the design of solid amine CDRA. The final optimal design parameters for the solid amine CDRA can guarantee the cabin CO2 concentration within the specified range, and also satisfy the requirements of lightweight and minimum energy consumption.

  19. Low emittance lattice optimization using a multi-objective evolutionary algorithm

    International Nuclear Information System (INIS)

    Gao Weiwei; Wang Lin; Li Weimin; He Duohui

    2011-01-01

    A low emittance lattice design and optimization procedure are systematically studied with a non-dominated sorting-based multi-objective evolutionary algorithm which not only globally searches the low emittance lattice, but also optimizes some beam quantities such as betatron tunes, momentum compaction factor and dispersion function simultaneously. In this paper the detailed algorithm and lattice design procedure are presented. The Hefei light source upgrade project storage ring lattice, with fixed magnet layout, is designed to illustrate this optimization procedure. (authors)

  20. Optimized multiple linear mappings for single image super-resolution

    Science.gov (United States)

    Zhang, Kaibing; Li, Jie; Xiong, Zenggang; Liu, Xiuping; Gao, Xinbo

    2017-12-01

    Learning piecewise linear regression has been recognized as an effective way for example learning-based single image super-resolution (SR) in literature. In this paper, we employ an expectation-maximization (EM) algorithm to further improve the SR performance of our previous multiple linear mappings (MLM) based SR method. In the training stage, the proposed method starts with a set of linear regressors obtained by the MLM-based method, and then jointly optimizes the clustering results and the low- and high-resolution subdictionary pairs for regression functions by using the metric of the reconstruction errors. In the test stage, we select the optimal regressor for SR reconstruction by accumulating the reconstruction errors of m-nearest neighbors in the training set. Thorough experimental results carried on six publicly available datasets demonstrate that the proposed SR method can yield high-quality images with finer details and sharper edges in terms of both quantitative and perceptual image quality assessments.

  1. Optimal redistribution with unobservable disability: welfarist versus non-welfarist social objectives

    OpenAIRE

    Pestieau, Pierre; Racionero, Maria

    2007-01-01

    This paper examines the optimal non linear income and commodity tax when the same labor disutility can receive two alternative interpretations, taste for leisure and disability, but the disability is not readily observable. We compare the optimal policy under alternative social objectives, welfarist and non-welfarist, and conclude that the non-welfarist objective, in which the planner gives a higher weight to the disutility of labour of the disabled individuals, is the only reasonable spec...

  2. Managing XML Data to optimize Performance into Object-Relational Databases

    Directory of Open Access Journals (Sweden)

    Iuliana BOTHA

    2011-06-01

    Full Text Available This paper propose some possibilities for manage XML data in order to optimize performance into object-relational databases. It is detailed the possibility of storing XML data into such databases, using for exemplification an Oracle database and there are tested some optimizing techniques of the queries over XMLType tables, like indexing and partitioning tables.

  3. Aggregate meta-models for evolutionary multiobjective and many-objective optimization

    Czech Academy of Sciences Publication Activity Database

    Pilát, Martin; Neruda, Roman

    Roč. 116, 20 September (2013), s. 392-402 ISSN 0925-2312 R&D Projects: GA ČR GAP202/11/1368 Institutional support: RVO:67985807 Keywords : evolutionary algorithms * multiobjective optimization * many-objective optimization * surrogate models * meta-models * memetic algorithm Subject RIV: IN - Informatics, Computer Science Impact factor: 2.005, year: 2013

  4. Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

    Science.gov (United States)

    Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

    2018-05-01

    A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

  5. Aerodynamic multi-objective integrated optimization based on principal component analysis

    Directory of Open Access Journals (Sweden)

    Jiangtao HUANG

    2017-08-01

    Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

  6. Integrated production planning and control: A multi-objective optimization model

    Directory of Open Access Journals (Sweden)

    Cheng Wang

    2013-09-01

    Full Text Available Purpose: Production planning and control has crucial impact on the production and business activities of enterprise. Enterprise Resource Planning (ERP is the most popular resources planning and management system, however there are some shortcomings and deficiencies in the production planning and control because its core component is still the Material Requirements Planning (MRP. For the defects of ERP system, many local improvement and optimization schemes have been proposed, and improve the feasibility and practicality of the plan in some extent, but study considering the whole planning system optimization in the multiple performance management objectives and achieving better application performance is less. The purpose of this paper is to propose a multi-objective production planning optimization model Based on the point of view of the integration of production planning and control, in order to achieve optimization and control of enterprise manufacturing management. Design/methodology/approach: On the analysis of ERP planning system’s defects and disadvantages, and related research and literature, a multi-objective production planning optimization model is proposed, in addition to net demand and capacity, multiple performance management objectives, such as on-time delivery, production balance, inventory, overtime production, are considered incorporating into the examination scope of the model, so that the manufacturing process could be management and controlled Optimally between multiple objectives. The validity and practicability of the model will be verified by the instance in the last part of the paper. Findings: The main finding is that production planning management of manufacturing enterprise considers not only the capacity and materials, but also a variety of performance management objectives in the production process, and building a multi-objective optimization model can effectively optimize the management and control of enterprise

  7. Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

    International Nuclear Information System (INIS)

    Bellary, Sayed Ahmed Imran; Samad, Abdus; Husain, Afzal

    2014-01-01

    The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

  8. Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

    Energy Technology Data Exchange (ETDEWEB)

    Bellary, Sayed Ahmed Imran; Samad, Abdus [Indian Institute of Technology Madras, Chennai (India); Husain, Afzal [Sultan Qaboos University, Al-Khoudh (Oman)

    2014-12-15

    The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

  9. An Architectural Style for Optimizing System Qualities in Adaptive Embedded Systems using Multi-Objective Optimization

    NARCIS (Netherlands)

    de Roo, Arjan; Sözer, Hasan; Aksit, Mehmet

    Customers of today's complex embedded systems demand the optimization of multiple system qualities under varying operational conditions. To be able to influence the system qualities, the system must have parameters that can be adapted. Constraints may be defined on the value of these parameters.

  10. Building a Smart E-Portfolio Platform for Optimal E-Learning Objects Acquisition

    Directory of Open Access Journals (Sweden)

    Chih-Kun Ke

    2013-01-01

    Full Text Available In modern education, an e-portfolio platform helps students in acquiring e-learning objects in a learning activity. Quality is an important consideration in evaluating the desirable e-learning object. Finding a means of determining a high quality e-learning object from a large number of candidate e-learning objects is an important requirement. To assist student learning in a modern e-portfolio platform, this work proposed an optimal selection approach determining a reasonable e-learning object from various candidate e-learning objects. An optimal selection approach which uses advanced information techniques is proposed. Each e-learning object undergoes a formalization process. An Information Retrieval (IR technique extracts and analyses key concepts from the student’s previous learning contexts. A context-based utility model computes the expected utility values of various e-learning objects based on the extracted key concepts. The expected utility values of e-learning objects are used in a multicriteria decision analysis to determine the optimal selection order of the candidate e-learning objects. The main contribution of this work is the demonstration of an effective e-learning object selection method which is easy to implement within an e-portfolio platform and which makes it smarter.

  11. A mean–variance objective for robust production optimization in uncertain geological scenarios

    DEFF Research Database (Denmark)

    Capolei, Andrea; Suwartadi, Eka; Foss, Bjarne

    2014-01-01

    directly. In the mean–variance bi-criterion objective function risk appears directly, it also considers an ensemble of reservoir models, and has robust optimization as a special extreme case. The mean–variance objective is common for portfolio optimization problems in finance. The Markowitz portfolio...... optimization problem is the original and simplest example of a mean–variance criterion for mitigating risk. Risk is mitigated in oil production by including both the expected NPV (mean of NPV) and the risk (variance of NPV) for the ensemble of possible reservoir models. With the inclusion of the risk...

  12. SU-F-T-342: Dosimetric Constraint Prediction Guided Automatic Mulit-Objective Optimization for Intensity Modulated Radiotherapy

    International Nuclear Information System (INIS)

    Song, T; Zhou, L; Li, Y

    2016-01-01

    Purpose: For intensity modulated radiotherapy, the plan optimization is time consuming with difficulties of selecting objectives and constraints, and their relative weights. A fast and automatic multi-objective optimization algorithm with abilities to predict optimal constraints and manager their trade-offs can help to solve this problem. Our purpose is to develop such a framework and algorithm for a general inverse planning. Methods: There are three main components contained in this proposed multi-objective optimization framework: prediction of initial dosimetric constraints, further adjustment of constraints and plan optimization. We firstly use our previously developed in-house geometry-dosimetry correlation model to predict the optimal patient-specific dosimetric endpoints, and treat them as initial dosimetric constraints. Secondly, we build an endpoint(organ) priority list and a constraint adjustment rule to repeatedly tune these constraints from their initial values, until every single endpoint has no room for further improvement. Lastly, we implement a voxel-independent based FMO algorithm for optimization. During the optimization, a model for tuning these voxel weighting factors respecting to constraints is created. For framework and algorithm evaluation, we randomly selected 20 IMRT prostate cases from the clinic and compared them with our automatic generated plans, in both the efficiency and plan quality. Results: For each evaluated plan, the proposed multi-objective framework could run fluently and automatically. The voxel weighting factor iteration time varied from 10 to 30 under an updated constraint, and the constraint tuning time varied from 20 to 30 for every case until no more stricter constraint is allowed. The average total costing time for the whole optimization procedure is ∼30mins. By comparing the DVHs, better OAR dose sparing could be observed in automatic generated plan, for 13 out of the 20 cases, while others are with competitive

  13. SU-F-T-342: Dosimetric Constraint Prediction Guided Automatic Mulit-Objective Optimization for Intensity Modulated Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Song, T; Zhou, L [Southern Medical University, Guangzhou, Guangdong (China); Li, Y [Beihang University, Beijing, Beijing (China)

    2016-06-15

    Purpose: For intensity modulated radiotherapy, the plan optimization is time consuming with difficulties of selecting objectives and constraints, and their relative weights. A fast and automatic multi-objective optimization algorithm with abilities to predict optimal constraints and manager their trade-offs can help to solve this problem. Our purpose is to develop such a framework and algorithm for a general inverse planning. Methods: There are three main components contained in this proposed multi-objective optimization framework: prediction of initial dosimetric constraints, further adjustment of constraints and plan optimization. We firstly use our previously developed in-house geometry-dosimetry correlation model to predict the optimal patient-specific dosimetric endpoints, and treat them as initial dosimetric constraints. Secondly, we build an endpoint(organ) priority list and a constraint adjustment rule to repeatedly tune these constraints from their initial values, until every single endpoint has no room for further improvement. Lastly, we implement a voxel-independent based FMO algorithm for optimization. During the optimization, a model for tuning these voxel weighting factors respecting to constraints is created. For framework and algorithm evaluation, we randomly selected 20 IMRT prostate cases from the clinic and compared them with our automatic generated plans, in both the efficiency and plan quality. Results: For each evaluated plan, the proposed multi-objective framework could run fluently and automatically. The voxel weighting factor iteration time varied from 10 to 30 under an updated constraint, and the constraint tuning time varied from 20 to 30 for every case until no more stricter constraint is allowed. The average total costing time for the whole optimization procedure is ∼30mins. By comparing the DVHs, better OAR dose sparing could be observed in automatic generated plan, for 13 out of the 20 cases, while others are with competitive

  14. Long working distance objective lenses for single atom trapping and imaging

    Energy Technology Data Exchange (ETDEWEB)

    Pritchard, J. D., E-mail: jonathan.pritchard@strath.ac.uk [Department of Physics, University of Wisconsin-Madison, 1150 University Avenue, Madison, Wisconsin 53706 (United States); Department of Physics, University of Strathclyde, 107 Rottenrow East, Glasgow G4 0NG (United Kingdom); Isaacs, J. A.; Saffman, M. [Department of Physics, University of Wisconsin-Madison, 1150 University Avenue, Madison, Wisconsin 53706 (United States)

    2016-07-15

    We present a pair of optimized objective lenses with long working distances of 117 mm and 65 mm, respectively, that offer diffraction limited performance for both Cs and Rb wavelengths when imaging through standard vacuum windows. The designs utilise standard catalog lens elements to provide a simple and cost-effective solution. Objective 1 provides NA = 0.175 offering 3 μm resolution whilst objective 2 is optimized for high collection efficiency with NA = 0.29 and 1.8 μm resolution. This flexible design can be further extended for use at shorter wavelengths by simply re-optimising the lens separations.

  15. Enhancing State-of-the-art Multi-objective Optimization Algorithms by Applying Domain Specific Operators

    DEFF Research Database (Denmark)

    Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

    2015-01-01

    optimization problems where the environment does not change dynamically. For that reason, the requirement for convergence in static optimization problems is not as timecritical as for dynamic optimization problems. Most MOEAs use generic variables and operators that scale to static multi-objective optimization...... problem. The domain specific operators only encode existing knowledge about the environment. A comprehensive comparative study is provided to evaluate the results of applying the CONTROLEUM-GA compared to NSGAII, e-NSGAII and e- MOEA. Experimental results demonstrate clear improvements in convergence time...

  16. Application of evolutionary algorithms for multi-objective optimization in VLSI and embedded systems

    CERN Document Server

    2015-01-01

    This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design. Many complex engineering optimization problems can be modelled as multi-objective formulations. This book provides an introduction to multi-objective optimization using meta-heuristic algorithms, GA and PSO, and how they can be applied to problems like hardware/software partitioning in embedded systems, circuit partitioning in VLSI, design of operational amplifiers in analog VLSI, design space exploration in high-level synthesis, delay fault testing in VLSI testing, and scheduling in heterogeneous distributed systems. It is shown how, in each case, the various aspects of the EA, namely its representation, and operators like crossover, mutation, etc. can be separately formulated to solve these problems. This book is intended for design engineers and researchers in the field ...

  17. Multi-Objective Optimization Design for a Hybrid Energy System Using the Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Myeong Jin Ko

    2015-04-01

    Full Text Available To secure a stable energy supply and bring renewable energy to buildings within a reasonable cost range, a hybrid energy system (HES that integrates both fossil fuel energy systems (FFESs and new and renewable energy systems (NRESs needs to be designed and applied. This paper presents a methodology to optimize a HES consisting of three types of NRESs and six types of FFESs while simultaneously minimizing life cycle cost (LCC, maximizing penetration of renewable energy and minimizing annual greenhouse gas (GHG emissions. An elitist non-dominated sorting genetic algorithm is utilized for multi-objective optimization. As an example, we have designed the optimal configuration and sizing for a HES in an elementary school. The evolution of Pareto-optimal solutions according to the variation in the economic, technical and environmental objective functions through generations is discussed. The pair wise trade-offs among the three objectives are also examined.

  18. Multi-objective optimal power flow for active distribution network considering the stochastic characteristic of photovoltaic

    Science.gov (United States)

    Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming

    2017-05-01

    To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.

  19. Tuning rules for robust FOPID controllers based on multi-objective optimization with FOPDT models.

    Science.gov (United States)

    Sánchez, Helem Sabina; Padula, Fabrizio; Visioli, Antonio; Vilanova, Ramon

    2017-01-01

    In this paper a set of optimally balanced tuning rules for fractional-order proportional-integral-derivative controllers is proposed. The control problem of minimizing at once the integrated absolute error for both the set-point and the load disturbance responses is addressed. The control problem is stated as a multi-objective optimization problem where a first-order-plus-dead-time process model subject to a robustness, maximum sensitivity based, constraint has been considered. A set of Pareto optimal solutions is obtained for different normalized dead times and then the optimal balance between the competing objectives is obtained by choosing the Nash solution among the Pareto-optimal ones. A curve fitting procedure has then been applied in order to generate suitable tuning rules. Several simulation results show the effectiveness of the proposed approach. Copyright © 2016. Published by Elsevier Ltd.

  20. To the Problem of Energy Security and Energy Objects Control Optimization

    International Nuclear Information System (INIS)

    Gotsiridze, A.; Abzianidze, D.

    2004-01-01

    One of the method of studying energy security of energy objects is evaluation of character and range of main safety risk influence with the help of indicator analysis. In the work is also reviewed an example of applying modern management theory to the group of tasks, connected with the optimal management of energy objects, which is the basis of their secure functioning. (authors)

  1. Mission-profile based multi-objective optimization of power electronics converter for wind turbines

    DEFF Research Database (Denmark)

    Gohil, Ghanshyamsinh; Teodorescu, Remus; Kerekes, Tamas

    2017-01-01

    -objective optimization approach for designing power converter is presented. The objective is to minimize the energy loss for a given load profile as against the conventional approach of minimizing power loss at specific loading conditions. The proposed approach is illustrated by designing a grid-side power converter...

  2. A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2014-01-01

    Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

  3. Incorporating single detector failure into the ROP detector layout optimization for CANDU reactors

    Energy Technology Data Exchange (ETDEWEB)

    Kastanya, Doddy, E-mail: Doddy.Kastanya@snclavalin.com

    2015-12-15

    Highlights: • ROP TSP value needs to be adjusted when any detector in the system fails. • Single detector failure criterion has been incorporated into the detector layout optimization as a constraint. • Results show that the optimized detector layout is more robust with respect to its vulnerability to a single detector failure. • An early rejection scheme has been introduced to speed-up the optimization process. - Abstract: In CANDU{sup ®} reactors, the regional overpower protection (ROP) systems are designed to protect the reactor against overpower in the fuel which could reduce the safety margin-to-dryout. In the CANDU{sup ®} 600 MW (CANDU 6) design, there are two ROP systems in the core, each of which is connected to a fast-acting shutdown system. Each ROP system consists of a number of fast-responding, self-powered flux detectors suitably distributed throughout the core within vertical and horizontal flux detector assemblies. The placement of these ROP detectors is a challenging discrete optimization problem. In the past few years, two algorithms, DETPLASA and ADORE, have been developed to optimize the detector layout for the ROP systems in CANDU reactors. These algorithms utilize the simulated annealing (SA) technique to optimize the placement of the detectors in the core. The objective of the optimization process is typically either to maximize the TSP value for a given number of detectors in the system or to minimize the number of detectors in the system to obtain a target TSP value. One measure to determine the robustness of the optimized detector layout is to evaluate the maximum decrease (penalty) in TSP value when any single detector in the system fails. The smaller the penalty, the more robust the design is. Therefore, in order to ensure that the optimized detector layout is robust, the single detector failure (SDF) criterion has been incorporated as an additional constraint into the ADORE algorithm. Results from this study indicate that there

  4. Practical solutions for multi-objective optimization: An application to system reliability design problems

    International Nuclear Information System (INIS)

    Taboada, Heidi A.; Baheranwala, Fatema; Coit, David W.; Wattanapongsakorn, Naruemon

    2007-01-01

    For multiple-objective optimization problems, a common solution methodology is to determine a Pareto optimal set. Unfortunately, these sets are often large and can become difficult to comprehend and consider. Two methods are presented as practical approaches to reduce the size of the Pareto optimal set for multiple-objective system reliability design problems. The first method is a pseudo-ranking scheme that helps the decision maker select solutions that reflect his/her objective function priorities. In the second approach, we used data mining clustering techniques to group the data by using the k-means algorithm to find clusters of similar solutions. This provides the decision maker with just k general solutions to choose from. With this second method, from the clustered Pareto optimal set, we attempted to find solutions which are likely to be more relevant to the decision maker. These are solutions where a small improvement in one objective would lead to a large deterioration in at least one other objective. To demonstrate how these methods work, the well-known redundancy allocation problem was solved as a multiple objective problem by using the NSGA genetic algorithm to initially find the Pareto optimal solutions, and then, the two proposed methods are applied to prune the Pareto set

  5. Multi-Objective Stochastic Optimization Programs for a Non-Life Insurance Company under Solvency Constraints

    Directory of Open Access Journals (Sweden)

    Massimiliano Kaucic

    2015-09-01

    Full Text Available In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order to generate a dotted approximation of the Pareto frontier for bi- and tri-objective programming problems. Numerical experiments are carried out on a set of portfolios to be optimized for an EU-based non-life insurance company. Both performance indicators and risk measures are managed as objectives. Results show that this procedure is effective and readily applicable to achieve suitable risk-reward tradeoff analysis.

  6. Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design

    International Nuclear Information System (INIS)

    Toffolo, A.; Lazzaretto, A.

    2002-01-01

    Thermoeconomic analyses in thermal system design are always focused on the economic objective. However, knowledge of only the economic minimum may not be sufficient in the decision making process, since solutions with a higher thermodynamic efficiency, in spite of small increases in total costs, may result in much more interesting designs due to changes in energy market prices or in energy policies. This paper suggests how to perform a multi-objective optimization in order to find solutions that simultaneously satisfy exergetic and economic objectives. This corresponds to a search for the set of Pareto optimal solutions with respect to the two competing objectives. The optimization process is carried out by an evolutionary algorithm, that features a new diversity preserving mechanism using as a test case the well-known CGAM problem. (author)

  7. Bi-objective optimization of a multiple-target active debris removal mission

    Science.gov (United States)

    Bérend, Nicolas; Olive, Xavier

    2016-05-01

    The increasing number of space debris in Low-Earth Orbit (LEO) raises the question of future Active Debris Removal (ADR) operations. Typical ADR scenarios rely on an Orbital Transfer Vehicle (OTV) using one of the two following disposal strategies: the first one consists in attaching a deorbiting kit, such as a solid rocket booster, to the debris after rendezvous; with the second one, the OTV captures the debris and moves it to a low-perigee disposal orbit. For multiple-target ADR scenarios, the design of such a mission is very complex, as it involves two optimization levels: one for the space debris sequence, and a second one for the "elementary" orbit transfer strategy from a released debris to the next one in the sequence. This problem can be seen as a Time-Dependant Traveling Salesman Problem (TDTSP) with two objective functions to minimize: the total mission duration and the total propellant consumption. In order to efficiently solve this problem, ONERA has designed, under CNES contract, TOPAS (Tool for Optimal Planning of ADR Sequence), a tool that implements a Branch & Bound method developed in previous work together with a dedicated algorithm for optimizing the "elementary" orbit transfer. A single run of this tool yields an estimation of the Pareto front of the problem, which exhibits the trade-off between mission duration and propellant consumption. We first detail our solution to cope with the combinatorial explosion of complex ADR scenarios with 10 debris. The key point of this approach is to define the orbit transfer strategy through a small set of parameters, allowing an acceptable compromise between the quality of the optimum solution and the calculation cost. Then we present optimization results obtained for various 10 debris removal scenarios involving a 15-ton OTV, using either the deorbiting kit or the disposal orbit strategy. We show that the advantage of one strategy upon the other depends on the propellant margin, the maximum duration allowed

  8. Time-domain single-source integral equations for analyzing scattering from homogeneous penetrable objects

    KAUST Repository

    Valdé s, Felipe; Andriulli, Francesco P.; Bagci, Hakan; Michielssen, Eric

    2013-01-01

    Single-source time-domain electric-and magnetic-field integral equations for analyzing scattering from homogeneous penetrable objects are presented. Their temporal discretization is effected by using shifted piecewise polynomial temporal basis

  9. Objective Lens Optimized for Wavefront Delivery, Pupil Imaging, and Pupil Ghosting

    Science.gov (United States)

    Olzcak, Gene

    2009-01-01

    An interferometer objective lens (or diverger) may be used to transform a collimated beam into a diverging or converging beam. This innovation provides an objective lens that has diffraction-limited optical performance that is optimized at two sets of conjugates: imaging to the objective focus and imaging to the pupil. The lens thus provides for simultaneous delivery of a high-quality beam and excellent pupil resolution properties.

  10. VNM: An R Package for Finding Multiple-Objective Optimal Designs for the 4-Parameter Logistic Model

    OpenAIRE

    Hyun, Seung Won; Wong, Weng Kee; Yang, Yarong

    2018-01-01

    A multiple-objective optimal design is useful for dose-response studies because it can incorporate several objectives at the design stage. Objectives can be of varying interests and a properly constructed multiple-objective optimal design can provide user-specified efficiencies, delivering higher efficiencies for the more important objectives. In this work, we introduce the VNM package written in R for finding 3-objective locally optimal designs for the 4-parameter logistic (4PL) model widely...

  11. Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

    Science.gov (United States)

    Valdés, Julio J; Barton, Alan J

    2007-05-01

    A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

  12. Polar vessel hullform design based on the multi-objective optimization NSGA II

    Directory of Open Access Journals (Sweden)

    DUAN Fei

    2017-12-01

    Full Text Available [Objectives] With the increasing exploitation of the Arctic abundant oil and gas resources, a large number of ships which meet the polar navigational requirements are needed.[Methods] In this paper, the fast elitist Non-Dominated Sorting Genetic Algorithm (NSGA Ⅱ is applied to the hull optimization, and the multi-objective optimization method of polar vessel design is proposed. With the optimization goal of resistance and icebreaking resistance, filtering hull forms through the standard of polar vessel displacement and EEDI, fast ship hull optimization that satisfy the ice-ship dead weight and EEDI requirements has been achieved. Taking a 65 000 t shuttle tanker as an example, full parametric modeling method is adopted, the hull optimization of three different bow forms is conducted through the polar vessel multi-objective optimization method.[Results] The ship hull after optimization can satisfy the IA class navigation require, where the resistance in calm water decreases up to 12.94%, and the minimum propulsion power in ice field has a 27.36% reduction.[Conclusions] The feasibility and validity of the NSGA Ⅱ applying in polar vessel design is verified.

  13. Adaptive multi-objective Optimization scheme for cognitive radio resource management

    KAUST Repository

    Alqerm, Ismail

    2014-12-01

    Cognitive Radio is an intelligent Software Defined Radio that is capable to alter its transmission parameters according to predefined objectives and wireless environment conditions. Cognitive engine is the actuator that performs radio parameters configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance. The optimization relies on adapting radio transmission parameters to environment conditions using constrained optimization modeling called fitness functions in an iterative manner. These functions include minimizing power consumption, Bit Error Rate, delay and interference. On the other hand, maximizing throughput and spectral efficiency. Cross-layer optimization is exploited to access environmental parameters from all TCP/IP stack layers. AMOS uses adaptive Genetic Algorithm in terms of its parameters and objective weights as the vehicle of optimization. The proposed scheme has demonstrated quick response and efficiency in three different scenarios compared to other schemes. In addition, it shows its capability to optimize the performance of TCP/IP layers as whole not only the physical layer.

  14. Multi-Objective Optimization Control for the Aerospace Dual-Active Bridge Power Converter

    Directory of Open Access Journals (Sweden)

    Tao Lei

    2018-05-01

    Full Text Available With the development of More Electrical Aircraft (MEA, the electrification of secondary power systems in aircraft is becoming more and more common. As the key power conversion device, the dual active bridge (DAB converter is the power interface for the energy storage system with the high voltage direct current (HVDC bus in aircraft electrical power systems. In this paper, a DAB DC-DC converter is designed to meet aviation requirements. The extended dual phase shifted control strategy is adopted, and a multi-objective genetic algorithm is applied to optimize its operating performance. Considering the three indicators of inductance current root mean square root (RMS value, negative reverse power and direct current (DC bias component of the current for the high frequency transformer as the optimization objectives, the DAB converter’s optimization model is derived to achieve soft switching as the main constraint condition. Optimized methods of controlling quantity for the DAB based on the evolution and genetic algorithm is used to solve the model, and a number of optimal control parameters are obtained under different load conditions. The results of digital, hard-in-loop simulation and hardware prototype experiments show that the three performance indexes are all suppressed greatly, and the optimization method proposed in this paper is reasonable. The work of this paper provides a theoretical basis and researching method for the multi-objective optimization of the power converter in the aircraft electrical power system.

  15. Two-objective on-line optimization of supervisory control strategy

    Energy Technology Data Exchange (ETDEWEB)

    Nassif, N.; Kajl, S.; Sabourin, R. [Ecole de Technologie Superieure, Montreal (Canada)

    2004-09-01

    The set points of supervisory control strategy are optimized with respect to energy use and thermal comfort for existing HVAC systems. The set point values of zone temperatures, supply duct static pressure, and supply air temperature are the problem variables, while energy use and thermal comfort are the objective functions. The HVAC system model includes all the individual component models developed and validated against the monitored data of an existing VAV system. It serves to calculate energy use during the optimization process, whereas the actual energy use is determined by using monitoring data and the appropriate validated component models. A comparison, done for one summer week, of actual and optimal energy use shows that the on-line implementation of a genetic algorithm optimization program to determine the optimal set points of supervisory control strategy could save energy by 19.5%, while satisfying the minimum zone airflow rates and the thermal comfort. The results also indicate that the application of the two-objective optimization problem can help control daily energy use or daily building thermal comfort, thus saving more energy than the application of the one-objective optimization problem. (Author)

  16. Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

    Science.gov (United States)

    Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

    2015-05-01

    A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Multi-objective reliability redundancy allocation in an interval environment using particle swarm optimization

    International Nuclear Information System (INIS)

    Zhang, Enze; Chen, Qingwei

    2016-01-01

    Most of the existing works addressing reliability redundancy allocation problems are based on the assumption of fixed reliabilities of components. In real-life situations, however, the reliabilities of individual components may be imprecise, most often given as intervals, under different operating or environmental conditions. This paper deals with reliability redundancy allocation problems modeled in an interval environment. An interval multi-objective optimization problem is formulated from the original crisp one, where system reliability and cost are simultaneously considered. To render the multi-objective particle swarm optimization (MOPSO) algorithm capable of dealing with interval multi-objective optimization problems, a dominance relation for interval-valued functions is defined with the help of our newly proposed order relations of interval-valued numbers. Then, the crowding distance is extended to the multi-objective interval-valued case. Finally, the effectiveness of the proposed approach has been demonstrated through two numerical examples and a case study of supervisory control and data acquisition (SCADA) system in water resource management. - Highlights: • We model the reliability redundancy allocation problem in an interval environment. • We apply the particle swarm optimization directly on the interval values. • A dominance relation for interval-valued multi-objective functions is defined. • The crowding distance metric is extended to handle imprecise objective functions.

  18. Sensory Agreement Guides Kinetic Energy Optimization of Arm Movements during Object Manipulation.

    Directory of Open Access Journals (Sweden)

    Ali Farshchiansadegh

    2016-04-01

    Full Text Available The laws of physics establish the energetic efficiency of our movements. In some cases, like locomotion, the mechanics of the body dominate in determining the energetically optimal course of action. In other tasks, such as manipulation, energetic costs depend critically upon the variable properties of objects in the environment. Can the brain identify and follow energy-optimal motions when these motions require moving along unfamiliar trajectories? What feedback information is required for such optimal behavior to occur? To answer these questions, we asked participants to move their dominant hand between different positions while holding a virtual mechanical system with complex dynamics (a planar double pendulum. In this task, trajectories of minimum kinetic energy were along curvilinear paths. Our findings demonstrate that participants were capable of finding the energy-optimal paths, but only when provided with veridical visual and haptic information pertaining to the object, lacking which the trajectories were executed along rectilinear paths.

  19. Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

    Science.gov (United States)

    Nguyen, Nhan T.

    2012-01-01

    This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

  20. Multi-objective optimization of circular magnetic abrasive polishing of SUS304 and Cu materials

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, NhatTan; Yin, ShaoHui; Chen, FengJun; Yin, HanFeng [Hunan University, Changsha (China); Pham, VanThoan [Hanoi University, Hanoi (Viet Nam); Tran, TrongNhan [Industrial University of Ho Chi Minh City, HCM City (Viet Nam)

    2016-06-15

    In this paper, a Multi-objective particle swarm optimization algorithm (MOPSOA) is applied to optimize surface roughness of workpiece after circular magnetic abrasive polishing. The most important parameters of polishing model, namely current, gap between pole and workpiece, spindle speed and polishing time, were considered in this approach. The objective functions of the MOPSOA depend on the quality of surface roughness of polishing materials with both simultaneous surfaces (Ra1, Ra2), which are determined by means of experimental approach with the aid of circular magnetic field. Finally, the effectiveness of the approach is compared between the optimal results with the experimental data. The results show that the new proposed polishing optimization method is more feasible.

  1. A Global Multi-Objective Optimization Tool for Design of Mechatronic Components using Generalized Differential Evolution

    DEFF Research Database (Denmark)

    Bech, Michael Møller; Nørgård, Christian; Roemer, Daniel Beck

    2016-01-01

    This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri-objectiv......This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri...... different optimization control parameter settings and it is concluded that GDE3 is a reliable optimization tool that can assist mechatronic engineers in the design and decision making process....

  2. Optimal Rules for Single Machine Scheduling with Stochastic Breakdowns

    Directory of Open Access Journals (Sweden)

    Jinwei Gu

    2014-01-01

    Full Text Available This paper studies the problem of scheduling a set of jobs on a single machine subject to stochastic breakdowns, where jobs have to be restarted if preemptions occur because of breakdowns. The breakdown process of the machine is independent of the jobs processed on the machine. The processing times required to complete the jobs are constants if no breakdown occurs. The machine uptimes are independently and identically distributed (i.i.d. and are subject to a uniform distribution. It is proved that the Longest Processing Time first (LPT rule minimizes the expected makespan. For the large-scale problem, it is also showed that the Shortest Processing Time first (SPT rule is optimal to minimize the expected total completion times of all jobs.

  3. Spatial Optimization of Future Urban Development with Regards to Climate Risk and Sustainability Objectives.

    Science.gov (United States)

    Caparros-Midwood, Daniel; Barr, Stuart; Dawson, Richard

    2017-11-01

    Future development in cities needs to manage increasing populations, climate-related risks, and sustainable development objectives such as reducing greenhouse gas emissions. Planners therefore face a challenge of multidimensional, spatial optimization in order to balance potential tradeoffs and maximize synergies between risks and other objectives. To address this, a spatial optimization framework has been developed. This uses a spatially implemented genetic algorithm to generate a set of Pareto-optimal results that provide planners with the best set of trade-off spatial plans for six risk and sustainability objectives: (i) minimize heat risks, (ii) minimize flooding risks, (iii) minimize transport travel costs to minimize associated emissions, (iv) maximize brownfield development, (v) minimize urban sprawl, and (vi) prevent development of greenspace. The framework is applied to Greater London (U.K.) and shown to generate spatial development strategies that are optimal for specific objectives and differ significantly from the existing development strategies. In addition, the analysis reveals tradeoffs between different risks as well as between risk and sustainability objectives. While increases in heat or flood risk can be avoided, there are no strategies that do not increase at least one of these. Tradeoffs between risk and other sustainability objectives can be more severe, for example, minimizing heat risk is only possible if future development is allowed to sprawl significantly. The results highlight the importance of spatial structure in modulating risks and other sustainability objectives. However, not all planning objectives are suited to quantified optimization and so the results should form part of an evidence base to improve the delivery of risk and sustainability management in future urban development. © 2017 The Authors Risk Analysis published by Wiley Periodicals, Inc. on behalf of Society for Risk Analysis.

  4. Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

    Science.gov (United States)

    Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

    2018-04-01

    Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

  5. A new multi objective optimization model for designing a green supply chain network under uncertainty

    Directory of Open Access Journals (Sweden)

    Mohammad Mahdi Saffar

    2015-01-01

    Full Text Available Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ε-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE is applied.

  6. Multi-objective optimal design of sandwich panels using a genetic algorithm

    Science.gov (United States)

    Xu, Xiaomei; Jiang, Yiping; Pueh Lee, Heow

    2017-10-01

    In this study, an optimization problem concerning sandwich panels is investigated by simultaneously considering the two objectives of minimizing the panel mass and maximizing the sound insulation performance. First of all, the acoustic model of sandwich panels is discussed, which provides a foundation to model the acoustic objective function. Then the optimization problem is formulated as a bi-objective programming model, and a solution algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is provided to solve the proposed model. Finally, taking an example of a sandwich panel that is expected to be used as an automotive roof panel, numerical experiments are carried out to verify the effectiveness of the proposed model and solution algorithm. Numerical results demonstrate in detail how the core material, geometric constraints and mechanical constraints impact the optimal designs of sandwich panels.

  7. Multi-objective thermodynamic optimization of combined Brayton and inverse Brayton cycles using genetic algorithms

    International Nuclear Information System (INIS)

    Besarati, S.M.; Atashkari, K.; Jamali, A.; Hajiloo, A.; Nariman-zadeh, N.

    2010-01-01

    This paper presents a simultaneous optimization study of two outputs performance of a previously proposed combined Brayton and inverse Brayton cycles. It has been carried out by varying the upper cycle pressure ratio, the expansion pressure of the bottom cycle and using variable, above atmospheric, bottom cycle inlet pressure. Multi-objective genetic algorithms are used for Pareto approach optimization of the cycle outputs. The two important conflicting thermodynamic objectives that have been considered in this work are net specific work (w s ) and thermal efficiency (η th ). It is shown that some interesting features among optimal objective functions and decision variables involved in the Baryton and inverse Brayton cycles can be discovered consequently.

  8. Multi-objective optimization of a type of ellipse-parabola shaped superelastic flexure hinge

    Directory of Open Access Journals (Sweden)

    Z. Du

    2016-05-01

    Full Text Available Flexure hinges made of superelastic materials is a promising candidate to enhance the movability of compliant mechanisms. In this paper, we focus on the multi-objective optimization of a type of ellipse-parabola shaped superelastic flexure hinge. The objective is to determine a set of optimal geometric parameters that maximizes the motion range and the relative compliance of the flexure hinge and minimizes the relative rotation error during the deformation as well. Firstly, the paper presents a new type of ellipse-parabola shaped flexure hinge which is constructed by an ellipse arc and a parabola curve. Then, the static responses of superelastic flexure hinges are solved via non-prismatic beam elements derived by the co-rotational approach. Finite element analysis (FEA and experiment tests are performed to verify the modeling method. Finally, a multi-objective optimization is performed and the Pareto frontier is found via the NSGA-II algorithm.

  9. Multi-objective optimization problems concepts and self-adaptive parameters with mathematical and engineering applications

    CERN Document Server

    Lobato, Fran Sérgio

    2017-01-01

    This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.

  10. Intersection signal control multi-objective optimization based on genetic algorithm

    OpenAIRE

    Zhanhong Zhou; Ming Cai

    2014-01-01

    A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems arise recently, an intersection signal control optimization method which aims at reducing vehicle emissions, fuel consumption and vehicle delay is required heavily. This paper proposed a signal control multi-object optimization method to reduce vehicle emissions, fuel consumption and vehicle delay simultaneously at ...

  11. MULTI-OBJECTIVE OPTIMAL NUMBER AND LOCATION FOR STEEL OUTRIGGER-BELT TRUSS SYSTEM

    OpenAIRE

    MEHDI BABAEI

    2017-01-01

    During the past two decades, outrigger-belt truss system has been investigated and used in design of tall buildings. Most of the studies focused on the optimization of the system for minimum displacement and some of them proposed the best locations. In this study, however, multi-objective optimization of tall steel frames with belt trusses is investigated to minimize displacement and weight of the structure. For this purpose, structures with 20, 30, 40, and 50 stories are considered as ...

  12. Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

    Science.gov (United States)

    Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

    2016-01-01

    CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

  13. Localization of multilayer networks by optimized single-layer rewiring.

    Science.gov (United States)

    Jalan, Sarika; Pradhan, Priodyuti

    2018-04-01

    We study localization properties of principal eigenvectors (PEVs) of multilayer networks (MNs). Starting with a multilayer network corresponding to a delocalized PEV, we rewire the network edges using an optimization technique such that the PEV of the rewired multilayer network becomes more localized. The framework allows us to scrutinize structural and spectral properties of the networks at various localization points during the rewiring process. We show that rewiring only one layer is enough to attain a MN having a highly localized PEV. Our investigation reveals that a single edge rewiring of the optimized MN can lead to the complete delocalization of a highly localized PEV. This sensitivity in the localization behavior of PEVs is accompanied with the second largest eigenvalue lying very close to the largest one. This observation opens an avenue to gain a deeper insight into the origin of PEV localization of networks. Furthermore, analysis of multilayer networks constructed using real-world social and biological data shows that the localization properties of these real-world multilayer networks are in good agreement with the simulation results for the model multilayer network. This paper is relevant to applications that require understanding propagation of perturbation in multilayer networks.

  14. Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles

    International Nuclear Information System (INIS)

    Xu, Liangfei; Mueller, Clemens David; Li, Jianqiu; Ouyang, Minggao; Hu, Zunyan

    2015-01-01

    Highlights: • A non-linear model regarding fuel economy and system durability of FCEV. • A two-step algorithm for a quasi-optimal solution to a multi-objective problem. • Optimal parameters for DP algorithm considering accuracy and calculating time. • Influences of FC power and battery capacity on system performance. - Abstract: A typical topology of a proton electrolyte membrane (PEM) fuel cell electric vehicle contains at least two power sources, a fuel cell system (FCS) and a lithium battery package. The FCS provides stationary power, and the battery delivers dynamic power. In this paper, we report on the multi-objective optimization problem of powertrain parameters for a pre-defined driving cycle regarding fuel economy and system durability. We introduce the dynamic model for the FCEV. We take into consideration equations not only for fuel economy but also for system durability. In addition, we define a multi-objective optimization problem, and find a quasi-optimal solution using a two-loop framework. In the inside loop, for each group of powertrain parameters, a global optimal energy management strategy based on dynamic programming (DP) is exploited. We optimize coefficients for the DP algorithm to reduce calculating time as well as to maintain accuracy. For the outside loop, we compare the results of all the groups with each other, and choose the Pareto optimal solution based on a compromise of fuel economy and system durability. Simulation results show that for a “China city bus typical cycle,” a battery capacity of 150 Ah and an FCS maximal net output power of 40 kW are optimal for the fuel economy and system durability of a fuel cell city bus.

  15. An approach for multi-objective optimization of vehicle suspension system

    Science.gov (United States)

    Koulocheris, D.; Papaioannou, G.; Christodoulou, D.

    2017-10-01

    In this paper, a half car model of with nonlinear suspension systems is selected in order to study the vertical vibrations and optimize its suspension system with respect to ride comfort and road holding. A road bump was used as road profile. At first, the optimization problem is solved with the use of Genetic Algorithms with respect to 6 optimization targets. Then the k - ɛ optimization method was implemented to locate one optimum solution. Furthermore, an alternative approach is presented in this work: the previous optimization targets are separated in main and supplementary ones, depending on their importance in the analysis. The supplementary targets are not crucial to the optimization but they could enhance the main objectives. Thus, the problem was solved again using Genetic Algorithms with respect to the 3 main targets of the optimization. Having obtained the Pareto set of solutions, the k - ɛ optimality method was implemented for the 3 main targets and the supplementary ones, evaluated by the simulation of the vehicle model. The results of both cases are presented and discussed in terms of convergence of the optimization and computational time. The optimum solutions acquired from both cases are compared based on performance metrics as well.

  16. Energy quality management for building clusters and districts (BCDs) through multi-objective optimization

    International Nuclear Information System (INIS)

    Lu, Hai; Alanne, Kari; Martinac, Ivo

    2014-01-01

    Highlights: • Energy quality management is applied from individual building to district. • A novel time-effective multi-objective design optimization scheme is proposed. • The scheme searches for exergy efficient and environmental solution for districts. • System reliability is considered and addressed in this paper. - Abstract: Renewable energy systems entail a significant potential to meet the energy requirements of building clusters and districts (BCDs) provided that local energy sources are exploited efficiently. Besides improving the energy efficiency by reducing energy consumption and improving the match between energy supply and demand, energy quality issues have become a key topic of interest. Energy quality management is a technique that aims at optimally utilizing the exergy content of various renewable energy sources. In addition to minimizing life-cycle CO 2 emissions related to exergy losses of an energy system, issues such as system reliability should be addressed. The present work contributes to the research by proposing a novel multi-objective design optimization scheme that minimizes the global warming potential during the life-cycle and maximizes the exergy performance, while the maximum allowable level of the loss of power supply probability (LPSP) is predefined by the user as a constraint. The optimization makes use of Genetic Algorithm (GA). Finally, a case study is presented, where the above methodology has been applied to an office BCD located in Norway. The proposed optimization scheme is proven to be efficient in finding the optimal design and can be easily enlarged to encompass more relevant objective functions

  17. Multi-objective Optimization of Pulsed Gas Metal Arc Welding Process Using Neuro NSGA-II

    Science.gov (United States)

    Pal, Kamal; Pal, Surjya K.

    2018-05-01

    Weld quality is a critical issue in fabrication industries where products are custom-designed. Multi-objective optimization results number of solutions in the pareto-optimal front. Mathematical regression model based optimization methods are often found to be inadequate for highly non-linear arc welding processes. Thus, various global evolutionary approaches like artificial neural network, genetic algorithm (GA) have been developed. The present work attempts with elitist non-dominated sorting GA (NSGA-II) for optimization of pulsed gas metal arc welding process using back propagation neural network (BPNN) based weld quality feature models. The primary objective to maintain butt joint weld quality is the maximization of tensile strength with minimum plate distortion. BPNN has been used to compute the fitness of each solution after adequate training, whereas NSGA-II algorithm generates the optimum solutions for two conflicting objectives. Welding experiments have been conducted on low carbon steel using response surface methodology. The pareto-optimal front with three ranked solutions after 20th generations was considered as the best without further improvement. The joint strength as well as transverse shrinkage was found to be drastically improved over the design of experimental results as per validated pareto-optimal solutions obtained.

  18. Evolutionary Bi-objective Optimization for Bulldozer and Its Blade in Soil Cutting

    Science.gov (United States)

    Sharma, Deepak; Barakat, Nada

    2018-02-01

    An evolutionary optimization approach is adopted in this paper for simultaneously achieving the economic and productive soil cutting. The economic aspect is defined by minimizing the power requirement from the bulldozer, and the soil cutting is made productive by minimizing the time of soil cutting. For determining the power requirement, two force models are adopted from the literature to quantify the cutting force on the blade. Three domain-specific constraints are also proposed, which are limiting the power from the bulldozer, limiting the maximum force on the bulldozer blade and achieving the desired production rate. The bi-objective optimization problem is solved using five benchmark multi-objective evolutionary algorithms and one classical optimization technique using the ɛ-constraint method. The Pareto-optimal solutions are obtained with the knee-region. Further, the post-optimal analysis is performed on the obtained solutions to decipher relationships among the objectives and decision variables. Such relationships are later used for making guidelines for selecting the optimal set of input parameters. The obtained results are then compared with the experiment results from the literature that show a close agreement among them.

  19. Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant

    International Nuclear Information System (INIS)

    Liu, Xingrang; Bansal, R.C.

    2014-01-01

    Highlights: • A coal fired power plant boiler combustion process model based on real data. • We propose multi-objective optimization with CFD to optimize boiler combustion. • The proposed method uses software CORBA C++ and ANSYS Fluent 14.5 with AI. • It optimizes heat flux transfers and maintains temperature to avoid ash melt. - Abstract: The dominant role of electricity generation and environment consideration have placed strong requirements on coal fired power plants, requiring them to improve boiler combustion efficiency and decrease carbon emission. Although neural network based optimization strategies are often applied to improve the coal fired power plant boiler efficiency, they are limited by some combustion related problems such as slagging. Slagging can seriously influence heat transfer rate and decrease the boiler efficiency. In addition, it is difficult to measure slag build-up. The lack of measurement for slagging can restrict conventional neural network based coal fired boiler optimization, because no data can be used to train the neural network. This paper proposes a novel method of integrating non-dominated sorting genetic algorithm (NSGA II) based multi-objective optimization with computational fluid dynamics (CFD) to decrease or even avoid slagging inside a coal fired boiler furnace and improve boiler combustion efficiency. Compared with conventional neural network based boiler optimization methods, the method developed in the work can control and optimize the fields of flue gas properties such as temperature field inside a boiler by adjusting the temperature and velocity of primary and secondary air in coal fired power plant boiler control systems. The temperature in the vicinity of water wall tubes of a boiler can be maintained within the ash melting temperature limit. The incoming ash particles cannot melt and bond to surface of heat transfer equipment of a boiler. So the trend of slagging inside furnace is controlled. Furthermore, the

  20. Multi-objective optimization model of CNC machining to minimize processing time and environmental impact

    Science.gov (United States)

    Hamada, Aulia; Rosyidi, Cucuk Nur; Jauhari, Wakhid Ahmad

    2017-11-01

    Minimizing processing time in a production system can increase the efficiency of a manufacturing company. Processing time are influenced by application of modern technology and machining parameter. Application of modern technology can be apply by use of CNC machining, one of the machining process can be done with a CNC machining is turning. However, the machining parameters not only affect the processing time but also affect the environmental impact. Hence, optimization model is needed to optimize the machining parameters to minimize the processing time and environmental impact. This research developed a multi-objective optimization to minimize the processing time and environmental impact in CNC turning process which will result in optimal decision variables of cutting speed and feed rate. Environmental impact is converted from environmental burden through the use of eco-indicator 99. The model were solved by using OptQuest optimization software from Oracle Crystal Ball.

  1. Label-free, single-object sensing with a microring resonator: FDTD simulation.

    Science.gov (United States)

    Nguyen, Dan T; Norwood, Robert A

    2013-01-14

    Label-free, single-object sensing with a microring resonator is investigated numerically using the finite difference time-domain (FDTD) method. A pulse with ultra-wide bandwidth that spans over several resonant modes of the ring and of the sensing object is used for simulation, enabling a single-shot simulation of the microring sensing. The FDTD simulation not only can describe the circulation of the light in a whispering-gallery-mode (WGM) microring and multiple interactions between the light and the sensing object, but also other important factors of the sensing system, such as scattering and radiation losses. The FDTD results show that the simulation can yield a resonant shift of the WGM cavity modes. Furthermore, it can also extract eigenmodes of the sensing object, and therefore information from deep inside the object. The simulation method is not only suitable for a single object (single molecule, nano-, micro-scale particle) but can be extended to the problem of multiple objects as well.

  2. Considering Decision Variable Diversity in Multi-Objective Optimization: Application in Hydrologic Model Calibration

    Science.gov (United States)

    Sahraei, S.; Asadzadeh, M.

    2017-12-01

    Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.

  3. An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

    Directory of Open Access Journals (Sweden)

    Rafał Dreżewski

    2017-08-01

    Full Text Available Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

  4. Multi-objective optimization of the management of a waterworks using an integrated well field model

    DEFF Research Database (Denmark)

    Hansen, Annette Kirstine; Bauer-Gottwein, Peter; Rosbjerg, Dan

    2012-01-01

    of predicting the water level and the energy consumption of the individual production wells. The model has been applied to Søndersø waterworks in Denmark, where it predicts the energy consumption within 1.8% of the observed. The objectives of the optimization problem are to minimize the specific energy...... provides the decision-makers with compromise solutions between the two competing objectives. In the test case the Pareto optimal solutions are compared with an exhaustive benchmark solution. It is shown that the energy consumption can be reduced by 4% by changing the pumping configuration without violating...

  5. Fuzzy preference based interactive fuzzy physical programming and its application in multi-objective optimization

    International Nuclear Information System (INIS)

    Zhang, Xu; Huang, Hong Zhong; Yu, Lanfeng

    2006-01-01

    Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer

  6. Image de-noising based on mathematical morphology and multi-objective particle swarm optimization

    Science.gov (United States)

    Dou, Liyun; Xu, Dan; Chen, Hao; Liu, Yicheng

    2017-07-01

    To overcome the problem of image de-noising, an efficient image de-noising approach based on mathematical morphology and multi-objective particle swarm optimization (MOPSO) is proposed in this paper. Firstly, constructing a series and parallel compound morphology filter based on open-close (OC) operation and selecting a structural element with different sizes try best to eliminate all noise in a series link. Then, combining multi-objective particle swarm optimization (MOPSO) to solve the parameters setting of multiple structural element. Simulation result shows that our algorithm can achieve a superior performance compared with some traditional de-noising algorithm.

  7. Multi-objective genetic algorithm based innovative wind farm layout optimization method

    International Nuclear Information System (INIS)

    Chen, Ying; Li, Hua; He, Bang; Wang, Pengcheng; Jin, Kai

    2015-01-01

    Highlights: • Innovative optimization procedures for both regular and irregular shape wind farm. • Using real wind condition and commercial wind turbine parameters. • Using multiple-objective genetic algorithm optimization method. • Optimize the selection of different wind turbine types and their hub heights. - Abstract: Layout optimization has become one of the critical approaches to increase power output and decrease total cost of a wind farm. Previous researches have applied intelligent algorithms to optimizing the wind farm layout. However, those wind conditions used in most of previous research are simplified and not accurate enough to match the real world wind conditions. In this paper, the authors propose an innovative optimization method based on multi-objective genetic algorithm, and test it with real wind condition and commercial wind turbine parameters. Four case studies are conducted to investigate the number of wind turbines needed in the given wind farm. Different cost models are also considered in the case studies. The results clearly demonstrate that the new method is able to optimize the layout of a given wind farm with real commercial data and wind conditions in both regular and irregular shapes, and achieve a better result by selecting different type and hub height wind turbines.

  8. Constructal design of a blast furnace iron-making process based on multi-objective optimization

    International Nuclear Information System (INIS)

    Liu, Xiong; Chen, Lingen; Feng, Huijun; Qin, Xiaoyong; Sun, Fengrui

    2016-01-01

    For the fixed total raw material cost and based on constructal theory and finite time thermodynamics, a BFIM (blast furnace iron-making) process is optimized by taking a complex function as optimization objective. The complex function is integrated with HM (hot metal) yield and useful energy of the BF (blast furnace). The optimal cost distribution of raw materials (namely “generalized optimal construct”) is obtained. The effects of some parameters, such as oxygen enrichment, blast temperature and pulverized coal dosage, on the optimization results are analyzed. The results show that the HM yield, useful energy and complex function are, respectively, increased by 3.13%, 2.66% and 2.90% after generalized constructal optimization. The utilization efficiencies of the BFG (blast furnace gas) and slag are 41.3% and 57.1%, respectively, which means that the utilization potentials of the BFG and slag can be further exploited. Increasing pulverized coal dosage and decreasing the agglomerate ratio can increase the complex function. The performance the BFIM process can be improved by adjusting the oxygen enrichment, blast temperature, blast dosage, pressure ratio of the Brayton cycle's air compressor and relative pressure drop of the air compressor inlet to their optimal values, respectively, which are new findings of this paper. - Highlights: • Constructal optimization of a blast furnace iron-making process is performed. • Finite time thermodynamic model of open Brayton cycle is adopted. • Weighting function is taken as optimization objective. • Optimal cost distribution of the raw materials is obtained.

  9. A comprehensive, multi-objective optimization of solar-powered absorption chiller systems for air-conditioning applications

    International Nuclear Information System (INIS)

    Shirazi, Ali; Taylor, Robert A.; Morrison, Graham L.; White, Stephen D.

    2017-01-01

    Highlights: • Multi-objective optimization of solar single/multi-effect absorption chillers was conducted. • Primary energy consumption and total annual cost were considered as the objectives. • Optimized designs of the alternative configurations were compared. • A detailed sensitivity analysis of the Pareto optimal solutions was investigated. - Abstract: Solar heating and cooling (SHC) systems are currently under rapid development and deployment due to their potential to reduce the use of fossil fuel resources and to alleviate greenhouse gas emissions in the building sector – a sector which is responsible for ∼40% of the world energy use. Absorption chiller technology (traditionally powered by natural gas in large buildings), can easily be retrofitted to run on solar energy. However, numerous non-intuitive design choices must be analyzed to achieve the best techno-economic performance of these systems. To date, there has been little research into the optimal configurations among the long list of potential solar-driven absorption chiller systems. To address this lack of knowledge, this paper presents a systematic simulation-based, multi-objective optimization of three common, commercially available lithium bromide-water absorption chillers – single-effect, double-effect and triple-effect – powered by evacuated tube collectors (ETCs), evacuated flat plate collectors (EFPCs), and concentrating parabolic trough collectors (PTCs), respectively. To the best of authors’ knowledge, this is the first study of its kind that compares the optimized designs of the most promising configurations of solar-assisted absorption chillers against a common set of energy, economic, and environmental metrics from a holistic perspective. A simulation model of these three configurations is developed using TRNSYS 17. A combined energy, economic, and environmental analysis of the modeled systems is conducted to calculate the primary energy use as well as the levelized total

  10. Multiobjective Optimization Modeling Approach for Multipurpose Single Reservoir Operation

    Directory of Open Access Journals (Sweden)

    Iosvany Recio Villa

    2018-04-01

    Full Text Available The water resources planning and management discipline recognizes the importance of a reservoir’s carryover storage. However, mathematical models for reservoir operation that include carryover storage are scarce. This paper presents a novel multiobjective optimization modeling framework that uses the constraint-ε method and genetic algorithms as optimization techniques for the operation of multipurpose simple reservoirs, including carryover storage. The carryover storage was conceived by modifying Kritsky and Menkel’s method for reservoir design at the operational stage. The main objective function minimizes the cost of the total annual water shortage for irrigation areas connected to a reservoir, while the secondary one maximizes its energy production. The model includes operational constraints for the reservoir, Kritsky and Menkel’s method, irrigation areas, and the hydropower plant. The study is applied to Carlos Manuel de Céspedes reservoir, establishing a 12-month planning horizon and an annual reliability of 75%. The results highly demonstrate the applicability of the model, obtaining monthly releases from the reservoir that include the carryover storage, degree of reservoir inflow regulation, water shortages in irrigation areas, and the energy generated by the hydroelectric plant. The main product is an operational graph that includes zones as well as rule and guide curves, which are used as triggers for long-term reservoir operation.

  11. Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

    Science.gov (United States)

    Omagari, Hiroki; Higashino, Shin-Ichiro

    2018-04-01

    In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

  12. Flexible aluminum tubes and a least square multi-objective non-linear optimization scheme

    International Nuclear Information System (INIS)

    Endelt, Benny; Nielsen, Karl Brian; Olsen, Soeren

    2004-01-01

    The automotive industry currently uses rubber hoses as the media carrier between e.g. the radiator and the engine, and the basic idea is to replace the rubber hoses with flexible aluminum tubes.A good quality is defined through several quality measurements, i.e. in the current case the key objective is to produce a flexible convolution through optimization of the tool geometry, but the process should also be stable, and the process stability is evaluated through Forming Limit Diagrams. Typically the defined objectives are conflicting, i.e. the optimized configuration represents therefore a trade-off between the individual objectives, in this case flexibility versus process stability.The optimization problem is solved through iteratively minimizing the object function. A second-order least square scheme is used for the approximation of the quadratic model, and the change in the design parameters is evaluated through the trust region scheme and box constraints are introduced within the trust region framework. Furthermore, the object function is minimized by applying the non-monotone scheme, and the trust region subproblem is solved by applying the Cholesky factorization scheme.An optimal bell shaped geometry is identified and the design is verified experimentally

  13. Multi-objective superstructure-free synthesis and optimization of thermal power plants

    International Nuclear Information System (INIS)

    Wang, Ligang; Lampe, Matthias; Voll, Philip; Yang, Yongping; Bardow, André

    2016-01-01

    The merits of superstructure-free synthesis are demonstrated for bi-objective design of thermal power plants. The design of thermal power plants is complex and thus best solved by optimization. Common optimization methods require specification of a superstructure which becomes a tedious and error-prone task for complex systems. Superstructure specification is avoided by the presented superstructure-free approach, which is shown to successfully solve the design task yielding a high-quality Pareto front of promising structural alternatives. The economic objective function avoids introducing infinite numbers of units (e.g., turbine, reheater and feedwater preheater) as favored by pure thermodynamic optimization. The number of feasible solutions found per number of mutation tries is still high even after many generations but declines after introducing highly-nonlinear cost functions leading to challenging MINLP problems. The identified Pareto-optimal solutions tend to employ more units than found in modern power plants indicating the need for cost functions to reflect current industrial practice. In summary, the multi-objective superstructure-free synthesis framework is a robust approach for very complex problems in the synthesis of thermal power plants. - Highlights: • A generalized multi-objective superstructure-free synthesis framework for thermal power plants is presented. • The superstructure-free synthesis framework is comprehensively evaluated by complex bi-objective synthesis problems. • The proposed framework is effective to explore the structural design space even for complex problems.

  14. Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine

    Directory of Open Access Journals (Sweden)

    Bambang Wahono

    2014-01-01

    Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.

  15. Multi-objective portfolio optimization of mutual funds under downside risk measure using fuzzy theory

    OpenAIRE

    M. Amiri; M. Zandieh; A. Alimi

    2012-01-01

    Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnove...

  16. Objective lens simultaneously optimized for pupil ghosting, wavefront delivery and pupil imaging

    Science.gov (United States)

    Olczak, Eugene G (Inventor)

    2011-01-01

    An objective lens includes multiple optical elements disposed between a first end and a second end, each optical element oriented along an optical axis. Each optical surface of the multiple optical elements provides an angle of incidence to a marginal ray that is above a minimum threshold angle. This threshold angle minimizes pupil ghosts that may enter an interferometer. The objective lens also optimizes wavefront delivery and pupil imaging onto an optical surface under test.

  17. Multi-objective room acoustic optimization of timber folded plate structure

    DEFF Research Database (Denmark)

    Skov, Rasmus; Parigi, Dario; Damkilde, Lars

    2017-01-01

    This paper investigates the application of multi-objective optimization in the design of timber folded plate structures in the scope of the architectural design process. Considering contrasting objectives of structural displacement, early decay time (EDT), clarity (C50) and sound strength (G......), the methodology applied in two benchmarks tests, encompasses both structural and acoustic performance when determining folding characteristics and directionality of surfaces in a timber folded plate structure....

  18. Graphical user interface to optimize image contrast parameters used in object segmentation - biomed 2009.

    Science.gov (United States)

    Anderson, Jeffrey R; Barrett, Steven F

    2009-01-01

    Image segmentation is the process of isolating distinct objects within an image. Computer algorithms have been developed to aid in the process of object segmentation, but a completely autonomous segmentation algorithm has yet to be developed [1]. This is because computers do not have the capability to understand images and recognize complex objects within the image. However, computer segmentation methods [2], requiring user input, have been developed to quickly segment objects in serial sectioned images, such as magnetic resonance images (MRI) and confocal laser scanning microscope (CLSM) images. In these cases, the segmentation process becomes a powerful tool in visualizing the 3D nature of an object. The user input is an important part of improving the performance of many segmentation methods. A double threshold segmentation method has been investigated [3] to separate objects in gray scaled images, where the gray level of the object is among the gray levels of the background. In order to best determine the threshold values for this segmentation method the image must be manipulated for optimal contrast. The same is true of other segmentation and edge detection methods as well. Typically, the better the image contrast, the better the segmentation results. This paper describes a graphical user interface (GUI) that allows the user to easily change image contrast parameters that will optimize the performance of subsequent object segmentation. This approach makes use of the fact that the human brain is extremely effective in object recognition and understanding. The GUI provides the user with the ability to define the gray scale range of the object of interest. These lower and upper bounds of this range are used in a histogram stretching process to improve image contrast. Also, the user can interactively modify the gamma correction factor that provides a non-linear distribution of gray scale values, while observing the corresponding changes to the image. This

  19. Sensitivity of Calibrated Parameters and Water Resource Estimates on Different Objective Functions and Optimization Algorithms

    Directory of Open Access Journals (Sweden)

    Delaram Houshmand Kouchi

    2017-05-01

    Full Text Available The successful application of hydrological models relies on careful calibration and uncertainty analysis. However, there are many different calibration/uncertainty analysis algorithms, and each could be run with different objective functions. In this paper, we highlight the fact that each combination of optimization algorithm-objective functions may lead to a different set of optimum parameters, while having the same performance; this makes the interpretation of dominant hydrological processes in a watershed highly uncertain. We used three different optimization algorithms (SUFI-2, GLUE, and PSO, and eight different objective functions (R2, bR2, NSE, MNS, RSR, SSQR, KGE, and PBIAS in a SWAT model to calibrate the monthly discharges in two watersheds in Iran. The results show that all three algorithms, using the same objective function, produced acceptable calibration results; however, with significantly different parameter ranges. Similarly, an algorithm using different objective functions also produced acceptable calibration results, but with different parameter ranges. The different calibrated parameter ranges consequently resulted in significantly different water resource estimates. Hence, the parameters and the outputs that they produce in a calibrated model are “conditioned” on the choices of the optimization algorithm and objective function. This adds another level of non-negligible uncertainty to watershed models, calling for more attention and investigation in this area.

  20. A Generalized Decision Framework Using Multi-objective Optimization for Water Resources Planning

    Science.gov (United States)

    Basdekas, L.; Stewart, N.; Triana, E.

    2013-12-01

    Colorado Springs Utilities (CSU) is currently engaged in an Integrated Water Resource Plan (IWRP) to address the complex planning scenarios, across multiple time scales, currently faced by CSU. The modeling framework developed for the IWRP uses a flexible data-centered Decision Support System (DSS) with a MODSIM-based modeling system to represent the operation of the current CSU raw water system coupled with a state-of-the-art multi-objective optimization algorithm. Three basic components are required for the framework, which can be implemented for planning horizons ranging from seasonal to interdecadal. First, a water resources system model is required that is capable of reasonable system simulation to resolve performance metrics at the appropriate temporal and spatial scales of interest. The system model should be an existing simulation model, or one developed during the planning process with stakeholders, so that 'buy-in' has already been achieved. Second, a hydrologic scenario tool(s) capable of generating a range of plausible inflows for the planning period of interest is required. This may include paleo informed or climate change informed sequences. Third, a multi-objective optimization model that can be wrapped around the system simulation model is required. The new generation of multi-objective optimization models do not require parameterization which greatly reduces problem complexity. Bridging the gap between research and practice will be evident as we use a case study from CSU's planning process to demonstrate this framework with specific competing water management objectives. Careful formulation of objective functions, choice of decision variables, and system constraints will be discussed. Rather than treating results as theoretically Pareto optimal in a planning process, we use the powerful multi-objective optimization models as tools to more efficiently and effectively move out of the inferior decision space. The use of this framework will help CSU

  1. Design of a Fractional Order Frequency PID Controller for an Islanded Microgrid: A Multi-Objective Extremal Optimization Method

    Directory of Open Access Journals (Sweden)

    Huan Wang

    2017-10-01

    Full Text Available Fractional order proportional-integral-derivative(FOPID controllers have attracted increasing attentions recently due to their better control performance than the traditional integer-order proportional-integral-derivative (PID controllers. However, there are only few studies concerning the fractional order control of microgrids based on evolutionary algorithms. From the perspective of multi-objective optimization, this paper presents an effective FOPID based frequency controller design method called MOEO-FOPID for an islanded microgrid by using a Multi-objective extremal optimization (MOEO algorithm to minimize frequency deviation and controller output signal simultaneously in order to improve finally the efficient operation of distributed generations and energy storage devices. Its superiority to nondominated sorting genetic algorithm-II (NSGA-II based FOPID/PID controllers and other recently reported single-objective evolutionary algorithms such as Kriging-based surrogate modeling and real-coded population extremal optimization-based FOPID controllers is demonstrated by the simulation studies on a typical islanded microgrid in terms of the control performance including frequency deviation, deficit grid power, controller output signal and robustness.

  2. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

    Directory of Open Access Journals (Sweden)

    Maryam Mousavi

    Full Text Available Flexible manufacturing system (FMS enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs. An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA, particle swarm optimization (PSO, and hybrid GA-PSO to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

  3. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

    Science.gov (United States)

    Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

    2017-01-01

    Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

  4. Investigation of trunk muscle activities during lifting using a multi-objective optimization-based model and intelligent optimization algorithms.

    Science.gov (United States)

    Ghiasi, Mohammad Sadegh; Arjmand, Navid; Boroushaki, Mehrdad; Farahmand, Farzam

    2016-03-01

    A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data.

  5. Multi-Objective Distribution Network Operation Based on Distributed Generation Optimal Placement Using New Antlion Optimizer Considering Reliability

    Directory of Open Access Journals (Sweden)

    KHANBABAZADEH Javad

    2016-10-01

    Full Text Available Distribution network designers and operators are trying to deliver electrical energy with high reliability and quality to their subscribers. Due to high losses in the distribution systems, using distributed generation can improves reliability, reduces losses and improves voltage profile of distribution network. Therefore, the choice of the location of these resources and also determining the amount of their generated power to maximize the benefits of this type of resource is an important issue which is discussed from different points of view today. In this paper, a new multi-objective optimal location and sizing of distributed generation resources is performed to maximize its benefits on the 33 bus distribution test network considering reliability and using a new Antlion Optimizer (ALO. The benefits for DG are considered as system losses reduction, system reliability improvement and benefits from the sale electricity and voltage profile improvement. For each of the mentioned benefits, the ALO algorithm is used to optimize the location and sizing of distributed generation resources. In order to verify the proposed approach, the obtained results have been analyzed and compared with the results of particle swarm optimization (PSO algorithm. The results show that the ALO has shown better performance in optimization problem solution versus PSO.

  6. High Fidelity Multi-Objective Design Optimization of a Downscaled Cusped Field Thruster

    Directory of Open Access Journals (Sweden)

    Thomas Fahey

    2017-11-01

    Full Text Available The Cusped Field Thruster (CFT concept has demonstrated significantly improved performance over the Hall Effect Thruster and the Gridded Ion Thruster; however, little is understood about the complexities of the interactions and interdependencies of the geometrical, magnetic and ion beam properties of the thruster. This study applies an advanced design methodology combining a modified power distribution calculation and evolutionary algorithms assisted by surrogate modeling to a multi-objective design optimization for the performance optimization and characterization of the CFT. Optimization is performed for maximization of performance defined by five design parameters (i.e., anode voltage, anode current, mass flow rate, and magnet radii, simultaneously aiming to maximize three objectives; that is, thrust, efficiency and specific impulse. Statistical methods based on global sensitivity analysis are employed to assess the optimization results in conjunction with surrogate models to identify key design factors with respect to the three design objectives and additional performance measures. The research indicates that the anode current and the Outer Magnet Radius have the greatest effect on the performance parameters. An optimal value for the anode current is determined, and a trend towards maximizing anode potential and mass flow rate is observed.

  7. Multi-objective optimization of Stirling engine using Finite Physical Dimensions Thermodynamics (FPDT) method

    International Nuclear Information System (INIS)

    Li, Ruijie; Grosu, Lavinia; Queiros-Conde, Diogo

    2016-01-01

    Highlights: • A gamma Stirling engine has been optimized using FPDT method by multi-objective criteria. • Genetic algorithm and decision making methods were used to get Pareto frontier and optimum points. • It shows: total thermal conductance, hot temperature, stroke and diameter ratios can be improved. - Abstract: In this paper, a solar energy powered gamma type SE has been optimized using Finite Physical Dimensions Thermodynamics (FPDT) method by multi-objective criteria. Genetic algorithm was used to get the Pareto frontier, and optimum points were obtained using the decision making methods of LINMAP and TOPSIS. The optimization results have been compared with those obtained using the ecological method. It was shown that the multi-objective optimization in this paper has a better balance among the optimizing criteria (maximum mechanical power, maximum thermal efficiency and minimum entropy generation flow). The effects of the hot source temperature and the total thermal conductance of the engine on the Pareto frontier have been also studied. This sensibility study shows that an increase in the hot reservoir temperature can increase the output mechanical power, the thermal efficiency of the engine, but also the entropy generation rate. In addition to this, an increase of the total thermal conductance of the engine can strongly increase the output mechanical power and only slightly increase the thermal efficiency. These results allow us to improve the engine performance after some modifications as geometrical dimensions (diameter, stroke, heat exchange surface, etc.) and physical parameters (temperature, thermal conductivity).

  8. Systematic analysis of the heat exchanger arrangement problem using multi-objective genetic optimization

    International Nuclear Information System (INIS)

    Daróczy, László; Janiga, Gábor; Thévenin, Dominique

    2014-01-01

    A two-dimensional cross-flow tube bank heat exchanger arrangement problem with internal laminar flow is considered in this work. The objective is to optimize the arrangement of tubes and find the most favorable geometries, in order to simultaneously maximize the rate of heat exchange while obtaining a minimum pressure loss. A systematic study was performed involving a large number of simulations. The global optimization method NSGA-II was retained. A fully automatized in-house optimization environment was used to solve the problem, including mesh generation and CFD (computational fluid dynamics) simulations. The optimization was performed in parallel on a Linux cluster with a very good speed-up. The main purpose of this article is to illustrate and analyze a heat exchanger arrangement problem in its most general form and to provide a fundamental understanding of the structure of the Pareto front and optimal geometries. The considered conditions are particularly suited for low-power applications, as found in a growing number of practical systems in an effort toward increasing energy efficiency. For such a detailed analysis with more than 140 000 CFD-based evaluations, a design-of-experiment study involving a response surface would not be sufficient. Instead, all evaluations rely on a direct solution using a CFD solver. - Highlights: • Cross-flow tube bank heat exchanger arrangement problem. • A fully automatized multi-objective optimization based on genetic algorithm. • A systematic study involving a large number of CFD (computational fluid dynamics) simulations

  9. Multi-objective optimization and simulation model for the design of distributed energy systems

    International Nuclear Information System (INIS)

    Falke, Tobias; Krengel, Stefan; Meinerzhagen, Ann-Kathrin; Schnettler, Armin

    2016-01-01

    Highlights: • Development of a model for the optimal design of district energy systems. • Multi-objective approach: integrated economic and ecological optimization. • Consideration of conventional conversion technologies, RES and district heating. • Decomposition of optimization problem to reduce computation complexity. • Approach enables the investigation of districts with more than 150 buildings. - Abstract: In this paper, a multi-objective optimization model for the investment planning and operation management of distributed heat and electricity supply systems is presented. Different energy efficiency measures and supply options are taken into account, including various distributed heat and power generation units, storage systems and energy-saving renovation measures. Furthermore, district heating networks are considered as an alternative to conventional, individual heat supply for each building. The optimization problem is decomposed into three subproblems to reduce the computational complexity. This enables a high level of detail in the optimization and simultaneously the comprehensive investigation of districts with more than 100 buildings. These capabilities distinguish the model from previous approaches in this field of research. The developed model is applied to a district in a medium-sized town in Germany in order to analyze the effects of different efficiency measures regarding total costs and emissions of CO 2 equivalents. Based on the Pareto efficient solutions, technologies and efficiency measures that can contribute most efficiently to reduce greenhouse gas emissions are identified.

  10. Multi-objective optimization of GPU3 Stirling engine using third order analysis

    International Nuclear Information System (INIS)

    Toghyani, Somayeh; Kasaeian, Alibakhsh; Hashemabadi, Seyyed Hasan; Salimi, Morteza

    2014-01-01

    Highlights: • A third-order analysis is carried out for optimization of Stirling engine. • The triple-optimization is done on a GPU3 Stirling engine. • A multi-objective optimization is carried out for a Stirling engine. • The results are compared with an experimental previous work for checking the model improvement. • The methods of TOPSIS, Fuzzy, and LINMAP are compared with each other in aspect of optimization. - Abstract: Stirling engine is an external combustion engine that uses any external heat source to generate mechanical power which operates at closed cycles. These engines are good choices for using in power generation systems; because these engines present a reasonable theoretical efficiency which can be closer to the Carnot efficiency, comparing with other reciprocating thermal engines. Hence, many studies have been conducted on Stirling engines and the third order thermodynamic analysis is one of them. In this study, multi-objective optimization with four decision variables including the temperature of heat source, stroke, mean effective pressure, and the engine frequency were applied in order to increase the efficiency and output power and reduce the pressure drop. Three decision-making procedures were applied to optimize the answers from the results. At last, the applied methods were compared with the results obtained of one experimental work and a good agreement was observed

  11. The multi-objective genetic algorithm optimization, of a superplastic forming process, using ansys®

    Directory of Open Access Journals (Sweden)

    Grebenişan Gavril

    2017-01-01

    Full Text Available In the industrial practice, the product is intended to be flawless, with no technological difficulty in making the profile shapes. If this product results without defects, then any Finite Elements Method (FEM based simulation can support that technology. A technology engineer does not propose, very often to analyze the simulation of the design technology, but rather to try to optimize a solution that he feels feasible. Experiments used as the basis for numerical optimization analysis support their research in the field of superplastic forming. Determining the influence of input parameters on the output parameters, Determining the optimal shape of the product and the optimal initial geometry, the prediction of the cracks and possibly the fractures, the prediction of the final thickness of the sheet, these are the objectives of the research and optimization for this project. The results of the numerical simulations have been compared with the measurements made on parts and sections of the parts obtained by superplastic forming. Of course, the consistency of the results, costs, benefits, and times required to perform numerical simulations are evaluated, but they are not objectives for optimizing the superplastic forming process.

  12. Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

    Science.gov (United States)

    Gong, W.; Duan, Q.; Huo, X.

    2017-12-01

    Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

  13. Ensemble-based hierarchical multi-objective production optimization of smart wells

    NARCIS (Netherlands)

    Fonseca, R.M.; Leeuwenburgh, O.; Van den Hof, P.M.J.; Jansen, J.D.

    2014-01-01

    In an earlier study two hierarchical multi-objective methods were suggested to include short-term targets in life-cycle production optimization. However this earlier study has two limitations: 1) the adjoint formulation is used to obtain gradient information, requiring simulator source code access

  14. Multi-objective optimization of riparian buffer networks; valuing present and future benefits

    Science.gov (United States)

    Multi-objective optimization has emerged as a popular approach to support water resources planning and management. This approach provides decision-makers with a suite of management options which are generated based on metrics that represent different social, economic, and environ...

  15. Analysis of Various Multi-Objective Optimization Evolutionary Algorithms for Monte Carlo Treatment Planning System

    CERN Document Server

    Tydrichova, Magdalena

    2017-01-01

    In this project, various available multi-objective optimization evolutionary algorithms were compared considering their performance and distribution of solutions. The main goal was to select the most suitable algorithms for applications in cancer hadron therapy planning. For our purposes, a complex testing and analysis software was developed. Also, many conclusions and hypothesis have been done for the further research.

  16. Design of homo-organic acid producing strains using multi-objective optimization

    DEFF Research Database (Denmark)

    Kim, Tae Yong; Park, Jong Myoung; Kim, Hyun Uk

    2015-01-01

    Production of homo-organic acids without byproducts is an important challenge in bioprocess engineering to minimize operation cost for separation processes. In this study, we used multi-objective optimization to design Escherichia coli strains with the goals of maximally producing target organic ...

  17. Robust optimization of robotic pick and place operations for deformable objects through simulation

    DEFF Research Database (Denmark)

    Bo Jorgensen, Troels; Debrabant, Kristian; Kruger, Norbert

    2016-01-01

    for the task. The solutions are parameterized in terms of the robot motion and the gripper configuration, and after each simulation various objective scores are determined and combined. This enables the use of various optimization strategies. Based on visual inspection of the most robust solution found...

  18. Localized probability of improvement for kriging based multi-objective optimization

    Science.gov (United States)

    Li, Yinjiang; Xiao, Song; Barba, Paolo Di; Rotaru, Mihai; Sykulski, Jan K.

    2017-12-01

    The paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

  19. Multi-objective Optimization of a Solar Humidification Dehumidification Desalination Unit

    Science.gov (United States)

    Rafigh, M.; Mirzaeian, M.; Najafi, B.; Rinaldi, F.; Marchesi, R.

    2017-11-01

    In the present paper, a humidification-dehumidification desalination unit integrated with solar system is considered. In the first step mathematical model of the whole plant is represented. Next, taking into account the logical constraints, the performance of the system is optimized. On one hand it is desired to have higher energetic efficiency, while on the other hand, higher efficiency results in an increment in the required area for each subsystem which consequently leads to an increase in the total cost of the plant. In the present work, the optimum solution is achieved when the specific energy of the solar heater and also the areas of humidifier and dehumidifier are minimized. Due to the fact that considered objective functions are in conflict, conventional optimization methods are not applicable. Hence, multi objective optimization using genetic algorithm which is an efficient tool for dealing with problems with conflicting objectives has been utilized and a set of optimal solutions called Pareto front each of which is a tradeoff between the mentioned objectives is generated.

  20. Methods for providing decision makers with optimal solutions for multiple objectives that change over time

    CSIR Research Space (South Africa)

    Greeff, M

    2010-09-01

    Full Text Available Decision making - with the goal of finding the optimal solution - is an important part of modern life. For example: In the control room of an airport, the goals or objectives are to minimise the risk of airplanes colliding, minimise the time that a...

  1. A Linear Programming Model to Optimize Various Objective Functions of a Foundation Type State Support Program.

    Science.gov (United States)

    Matzke, Orville R.

    The purpose of this study was to formulate a linear programming model to simulate a foundation type support program and to apply this model to a state support program for the public elementary and secondary school districts in the State of Iowa. The model was successful in producing optimal solutions to five objective functions proposed for…

  2. Optimization of Combined Thermal and Electrical Behavior of Power Converters Using Multi-Objective Genetic Algorithms

    NARCIS (Netherlands)

    Malyna, D.V.; Duarte, J.L.; Hendrix, M.A.M.; Horck, van F.B.M.

    2007-01-01

    A practical example of power electronic converter synthesis is presented, where a multi-objective genetic algorithm, namely non-dominated sorting genetic algorithm (NSGA-II) is used. The optimization algorithm takes an experimentally-derived thermal model for the converter into account. Experimental

  3. A Multi-objective Optimization Application in Friction Stir Welding: Considering Thermo-mechanical Aspects

    DEFF Research Database (Denmark)

    Tutum, Cem Celal; Hattel, Jesper Henri

    2010-01-01

    speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2-dimensional sequentially coupled thermomechanical...

  4. A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices

    International Nuclear Information System (INIS)

    Khoroshiltseva, Marina; Slanzi, Debora; Poli, Irene

    2016-01-01

    Highlights: • We present a multi-objective optimization algorithm for shading design. • We combine Harmony search and Pareto-based procedures. • Thermal and daylighting performances of external shading were considered. • We applied the optimization process to a residential social housing in Madrid. - Abstract: In this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions.

  5. Swarm intelligence for multi-objective optimization of synthesis gas production

    Science.gov (United States)

    Ganesan, T.; Vasant, P.; Elamvazuthi, I.; Ku Shaari, Ku Zilati

    2012-11-01

    In the chemical industry, the production of methanol, ammonia, hydrogen and higher hydrocarbons require synthesis gas (or syn gas). The main three syn gas production methods are carbon dioxide reforming (CRM), steam reforming (SRM) and partial-oxidation of methane (POM). In this work, multi-objective (MO) optimization of the combined CRM and POM was carried out. The empirical model and the MO problem formulation for this combined process were obtained from previous works. The central objectives considered in this problem are methane conversion, carbon monoxide selectivity and the hydrogen to carbon monoxide ratio. The MO nature of the problem was tackled using the Normal Boundary Intersection (NBI) method. Two techniques (Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO)) were then applied in conjunction with the NBI method. The performance of the two algorithms and the quality of the solutions were gauged by using two performance metrics. Comparative studies and results analysis were then carried out on the optimization results.

  6. Dual-mode nested search method for categorical uncertain multi-objective optimization

    Science.gov (United States)

    Tang, Long; Wang, Hu

    2016-10-01

    Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

  7. Multi-objective optimization of a cascade refrigeration system: Exergetic, economic, environmental, and inherent safety analysis

    International Nuclear Information System (INIS)

    Eini, Saeed; Shahhosseini, Hamidreza; Delgarm, Navid; Lee, Moonyong; Bahadori, Alireza

    2016-01-01

    Highlights: • A multi-objective optimization is performed for a cascade refrigeration cycle. • The optimization problem considers inherently safe design as well as 3E analysis. • As a measure of inherent safety level a quantitative risk analysis is utilized. • A CO 2 /NH 3 cascade refrigeration system is compared with a CO 2 /C 3 H 8 system. - Abstract: Inherently safer design is the new approach to maximize the overall safety of a process plant. This approach suggests some risk reduction strategies to be implemented in the early stages of design. In this paper a multi-objective optimization was performed considering economic, exergetic, and environmental aspects besides evaluation of the inherent safety level of a cascade refrigeration system. The capital costs, the processing costs, and the social cost due to CO 2 emission were considered to be included in the economic objective function. Exergetic efficiency of the plant was considered as the second objective function. As a measure of inherent safety level, Quantitative Risk Assessment (QRA) was performed to calculate total risk level of the cascade as the third objective function. Two cases (ammonia and propane) were considered to be compared as the refrigerant of the high temperature circuit. The achieved optimum solutions from the multi–objective optimization process were given as Pareto frontier. The ultimate optimal solution from available solutions on the Pareto optimal curve was selected using Decision-Makings approaches. NSGA-II algorithm was used to obtain Pareto optimal frontiers. Also, three decision-making approaches (TOPSIS, LINMAP, and Shannon’s entropy methods) were utilized to select the final optimum point. Considering continuous material release from the major equipment in the plant, flash and jet fire scenarios were considered for the CO 2 /C 3 H 8 cycle and toxic hazards were considered for the CO 2 /NH 3 cycle. The results showed no significant differences between CO 2 /NH 3 and

  8. Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Xiang Yu

    2016-06-01

    Full Text Available Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing hydropower generation and minimizing deviation from the outflow lower target to realize the system’s economic, environmental and social benefits during the drought season, are optimized simultaneously. Experimental results demonstrate that the optimization framework helps to robustly derive multiple feasible non-dominated solutions with satisfactory convergence, diversity and extremity in one single run for the case studied.

  9. Mathematical programming model for heat exchanger design through optimization of partial objectives

    International Nuclear Information System (INIS)

    Onishi, Viviani C.; Ravagnani, Mauro A.S.S.; Caballero, José A.

    2013-01-01

    Highlights: • Rigorous design of shell-and-tube heat exchangers according to TEMA standards. • Division of the problem into sets of equations that are easier to solve. • Selected heuristic objective functions based on the physical behavior of the problem. • Sequential optimization approach to avoid solutions stuck in local minimum. • The results obtained with this model improved the values reported in the literature. - Abstract: Mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigorously the standards of the Tubular Exchanger Manufacturers Association (TEMA). Bell–Delaware Method is used for the shell-side calculations. This approach produces a large and non-convex model that cannot be solved to global optimality with the current state of the art solvers. Notwithstanding, it is proposed to perform a sequential optimization approach of partial objective targets through the division of the problem into sets of related equations that are easier to solve. For each one of these problems a heuristic objective function is selected based on the physical behavior of the problem. The global optimal solution of the original problem cannot be ensured even in the case in which each of the sub-problems is solved to global optimality, but at least a very good solution is always guaranteed. Three cases extracted from the literature were studied. The results showed that in all cases the values obtained using the proposed MINLP model containing multiple objective functions improved the values presented in the literature

  10. Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g

    Directory of Open Access Journals (Sweden)

    Guozheng Li

    2018-03-01

    Full Text Available The integration of renewable energies into combined cooling, heating, and power (CCHP systems has become increasingly popular in recent years. However, the optimization of renewable energies integrated CCHP (RECCHP systems (i.e., optimal component configurations is far from being well addressed, especially in isolated mode. This study aims to fill this research gap. A multi-objective optimization model characterizing the system reliability, system cost, and environmental sustainability is constructed. In this model, the objectives include minimization of annual total cost (ATC, carbon dioxide emission (CDE, and loss of energy supply probability (LESP. The decision variables representing the configuration of the RECCHP system include the number of photovoltaic (PV panels and wind turbines (WTs, the tilt angle of PV panels, the height of WTs, the maximum fuel consumption, and the capacity of battery and heat storage tanks (HSTs. The multi-objective model is solved by a multi-objective evolutionary algorithm, namely, the preference-inspired coevolutionary algorithm (PICEA-g, resulting in a set of Pareto optimal (trade-off solutions. Then, a decision-making process is demonstrated, selecting a preferred solution amongst those trade-off solutions by further considering the decision-maker preferences. Furthermore, on the optimization of the RECCHP system, operational strategies (i.e., following electric load, FEL, and following thermal load, FTL are considered, respectively. Experimental results show that the FEL and FTL strategies lead to different optimal configurations. In general, the FTL is recommended in summer and winter, while the FEL is more suitable for spring and autumn. Compared with traditional energy systems, RECCHP has better economic and environmental advantages.

  11. Intersection signal control multi-objective optimization based on genetic algorithm

    Directory of Open Access Journals (Sweden)

    Zhanhong Zhou

    2014-04-01

    Full Text Available A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems arise recently, an intersection signal control optimization method which aims at reducing vehicle emissions, fuel consumption and vehicle delay is required heavily. This paper proposed a signal control multi-object optimization method to reduce vehicle emissions, fuel consumption and vehicle delay simultaneously at an intersection. The optimization method combined the Paramics microscopic traffic simulation software, Comprehensive Modal Emissions Model (CMEM, and genetic algorithm. An intersection in Haizhu District, Guangzhou, was taken for a case study. The result of the case study shows the optimal timing scheme obtained from this method is better than the Webster timing scheme.

  12. Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

    Directory of Open Access Journals (Sweden)

    Yasuaki Miyazato

    2016-12-01

    Full Text Available A smart house generally has a Photovoltaic panel (PV, a Heat Pump (HP, a Solar Collector (SC and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions.

  13. 3D high- and super-resolution imaging using single-objective SPIM.

    Science.gov (United States)

    Galland, Remi; Grenci, Gianluca; Aravind, Ajay; Viasnoff, Virgile; Studer, Vincent; Sibarita, Jean-Baptiste

    2015-07-01

    Single-objective selective-plane illumination microscopy (soSPIM) is achieved with micromirrored cavities combined with a laser beam-steering unit installed on a standard inverted microscope. The illumination and detection are done through the same objective. soSPIM can be used with standard sample preparations and features high background rejection and efficient photon collection, allowing for 3D single-molecule-based super-resolution imaging of whole cells or cell aggregates. Using larger mirrors enabled us to broaden the capabilities of our system to image Drosophila embryos.

  14. Multi objective optimization of horizontal axis tidal current turbines, using Meta heuristics algorithms

    International Nuclear Information System (INIS)

    Tahani, Mojtaba; Babayan, Narek; Astaraei, Fatemeh Razi; Moghadam, Ali

    2015-01-01

    Highlights: • The performance of four different Meta heuristic optimization algorithms was studied. • Power coefficient and produced torque on stationary blade were selected as objective functions. • Chord and twist distributions were selected as decision variables. • All optimization algorithms were combined with blade element momentum theory. • The best Pareto front was obtained by multi objective flower pollination algorithm for HATCTs. - Abstract: The performance of horizontal axis tidal current turbines (HATCT) strongly depends on their geometry. According to this fact, the optimum performance will be achieved by optimized geometry. In this research study, the multi objective optimization of the HATCT is carried out by using four different multi objective optimization algorithms and their performance is evaluated in combination with blade element momentum theory (BEM). The second version of non-dominated sorting genetic algorithm (NSGA-II), multi objective particle swarm optimization algorithm (MOPSO), multi objective cuckoo search algorithm (MOCS) and multi objective flower pollination algorithm (MOFPA) are the selected algorithms. The power coefficient and the produced torque on stationary blade are selected as objective functions and chord and twist distributions along the blade span are selected as decision variables. These algorithms are combined with the blade element momentum (BEM) theory for the purpose of achieving the best Pareto front. The obtained Pareto fronts are compared with each other. Different sets of experiments are carried out by considering different numbers of iterations, population size and tip speed ratios. The Pareto fronts which are achieved by MOFPA and NSGA-II have better quality in comparison to MOCS and MOPSO, but on the other hand a detail comparison between the first fronts of MOFPA and NSGA-II indicated that MOFPA algorithm can obtain the best Pareto front and can maximize the power coefficient up to 4.3% and the

  15. Determination of radial profile of ICF hot spot's state by multi-objective parameters optimization

    International Nuclear Information System (INIS)

    Dong Jianjun; Deng Bo; Cao Zhurong; Ding Yongkun; Jiang Shaoen

    2014-01-01

    A method using multi-objective parameters optimization is presented to determine the radial profile of hot spot temperature and density. And a parameter space which contain five variables: the temperatures at center and the interface of fuel and remain ablator, the maximum model density of remain ablator, the mass ratio of remain ablator to initial ablator and the position of interface between fuel and the remain ablator, is used to described the hot spot radial temperature and density. Two objective functions are set as the variances of normalized intensity profile from experiment X-ray images and the theory calculation. Another objective function is set as the variance of experiment average temperature of hot spot and the average temperature calculated by theoretical model. The optimized parameters are obtained by multi-objective genetic algorithm searching for the five dimension parameter space, thereby the optimized radial temperature and density profiles can be determined. The radial temperature and density profiles of hot spot by experiment data measured by KB microscope cooperating with X-ray film are presented. It is observed that the temperature profile is strongly correlated to the objective functions. (authors)

  16. Many-Objective Optimization Using Adaptive Differential Evolution with a New Ranking Method

    Directory of Open Access Journals (Sweden)

    Xiaoguang He

    2014-01-01

    Full Text Available Pareto dominance is an important concept and is usually used in multiobjective evolutionary algorithms (MOEAs to determine the nondominated solutions. However, for many-objective problems, using Pareto dominance to rank the solutions even in the early generation, most obtained solutions are often the nondominated solutions, which results in a little selection pressure of MOEAs toward the optimal solutions. In this paper, a new ranking method is proposed for many-objective optimization problems to verify a relatively smaller number of representative nondominated solutions with a uniform and wide distribution and improve the selection pressure of MOEAs. After that, a many-objective differential evolution with the new ranking method (MODER for handling many-objective optimization problems is designed. At last, the experiments are conducted and the proposed algorithm is compared with several well-known algorithms. The experimental results show that the proposed algorithm can guide the search to converge to the true PF and maintain the diversity of solutions for many-objective problems.

  17. Design of AC-DC Grid Connected Converter using Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    Piasecki Szymon

    2014-05-01

    Full Text Available Power electronic circuits, in particular AC-DC converters are complex systems, many different parameters and objectives have to be taken into account during the design process. Implementation of Multi-Objective Optimization (MOO seems to be attractive idea, which used as designer supporting tool gives possibility for better analysis of the designed system. This paper presents a short introduction to the MOO applied in the field of power electronics. Short introduction to the subject is given in section I. Then, optimization process and its elements are briefly described in section II. Design procedure with proposed optimization parameters and performance indices for AC-DC Grid Connected Converter (GCC interfacing distributed systems is introduced in section III. Some preliminary optimization results, achieved on the basis of analytical and simulation study, are shown at each stage of designing process. Described optimization parameters and performance indices are part of developed global optimization method dedicated for ACDC GCC introduced in section IV. Described optimization method is under development and only short introduction and basic assumptions are presented. In section V laboratory prototype of high efficient and compact 14 kVA AC-DC converter is introduced. The converter is elaborated based on performed designing and optimization procedure with the use of silicon carbide (SiC power semiconductors. Finally, the paper is summarized and concluded in section VI. In presented work theoretical research are conducted in parallel with laboratory prototyping e.g. all theoretical ideas are verified in laboratory using modern DSP microcontrollers and prototypes of the ACDC GCC.

  18. Simultaneous positioning and orientation of a single nano-object by flow control: theory and simulations

    International Nuclear Information System (INIS)

    Mathai, Pramod P; Berglund, Andrew J; Alexander Liddle, J; Shapiro, Benjamin A

    2011-01-01

    In this paper, we theoretically describe a method to simultaneously control both the position and orientation of single nano-objects in fluids by precisely controlling the flow around them. We develop and simulate a control law that uses electro-osmotic flow (EOF) actuation to translate and rotate rigid nano-objects in two spatial dimensions. Using EOF to control nano-objects offers advantages as compared to other approaches: a wide class of objects can be manipulated (no magnetic or electric dipole moments are needed), the object can be controlled over a long range (>100 μm) with sub-micrometer accuracy, and control may be achieved with simple polydimethylsiloxane (PDMS) devices. We demonstrate the theory and numerical solutions that will enable deterministic control of the position and orientation of a nano-object in solution, which can be used, for example, to integrate nanostructures in circuits and orient sensors to probe living cells.

  19. Multi-objective optimization of a series–parallel system using GPSIA

    International Nuclear Information System (INIS)

    Okafor, Ekene Gabriel; Sun Youchao

    2012-01-01

    The optimal solution of a multi-objective optimization problem (MOP) corresponds to a Pareto set that is characterized by a tradeoff between objectives. Genetic Pareto Set Identification Algorithm (GPSIA) proposed for reliability-redundant MOPs is a hybrid technique which combines genetic and heuristic principles to generate non-dominated solutions. Series–parallel system with active redundancy is studied in this paper. Reliability and cost were the research objective functions subject to cost and weight constraints. The results reveal an evenly distributed non-dominated front. The distances between successive Pareto points were used to evaluate the general performance of the method. Plots were also used to show the computational results for the type of system studied and the robustness of the technique is discussed in comparison with NSGA-II and SPEA-2.

  20. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

    OpenAIRE

    Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of tra...

  1. Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

    Science.gov (United States)

    Fiorini, Rodolfo A.; Dacquino, Gianfranco

    2005-03-01

    GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous

  2. Parametric analysis of energy quality management for district in China using multi-objective optimization approach

    International Nuclear Information System (INIS)

    Lu, Hai; Yu, Zitao; Alanne, Kari; Xu, Xu; Fan, Liwu; Yu, Han; Zhang, Liang; Martinac, Ivo

    2014-01-01

    Highlights: • A time-effective multi-objective design optimization scheme is proposed. • The scheme aims at exploring suitable 3E energy system for the specific case. • A realistic case located in China is used for the analysis. • Parametric study is investigated to test the effects of different parameters. - Abstract: Due to the increasing energy demands and global warming, energy quality management (EQM) for districts has been getting importance over the last few decades. The evaluation of the optimum energy systems for specific districts is an essential part of EQM. This paper presents a deep analysis of the optimum energy systems for a district sited in China. A multi-objective optimization approach based on Genetic Algorithm (GA) is proposed for the analysis. The optimization process aims to search for the suitable 3E (minimum economic cost and environmental burden as well as maximum efficiency) energy systems. Here, life cycle CO 2 equivalent (LCCO 2 ), life cycle cost (LCC) and exergy efficiency (EE) are set as optimization objectives. Then, the optimum energy systems for the Chinese case are presented. The final work is to investigate the effects of different energy parameters. The results show the optimum energy systems might vary significantly depending on some parameters

  3. Multi-objective optimization of linear multi-state multiple sliding window system

    International Nuclear Information System (INIS)

    Konak, Abdullah; Kulturel-Konak, Sadan; Levitin, Gregory

    2012-01-01

    This paper considers the optimal element sequencing in a linear multi-state multiple sliding window system that consists of n linearly ordered multi-state elements. Each multi-state element can have different states: from complete failure up to perfect functioning. A performance rate is associated with each state. The failure of type i in the system occurs if for any i (1≤i≤I) the cumulative performance of any r i consecutive elements is lower than w i . The element sequence strongly affects the probability of any type of system failure. The sequence that minimizes the probability of certain type of failure can provide high probability of other types of failures. Therefore the optimization problem for the multiple sliding window system is essentially multi-objective. The paper formulates and solves the multi-objective optimization problem for the multiple sliding window systems. A multi-objective Genetic Algorithm is used as the optimization engine. Illustrative examples are presented.

  4. Multi-objective optimization with estimation of distribution algorithm in a noisy environment.

    Science.gov (United States)

    Shim, Vui Ann; Tan, Kay Chen; Chia, Jun Yong; Al Mamun, Abdullah

    2013-01-01

    Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.

  5. System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization

    International Nuclear Information System (INIS)

    Uriarte, A Goienetxea; Zúñiga, E Ruiz; Moris, M Urenda; Ng, A H C

    2015-01-01

    Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process. (paper)

  6. Pareto Optimal Solutions for Network Defense Strategy Selection Simulator in Multi-Objective Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Yang Sun

    2018-01-01

    Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.

  7. Objectivity

    CERN Document Server

    Daston, Lorraine

    2010-01-01

    Objectivity has a history, and it is full of surprises. In Objectivity, Lorraine Daston and Peter Galison chart the emergence of objectivity in the mid-nineteenth-century sciences--and show how the concept differs from its alternatives, truth-to-nature and trained judgment. This is a story of lofty epistemic ideals fused with workaday practices in the making of scientific images. From the eighteenth through the early twenty-first centuries, the images that reveal the deepest commitments of the empirical sciences--from anatomy to crystallography--are those featured in scientific atlases, the compendia that teach practitioners what is worth looking at and how to look at it. Galison and Daston use atlas images to uncover a hidden history of scientific objectivity and its rivals. Whether an atlas maker idealizes an image to capture the essentials in the name of truth-to-nature or refuses to erase even the most incidental detail in the name of objectivity or highlights patterns in the name of trained judgment is a...

  8. Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

    Science.gov (United States)

    Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

    2015-06-18

    Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

  9. Multi-objective Design Optimization of a Parallel Schönflies-motion Robot

    DEFF Research Database (Denmark)

    Wu, Guanglei; Bai, Shaoping; Hjørnet, Preben

    2016-01-01

    . The dynamic performance is concerned mainly the capability of force transmission in the parallel kinematic chain, for which transmission indices are defined. The Pareto-front is obtained to investigate the influence of the design variables to the robot performance. Dynamic characteristics for three Pareto......This paper introduces a parallel Schoenflies-motion robot with rectangular workspace, which is suitable for pick-and-place operations. A multi-objective optimization problem is formulated to optimize the robot's geometric parameters with consideration of kinematic and dynamic performances...

  10. A Method for Interactive 3D Reconstruction of Piecewise Planar Objects from Single Images

    OpenAIRE

    Sturm , Peter; Maybank , Steve

    1999-01-01

    International audience; We present an approach for 3D reconstruction of objects from a single image. Obviously, constraints on the 3D structure are needed to perform this task. Our approach is based on user-provided coplanarity, perpendicularity and parallelism constraints. These are used to calibrate the image and perform 3D reconstruction. The method is described in detail and results are provided.

  11. Constrained multi-objective optimization of radial expanders in organic Rankine cycles by firefly algorithm

    International Nuclear Information System (INIS)

    Bahadormanesh, Nikrouz; Rahat, Shayan; Yarali, Milad

    2017-01-01

    Highlights: • A multi-objective optimization for radial expander in Organic Rankine Cycles is implemented. • By using firefly algorithm, Pareto front based on the size of turbine and thermal efficiency is produced. • Tension and vibration constrains have a significant effect on optimum design points. - Abstract: Organic Rankine Cycles are viable energy conversion systems in sustainable energy systems due to their compatibility with low-temperature heat sources. In the present study, one dimensional model of radial expanders in conjunction with a thermodynamic model of organic Rankine cycles is prepared. After verification, by defining thermal efficiency of the cycle and size parameter of a radial turbine as the objective functions, a multi-objective optimization was conducted regarding tension and vibration constraints for 4 different organic working fluids (R22, R245fa, R236fa and N-Pentane). In addition to mass flow rate, evaporator temperature, maximum pressure of cycle and turbo-machinery design parameters are selected as the decision variables. Regarding Pareto fronts, by a little increase in size of radial expanders, it is feasible to reach high efficiency. Moreover, by assessing the distribution of decision variables, the variables that play a major role in trending between the objective functions are found. Effects of mechanical and vibration constrains on optimum decision variables are investigated. The results of optimization can be considered as an initial values for design of radial turbines for Organic Rankine Cycles.

  12. Environment-Aware Production Schedulingfor Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach.

    Science.gov (United States)

    Zhang, Rui

    2017-12-25

    The traditional way of scheduling production processes often focuses on profit-driven goals (such as cycle time or material cost) while tending to overlook the negative impacts of manufacturing activities on the environment in the form of carbon emissions and other undesirable by-products. To bridge the gap, this paper investigates an environment-aware production scheduling problem that arises from a typical paint shop in the automobile manufacturing industry. In the studied problem, an objective function is defined to minimize the emission of chemical pollutants caused by the cleaning of painting devices which must be performed each time before a color change occurs. Meanwhile, minimization of due date violations in the downstream assembly shop is also considered because the two shops are interrelated and connected by a limited-capacity buffer. First, we have developed a mixed-integer programming formulation to describe this bi-objective optimization problem. Then, to solve problems of practical size, we have proposed a novel multi-objective particle swarm optimization (MOPSO) algorithm characterized by problem-specific improvement strategies. A branch-and-bound algorithm is designed for accurately assessing the most promising solutions. Finally, extensive computational experiments have shown that the proposed MOPSO is able to match the solution quality of an exact solver on small instances and outperform two state-of-the-art multi-objective optimizers in literature on large instances with up to 200 cars.

  13. Multi-objective portfolio optimization of mutual funds under downside risk measure using fuzzy theory

    Directory of Open Access Journals (Sweden)

    M. Amiri

    2012-10-01

    Full Text Available Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnover rate, Treynor index and Sharpe index. Semivariance is used as a downside risk measure. The proposed model of this paper uses fuzzy variables for return rate and semivariance. A multi-objective fuzzy mean-semivariance portfolio optimization model is implemented and fuzzy programming technique is adopted to solve the resulted problem. The proposed model of this paper has gathered the information of mutual fund traded on Nasdaq from 2007 to 2009 and Pareto optimal solutions are obtained considering different weights for objective functions. The results of asset allocation, rate of return and risk of each cluster are also determined and they are compared with the results of two clustering methods.

  14. Multiplicative mixing of object identity and image attributes in single inferior temporal neurons.

    Science.gov (United States)

    Ratan Murty, N Apurva; Arun, S P

    2018-04-03

    Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively. Copyright © 2018 the Author(s). Published by PNAS.

  15. A conceptual framework for economic optimization of single hazard surveillance in livestock production chains.

    Science.gov (United States)

    Guo, Xuezhen; Claassen, G D H; Oude Lansink, A G J M; Saatkamp, H W

    2014-06-01

    Economic analysis of hazard surveillance in livestock production chains is essential for surveillance organizations (such as food safety authorities) when making scientifically based decisions on optimization of resource allocation. To enable this, quantitative decision support tools are required at two levels of analysis: (1) single-hazard surveillance system and (2) surveillance portfolio. This paper addresses the first level by presenting a conceptual approach for the economic analysis of single-hazard surveillance systems. The concept includes objective and subjective aspects of single-hazard surveillance system analysis: (1) a simulation part to derive an efficient set of surveillance setups based on the technical surveillance performance parameters (TSPPs) and the corresponding surveillance costs, i.e., objective analysis, and (2) a multi-criteria decision making model to evaluate the impacts of the hazard surveillance, i.e., subjective analysis. The conceptual approach was checked for (1) conceptual validity and (2) data validity. Issues regarding the practical use of the approach, particularly the data requirement, were discussed. We concluded that the conceptual approach is scientifically credible for economic analysis of single-hazard surveillance systems and that the practicability of the approach depends on data availability. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives

    International Nuclear Information System (INIS)

    Warmflash, Aryeh; Siggia, Eric D; Francois, Paul

    2012-01-01

    The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input–output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria. (paper)

  17. Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.

    Science.gov (United States)

    Warmflash, Aryeh; Francois, Paul; Siggia, Eric D

    2012-10-01

    The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.

  18. Hovering efficiency comparison of rotary and flapping flight for a rigid and rectangular wings via dimensionless multi-objective optimization.

    Science.gov (United States)

    Bayiz, Yagiz Efe; Ghanaatpishe, Mohammad; Fathy, Hosam; Cheng, Bo

    2018-03-20

    In this work, a multi-objective optimization framework is developed for optimizing low-Reynolds number (Re) hovering flight. This framework is then applied to compare the efficiency of rigid revolving and flapping wings with rectangular shape under varying Re and Rossby number (Ro, or aspect ratio). The proposed framework is capable of generating sets of optimal solutions and Pareto fronts for maximizing lift coefficient and minimizing power coefficient in dimensionless space, which explicitly reveal the trade off between lift generation and power consumption. The results indicate that revolving wings are more efficient if the required average lift coefficient CL is low (< 1 for Re = 100 and < 1.6 for Re = 8000), while flapping wings are more efficient in achieving higher CL. Using dimensionless power loading as the single objective performance measure to be maximized, rotary flight is more efficient than flapping wings for Re > 100 regardless of the amount of energy storage assumed in the flapping-wing actuation mechanism, while flapping flight becomes more efficient for Re < 100. It is observed that wings with low Ro perform better if higher CL is needed, whereas higher Ro cases are more efficient at CL < 0.9 region. However, for the selected geometry and Re, the efficiency is weakly dependent on Ro if the dimensionless power loading is maximized. © 2018 IOP Publishing Ltd.

  19. Sensitivity Analysis of Multi-objective Optimization for Solid Waste Management: A Case Study of Dar es Salaam, Tanzania

    Directory of Open Access Journals (Sweden)

    Halidi Lyeme

    2017-11-01

    Full Text Available In this study, a sensitivity analysis of a multi-objective optimization model for solid waste management (SWM for Dar es Salaam city in Tanzania is considered. Our objectives were to identify the most sensitive parameters and effect of other input data to the model output. Five scenarios were considered by varying their associated parameter values. The results showed that the decrease of total cost for the SWM system in all scenarios was observed compared to the baseline solution when the single landfill was considered. Furthermore, the analysis shows that the variable cost parameter for the processing facilities is very sensitivity in such a way that if you increase the variable cost then, there is a rapid increase of total cost for the SWM system and the vice versa is true. The relevant suggestions to the decision makers were also discussed.

  20. Single-trial multisensory memories affect later auditory and visual object discrimination.

    Science.gov (United States)

    Thelen, Antonia; Talsma, Durk; Murray, Micah M

    2015-05-01

    Multisensory memory traces established via single-trial exposures can impact subsequent visual object recognition. This impact appears to depend on the meaningfulness of the initial multisensory pairing, implying that multisensory exposures establish distinct object representations that are accessible during later unisensory processing. Multisensory contexts may be particularly effective in influencing auditory discrimination, given the purportedly inferior recognition memory in this sensory modality. The possibility of this generalization and the equivalence of effects when memory discrimination was being performed in the visual vs. auditory modality were at the focus of this study. First, we demonstrate that visual object discrimination is affected by the context of prior multisensory encounters, replicating and extending previous findings by controlling for the probability of multisensory contexts during initial as well as repeated object presentations. Second, we provide the first evidence that single-trial multisensory memories impact subsequent auditory object discrimination. Auditory object discrimination was enhanced when initial presentations entailed semantically congruent multisensory pairs and was impaired after semantically incongruent multisensory encounters, compared to sounds that had been encountered only in a unisensory manner. Third, the impact of single-trial multisensory memories upon unisensory object discrimination was greater when the task was performed in the auditory vs. visual modality. Fourth, there was no evidence for correlation between effects of past multisensory experiences on visual and auditory processing, suggestive of largely independent object processing mechanisms between modalities. We discuss these findings in terms of the conceptual short term memory (CSTM) model and predictive coding. Our results suggest differential recruitment and modulation of conceptual memory networks according to the sensory task at hand. Copyright

  1. A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

    NARCIS (Netherlands)

    Hamdy, M.; Nguyen, A.T. (Anh Tuan); Hensen, J.L.M.

    2016-01-01

    Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective optimization algorithms have been developed; however few of them are tested in solving building design

  2. Multi-objective analytical model for optimal sizing of stand-alone photovoltaic water pumping systems

    International Nuclear Information System (INIS)

    Olcan, Ceyda

    2015-01-01

    Highlights: • An analytical optimal sizing model is proposed for PV water pumping systems. • The objectives are chosen as deficiency of power supply and life-cycle costs. • The crop water requirements are estimated for a citrus tree yard in Antalya. • The optimal tilt angles are calculated for fixed, seasonal and monthly changes. • The sizing results showed the validity of the proposed analytical model. - Abstract: Stand-alone photovoltaic (PV) water pumping systems effectively use solar energy for irrigation purposes in remote areas. However the random variability and unpredictability of solar energy makes difficult the penetration of PV implementations and complicate the system design. An optimal sizing of these systems proves to be essential. This paper recommends a techno-economic optimization model to determine optimally the capacity of the components of PV water pumping system using a water storage tank. The proposed model is developed regarding the reliability and cost indicators, which are the deficiency of power supply probability and life-cycle costs, respectively. The novelty is that the proposed optimization model is analytically defined for two-objectives and it is able to find a compromise solution. The sizing of a stand-alone PV water pumping system comprises a detailed analysis of crop water requirements and optimal tilt angles. Besides the necessity of long solar radiation and temperature time series, the accurate forecasts of water supply needs have to be determined. The calculation of the optimal tilt angle for yearly, seasonally and monthly frequencies results in higher system efficiency. It is, therefore, suggested to change regularly the tilt angle in order to maximize solar energy output. The proposed optimal sizing model incorporates all these improvements and can accomplish a comprehensive optimization of PV water pumping systems. A case study is conducted considering the irrigation of citrus trees yard located in Antalya, Turkey

  3. Effects of Implied Motion and Facing Direction on Positional Preferences in Single-Object Pictures.

    Science.gov (United States)

    Palmer, Stephen E; Langlois, Thomas A

    2017-07-01

    Palmer, Gardner, and Wickens studied aesthetic preferences for pictures of single objects and found a strong inward bias: Right-facing objects were preferred left-of-center and left-facing objects right-of-center. They found no effect of object motion (people and cars showed the same inward bias as chairs and teapots), but the objects were not depicted as moving. Here we measured analogous inward biases with objects depicted as moving with an implied direction and speed by having participants drag-and-drop target objects into the most aesthetically pleasing position. In Experiment 1, human figures were shown diving or falling while moving forward or backward. Aesthetic biases were evident for both inward-facing and inward-moving figures, but the motion-based bias dominated so strongly that backward divers or fallers were preferred moving inward but facing outward. Experiment 2 investigated implied speed effects using images of humans, horses, and cars moving at different speeds (e.g., standing, walking, trotting, and galloping horses). Inward motion or facing biases were again present, and differences in their magnitude due to speed were evident. Unexpectedly, faster moving objects were generally preferred closer to frame center than slower moving objects. These results are discussed in terms of the combined effects of prospective, future-oriented biases, and retrospective, past-oriented biases.

  4. A spatial multi-objective optimization model for sustainable urban wastewater system layout planning.

    Science.gov (United States)

    Dong, X; Zeng, S; Chen, J

    2012-01-01

    Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design.

  5. Wave drag as the objective function in transonic fighter wing optimization

    Science.gov (United States)

    Phillips, P. S.

    1984-01-01

    The original computational method for determining wave drag in a three dimensional transonic analysis method was replaced by a wave drag formula based on the loss in momentum across an isentropic shock. This formula was used as the objective function in a numerical optimization procedure to reduce the wave drag of a fighter wing at transonic maneuver conditions. The optimization procedure minimized wave drag through modifications to the wing section contours defined by a wing profile shape function. A significant reduction in wave drag was achieved while maintaining a high lift coefficient. Comparisons of the pressure distributions for the initial and optimized wing geometries showed significant reductions in the leading-edge peaks and shock strength across the span.

  6. An improved fast and elitist multi-objective genetic algorithm-ANSGA-II for multi-objective optimization of inverse radiotherapy treatment planning

    International Nuclear Information System (INIS)

    Cao Ruifen; Li Guoli; Song Gang; Zhao Pan; Lin Hui; Wu Aidong; Huang Chenyu; Wu Yican

    2007-01-01

    Objective: To provide a fast and effective multi-objective optimization algorithm for inverse radiotherapy treatment planning system. Methods: Non-dominated Sorting Genetic Algorithm-NSGA-II is a representative of multi-objective evolutionary optimization algorithms and excels the others. The paper produces ANSGA-II that makes use of advantage of NSGA-II, and uses adaptive crossover and mutation to improve its flexibility; according the character of inverse radiotherapy treatment planning, the paper uses the pre-known knowledge to generate individuals of every generation in the course of optimization, which enhances the convergent speed and improves efficiency. Results: The example of optimizing average dose of a sheet of CT, including PTV, OAR, NT, proves the algorithm could find satisfied solutions in several minutes. Conclusions: The algorithm could provide clinic inverse radiotherapy treatment planning system with selection of optimization algorithms. (authors)

  7. An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

    Directory of Open Access Journals (Sweden)

    Jiuyuan Huo

    2017-02-01

    Full Text Available To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

  8. Multi-Objective Optimization of Pulsed Power Supply for a Railgun

    Directory of Open Access Journals (Sweden)

    Mehrdad Jafarboland

    2011-07-01

    Full Text Available A novel two-objective optimization design model for pulsed power supply (PPS is proposed in this paper. The objectives are the muzzle velocity and the stored-to-kinetic energy efficiency. The design variables include the operating voltage and the trigger delay times between segments. The acceleration of the armature is constrained to lower than 106 m/s2. The optimization results for nuzzle velocity and the efficiency separately show the following: 1 The acceleration constraint has great influence on the performance; 2 wide current pulse yields high velocity but low efficiency; and 3 The operating voltage has to be increased to accelerate a heavier projectile to a certain velocity or at a certain efficiency. Pareto solution fronts for various projectile masses are found using the nondominated sorting genetic algorithm (NSGA-II under the integration environment of MATLAB software.

  9. Multiple objective optimization of hydro-thermal systems using Ritz's method

    Directory of Open Access Journals (Sweden)

    Arnáu L. Bayón

    1999-01-01

    Full Text Available This paper examines the applicability of the Ritz method to multi-objective optimization of hydro-thermal systems. The algorithm proposed is aimed to minimize an objective functional that incorporates the cost of energy losses, the conventional fuel cost and the production of atmospheric emissions such as NO x and SO 2 caused by the operation of fossil-fueled thermal generation. The formulation includes a general layout of hydro-plants that may form multi-chains of reservoir network. Time-delays are included and the electric network is considered by using the active power balance equation. The volume of water discharge for each hydro-plant is a given constant amount from the optimization interval. The generic minimization algorithm, which is not difficult to construct on the basis of the Ritz method, has certain advantages in comparison with the conventional methods.

  10. A multi-objective optimization for brush monofilament tufting process design

    Directory of Open Access Journals (Sweden)

    Ali Salmasnia

    2018-01-01

    Full Text Available This paper addresses the optimization of monofilament tufting process as the most important and the main stage of toothbrush production in sanitary industries. In order to minimize both process time and depreciation costs, and ultimately increase the production efficiency in such an industrial unit, we propose a metaheuristic based optimization approach to solve it. The Traveling Salesman Problem (TSP is used to formulate the proposed problem. Then by using multi-objective evolutionary algorithms, NSGA-II and MOPSO, we seek to obtain the best solution and objective functions described above. Extensive computational experiments on three different kinds of toothbrush handles are performed and the results demonstrate the applicability and appropriate performance of algorithms. The comparison metrics like spacing, number of Pareto solutions, time, mean distance from the ideal solution and diversity are used to evaluate the quality of solutions. Moreover a sensitivity analysis is done for investigation of the performance in various setting of parameters.

  11. Extension of an Object-Oriented Optimization Tool: User's Reference Manual

    Science.gov (United States)

    Pak, Chan-Gi; Truong, Samson S.

    2015-01-01

    The National Aeronautics and Space Administration Armstrong Flight Research Center has developed a cost-effective and flexible object-oriented optimization (O (sup 3)) tool that leverages existing tools and practices and allows easy integration and adoption of new state-of-the-art software. This object-oriented framework can integrate the analysis codes for multiple disciplines, as opposed to relying on one code to perform analysis for all disciplines. Optimization can thus take place within each discipline module, or in a loop between the O (sup 3) tool and the discipline modules, or both. Six different sample mathematical problems are presented to demonstrate the performance of the O (sup 3) tool. Instructions for preparing input data for the O (sup 3) tool are detailed in this user's manual.

  12. Dynamic optimal grasping of a circular object with gravity using robotic soft-fingertips

    Directory of Open Access Journals (Sweden)

    García-Rodríguez Rodolfo

    2016-06-01

    Full Text Available Object manipulation usually requires dexterity, encoded as the ability to roll, which is very difficult to achieve with robotic hands based on point contact models (subject to holonomic constraints. As an alternative for dexterous manipulation, deformable contact with hemispherical shape fingertips has been proposed to yield naturally a rolling constraint. It entails dexterity at the expense of dealing with normal and tangential forces, as well as more elaborated models and control schemes. Furthermore, the essential feature of the quality of grasp can be addressed with this type of robot hands, but it has been overlooked for deformable contact. In this paper, a passivity-based controller that considers an optimal grasping measure is proposed for robotic hands with hemispherical deformable fingertips, to manipulate circular dynamic objects. Optimal grasping that minimizes the contact wrenches is achieved through fingertip rolling until normal forces pass through the center of mass of the object, aligning the relative angle between these normal forces. The case of a circular object is developed in detail, though our proposal can be extended to objects with an arbitrary shape that admit a local decomposition by a circular curvature. Simulation and experimental results show convergence under various conditions, wherein rolling and tangent forces become instrumental to achieve such a quality of grasp.

  13. Nonlinear bioheat transfer models and multi-objective numerical optimization of the cryosurgery operations

    Energy Technology Data Exchange (ETDEWEB)

    Kudryashov, Nikolay A.; Shilnikov, Kirill E. [National Research Nuclear University MEPhI, Department of Applied Mathematics, Moscow (Russian Federation)

    2016-06-08

    Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumor tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.

  14. Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

    Science.gov (United States)

    Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

    2016-01-01

    Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

  15. Multi-objective Optimization of Process Parameters in Friction Stir Welding

    DEFF Research Database (Denmark)

    Tutum, Cem Celal; Hattel, Jesper Henri

    The objective of this paper is to investigate optimum process parameters in Friction Stir Welding (FSW) to minimize residual stresses in the work piece and maximize production efficiency meanwhile satisfying process specific constraints as well. More specifically, the choices of tool rotational...... speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2- dimensional sequentially coupled thermo...

  16. Design for Sustainability of Industrial Symbiosis based on Emergy and Multi-objective Particle Swarm Optimization

    DEFF Research Database (Denmark)

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang

    2016-01-01

    approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable...... performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied...

  17. Multi-Objective Bidding Strategy for Genco Using Non-Dominated Sorting Particle Swarm Optimization

    Science.gov (United States)

    Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn

    2010-06-01

    This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in uniform price spot market using non-dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals' bidding behavior. Test results indicate that the proposed approach can provide the efficient non-dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

  18. Multiple objective optimization of hydro-thermal systems using Ritz's method

    Directory of Open Access Journals (Sweden)

    L. Bayón Arnáu

    2000-01-01

    Full Text Available This paper examines the applicability of the Ritz method to multi-objective optimization of hydro-thermal systems. The algorithm proposed is aimed to minimize an objective functional that incorporates the cost of energy losses, the conventional fuel cost and the production of atmospheric emissions such as NOx and SO2 caused by the operation of fossil-fueled thermal generation. The formulation includes a general layout of hydro-plants that may form multi-chains of reservoir network.

  19. A multiple ship routing and speed optimization problem under time, cost and environmental objectives

    DEFF Research Database (Denmark)

    Wen, M.; Pacino, Dario; Kontovas, C.A.

    2017-01-01

    The purpose of this paper is to investigate a multiple ship routing and speed optimization problem under time, cost and environmental objectives. A branch and price algorithm as well as a constraint programming model are developed that consider (a) fuel consumption as a function of payload, (b......) fuel price as an explicit input, (c) freight rate as an input, and (d) in-transit cargo inventory costs. The alternative objective functions are minimum total trip duration, minimum total cost and minimum emissions. Computational experience with the algorithm is reported on a variety of scenarios....

  20. Identification of Auditory Object-Specific Attention from Single-Trial Electroencephalogram Signals via Entropy Measures and Machine Learning

    Directory of Open Access Journals (Sweden)

    Yun Lu

    2018-05-01

    Full Text Available Existing research has revealed that auditory attention can be tracked from ongoing electroencephalography (EEG signals. The aim of this novel study was to investigate the identification of peoples’ attention to a specific auditory object from single-trial EEG signals via entropy measures and machine learning. Approximate entropy (ApEn, sample entropy (SampEn, composite multiscale entropy (CmpMSE and fuzzy entropy (FuzzyEn were used to extract the informative features of EEG signals under three kinds of auditory object-specific attention (Rest, Auditory Object1 Attention (AOA1 and Auditory Object2 Attention (AOA2. The linear discriminant analysis and support vector machine (SVM, were used to construct two auditory attention classifiers. The statistical results of entropy measures indicated that there were significant differences in the values of ApEn, SampEn, CmpMSE and FuzzyEn between Rest, AOA1 and AOA2. For the SVM-based auditory attention classifier, the auditory object-specific attention of Rest, AOA1 and AOA2 could be identified from EEG signals using ApEn, SampEn, CmpMSE and FuzzyEn as features and the identification rates were significantly different from chance level. The optimal identification was achieved by the SVM-based auditory attention classifier using CmpMSE with the scale factor τ = 10. This study demonstrated a novel solution to identify the auditory object-specific attention from single-trial EEG signals without the need to access the auditory stimulus.

  1. A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization

    Czech Academy of Sciences Publication Activity Database

    Hájek, J.; Szöllös, A.; Šístek, Jakub

    2010-01-01

    Roč. 41, 7-8 (2010), s. 1031-1057 ISSN 0965-9978 R&D Projects: GA AV ČR IAA100760702 Institutional research plan: CEZ:AV0Z10190503 Keywords : multi-objective optimization * micro-genetic algorithm * diversity * Pareto archive Subject RIV: BA - General Mathematics Impact factor: 1.004, year: 2010 http://www.sciencedirect.com/science/article/pii/S0965997810000451

  2. A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization

    Czech Academy of Sciences Publication Activity Database

    Hájek, J.; Szöllös, A.; Šístek, Jakub

    2010-01-01

    Roč. 41, 7-8 (2010), s. 1031-1057 ISSN 0965-9978 R&D Projects: GA AV ČR IAA100760702 Institutional research plan: CEZ:AV0Z10190503 Keywords : multi-objective optimization * micro- genetic algorithm * diversity * Pareto archive Subject RIV: BA - General Mathematics Impact factor: 1.004, year: 2010 http://www.sciencedirect.com/science/article/pii/S0965997810000451

  3. Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes

    DEFF Research Database (Denmark)

    Maiti, Soumen K.; Eliasson Lantz, Anna; Bhushan, Mani

    2011-01-01

    batch operations using process model for Amycolatopsis balhimycina, a glycopeptide antibiotic producer. This resulted in a set of several pareto optimal solutions with the two objectives ranging from (0.75gl−1, 3.97g$-1) to (0.44gl−1, 5.19g$-1) for batch and from (1.5gl−1, 5.46g$-1) to (1.1gl−1, 6.34g...

  4. MULTI-OBJECTIVE OPTIMAL NUMBER AND LOCATION FOR STEEL OUTRIGGER-BELT TRUSS SYSTEM

    Directory of Open Access Journals (Sweden)

    MEHDI BABAEI

    2017-10-01

    Full Text Available During the past two decades, outrigger-belt truss system has been investigated and used in design of tall buildings. Most of the studies focused on the optimization of the system for minimum displacement and some of them proposed the best locations. In this study, however, multi-objective optimization of tall steel frames with belt trusses is investigated to minimize displacement and weight of the structure. For this purpose, structures with 20, 30, 40, and 50 stories are considered as models, based on the suggestions in the literature. The location and number of trusses and cross section of all structural elements are considered as design variables. After sizing of the structure for a specific topology and shape, weight and displacement of the structure are obtained and plotted in a diagram to illustrate trade-off between two objective functions. The results show the optimal Pareto-front solutions for different stories. Smooth trade-off and optimal number of trusses and their locations obtained.

  5. Probing optimal measurement configuration for optical scatterometry by the multi-objective genetic algorithm

    Science.gov (United States)

    Chen, Xiuguo; Gu, Honggang; Jiang, Hao; Zhang, Chuanwei; Liu, Shiyuan

    2018-04-01

    Measurement configuration optimization (MCO) is a ubiquitous and important issue in optical scatterometry, whose aim is to probe the optimal combination of measurement conditions, such as wavelength, incidence angle, azimuthal angle, and/or polarization directions, to achieve a higher measurement precision for a given measuring instrument. In this paper, the MCO problem is investigated and formulated as a multi-objective optimization problem, which is then solved by the multi-objective genetic algorithm (MOGA). The case study on the Mueller matrix scatterometry for the measurement of a Si grating verifies the feasibility of the MOGA in handling the MCO problem in optical scatterometry by making a comparison with the Monte Carlo simulations. Experiments performed at the achieved optimal measurement configuration also show good agreement between the measured and calculated best-fit Mueller matrix spectra. The proposed MCO method based on MOGA is expected to provide a more general and practical means to solve the MCO problem in the state-of-the-art optical scatterometry.

  6. Crowded Field Photometry and Moving Object Detection with Optimal Image Subtraction

    Science.gov (United States)

    Lee, Austin A. T.; Scheulen, F.; Sauro, C. M.; McMahon, C. T.; Berry, S. J.; Robinson, C. H.; Buie, M. W.; Little, P.

    2010-05-01

    High precision photometry and moving object detection are essential in the study of Pluto and the Kuiper Belt. In particular, the New Horizons mission would benefit from an accurate and fast method of performing image subtraction to locate faint Kuiper Belt Objects (KBO) among large data sets. The optimal image subtraction (OIS) algorithm was optimized for IDL to decrease execution time by a factor of about 140 from a previous implementation (Miller 2008, PASP, 120, 449). In addition, a powerful image transformation and interpolation routine was written to provide OIS with well-aligned input images using astrometric fit data. The first half of this project is complete including the code optimization and the alignment routine. The second half of the project is focused on using these tools to search a 5 x 10 degree search area to find KBOs for possible targets for the New Horizons mission. We will present examples of how these tools work and along with resulting Pluto photometry and KBO target lists. The optimized OIS and transformation routines are available in Marc Buie's IDL library at http://www.boulder.swri.edu/ buie/idl/ as ois.pro and dewarp.pro. This project was conducted for Harvey Mudd College's Clinic Program with financial support from the NASA Planetary Astronomy Program grant number NNX09AB43G.

  7. Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

    Science.gov (United States)

    Zheng, Ling; Duan, Xuwei; Deng, Zhaoxue; Li, Yinong

    2014-03-01

    A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.

  8. Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades

    Directory of Open Access Journals (Sweden)

    Jie Zhu

    2017-01-01

    Full Text Available A procedure based on MATLAB combined with ANSYS is presented and utilized for the multi-objective aerodynamic and structural optimization of horizontal-axis wind turbine (HAWT blades. In order to minimize the cost of energy (COE and improve the overall performance of the blades, materials of carbon fiber reinforced plastic (CFRP combined with glass fiber reinforced plastic (GFRP are applied. The maximum annual energy production (AEP, the minimum blade mass and the minimum blade cost are taken as three objectives. Main aerodynamic and structural characteristics of the blades are employed as design variables. Various design requirements including strain, deflection, vibration and buckling limits are taken into account as constraints. To evaluate the aerodynamic performances and the structural behaviors, the blade element momentum (BEM theory and the finite element method (FEM are applied in the procedure. Moreover, the non-dominated sorting genetic algorithm (NSGA II, which constitutes the core of the procedure, is adapted for the multi-objective optimization of the blades. To prove the efficiency and reliability of the procedure, a commercial 1.5 MW HAWT blade is used as a case study, and a set of trade-off solutions is obtained. Compared with the original scheme, the optimization results show great improvements for the overall performance of the blade.

  9. Gender approaches to evolutionary multi-objective optimization using pre-selection of criteria

    Science.gov (United States)

    Kowalczuk, Zdzisław; Białaszewski, Tomasz

    2018-01-01

    A novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identify the (aggregate) nature of optimality of individuals (solutions). This identification is drawn out of the fitness of individuals and applied during parental crossover in the processes of evolutionary multi-objective optimization (EMOO). The article introduces the principles of the genetic-gender approach (GGA) and virtual gender approach (VGA), which are not just evolutionary techniques, but constitute a completely new rule (philosophy) for use in solving MOO tasks. The proposed approaches are validated against principal representatives of the EMOO algorithms of the state of the art in solving benchmark problems in the light of recognized EC performance criteria. The research shows the superiority of the gender approach in terms of effectiveness, reliability, transparency, intelligibility and MOO problem simplification, resulting in the great usefulness and practicability of GGA and VGA. Moreover, an important feature of GGA and VGA is that they alleviate the 'curse' of dimensionality typical of many engineering designs.

  10. Multi-objective exergy-based optimization of a polygeneration energy system using an evolutionary algorithm

    International Nuclear Information System (INIS)

    Ahmadi, Pouria; Rosen, Marc A.; Dincer, Ibrahim

    2012-01-01

    A comprehensive thermodynamic modeling and optimization is reported of a polygeneration energy system for the simultaneous production of heating, cooling, electricity and hot water from a common energy source. This polygeneration system is composed of four major parts: gas turbine (GT) cycle, Rankine cycle, absorption cooling cycle and domestic hot water heater. A multi-objective optimization method based on an evolutionary algorithm is applied to determine the best design parameters for the system. The two objective functions utilized in the analysis are the total cost rate of the system, which is the cost associated with fuel, component purchasing and environmental impact, and the system exergy efficiency. The total cost rate of the system is minimized while the cycle exergy efficiency is maximized by using an evolutionary algorithm. To provide a deeper insight, the Pareto frontier is shown for multi-objective optimization. In addition, a closed form equation for the relationship between exergy efficiency and total cost rate is derived. Finally, a sensitivity analysis is performed to assess the effects of several design parameters on the system total exergy destruction rate, CO 2 emission and exergy efficiency.

  11. Optimization of urban spatial development against flooding and other climate risks, and wider sustainability objectives

    Directory of Open Access Journals (Sweden)

    Caparros-Midwood Daniel

    2016-01-01

    Full Text Available A spatial optimization framework has been developed to help urban areas mitigate climate risks such as flooding and to curb resource use and greenhouse gas emissions. Measures required to address these issues often conflict with each other, for example more compact cities typically use less energy for transportation but increase runoff from high intensity rainfall events. Balancing potential trade-offs and maximizing synergies between these risks and vulnerabilities is therefore a multi-dimensional, spatial, challenge for urban planners. A spatial optimization framework is used to optimize the following objectives to minimize: (1 risk from heat waves; (2 risk from flooding; (3 the distance of new development to the current central business district; (4 urban sprawl to prevent increased travel costs; and (5 the development of green-space. The framework is applied to a real case study in the North East of England. From an initial configuration, alternative spatial configurations are tested against these objectives and the spatial pattern is evolved over successive generations to search for spatially optimum configurations. The resulting solutions provide planners with a range of robust spatial development patterns known to be best trade-offs which mitigate conflicts between risk and sustainability objectives.

  12. Design optimization of single mixed refrigerant natural gas liquefaction process using the particle swarm paradigm with nonlinear constraints

    International Nuclear Information System (INIS)

    Khan, Mohd Shariq; Lee, Moonyong

    2013-01-01

    The particle swarm paradigm is employed to optimize single mixed refrigerant natural gas liquefaction process. Liquefaction design involves multivariable problem solving and non-optimal execution of these variables can waste energy and contribute to process irreversibilities. Design optimization requires these variables to be optimized simultaneously; minimizing the compression energy requirement is selected as the optimization objective. Liquefaction is modeled using Honeywell UniSim Design ™ and the resulting rigorous model is connected with the particle swarm paradigm coded in MATLAB. Design constraints are folded into the objective function using the penalty function method. Optimization successfully improved efficiency by reducing the compression energy requirement by ca. 10% compared with the base case. -- Highlights: ► The particle swarm paradigm (PSP) is employed for design optimization of SMR NG liquefaction process. ► Rigorous SMR process model based on UniSim is connected with PSP coded in MATLAB. ► Stochastic features of PSP give more confidence in the optimality of complex nonlinear problems. ► Optimization with PSP notably improves energy efficiency of the SMR process.

  13. A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

    Directory of Open Access Journals (Sweden)

    Silvio Rodrigues

    2016-03-01

    Full Text Available Current offshore wind farms (OWFs design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases.

  14. Multi-objective design optimization and control of magnetorheological fluid brakes for automotive applications

    Science.gov (United States)

    Shamieh, Hadi; Sedaghati, Ramin

    2017-12-01

    The magnetorheological brake (MRB) is an electromechanical device that generates a retarding torque through employing magnetorheological (MR) fluids. The objective of this paper is to design, optimize and control an MRB for automotive applications considering. The dynamic range of a disk-type MRB expressing the ratio of generated toque at on and off states has been formulated as a function of the rotational speed, geometrical and material properties, and applied electrical current. Analytical magnetic circuit analysis has been conducted to derive the relation between magnetic field intensity and the applied electrical current as a function of the MRB geometrical and material properties. A multidisciplinary design optimization problem has then been formulated to identify the optimal brake geometrical parameters to maximize the dynamic range and minimize the response time and weight of the MRB under weight, size and magnetic flux density constraints. The optimization problem has been solved using combined genetic and sequential quadratic programming algorithms. Finally, the performance of the optimally designed MRB has been investigated in a quarter vehicle model. A PID controller has been designed to regulate the applied current required by the MRB in order to improve vehicle’s slipping on different road conditions.

  15. Multi objective optimization model for minimizing production cost and environmental impact in CNC turning process

    Science.gov (United States)

    Widhiarso, Wahyu; Rosyidi, Cucuk Nur

    2018-02-01

    Minimizing production cost in a manufacturing company will increase the profit of the company. The cutting parameters will affect total processing time which then will affect the production cost of machining process. Besides affecting the production cost and processing time, the cutting parameters will also affect the environment. An optimization model is needed to determine the optimum cutting parameters. In this paper, we develop an optimization model to minimize the production cost and the environmental impact in CNC turning process. The model is used a multi objective optimization. Cutting speed and feed rate are served as the decision variables. Constraints considered are cutting speed, feed rate, cutting force, output power, and surface roughness. The environmental impact is converted from the environmental burden by using eco-indicator 99. Numerical example is given to show the implementation of the model and solved using OptQuest of Oracle Crystal Ball software. The results of optimization indicate that the model can be used to optimize the cutting parameters to minimize the production cost and the environmental impact.

  16. The Effect of Aerodynamic Evaluators on the Multi-Objective Optimization of Flatback Airfoils

    Science.gov (United States)

    Miller, M.; Slew, K. Lee; Matida, E.

    2016-09-01

    With the long lengths of today's wind turbine rotor blades, there is a need to reduce the mass, thereby requiring stiffer airfoils, while maintaining the aerodynamic efficiency of the airfoils, particularly in the inboard region of the blade where structural demands are highest. Using a genetic algorithm, the multi-objective aero-structural optimization of 30% thick flatback airfoils was systematically performed for a variety of aerodynamic evaluators such as lift-to-drag ratio (Cl/Cd), torque (Ct), and torque-to-thrust ratio (Ct/Cn) to determine their influence on airfoil shape and performance. The airfoil optimized for Ct possessed a 4.8% thick trailing-edge, and a rather blunt leading-edge region which creates high levels of lift and correspondingly, drag. It's ability to maintain similar levels of lift and drag under forced transition conditions proved it's insensitivity to roughness. The airfoil optimized for Cl/Cd displayed relatively poor insensitivity to roughness due to the rather aft-located free transition points. The Ct/Cn optimized airfoil was found to have a very similar shape to that of the Cl/Cd airfoil, with a slightly more blunt leading-edge which aided in providing higher levels of lift and moderate insensitivity to roughness. The influence of the chosen aerodynamic evaluator under the specified conditions and constraints in the optimization of wind turbine airfoils is shown to have a direct impact on the airfoil shape and performance.

  17. Balancing Exploration, Uncertainty Representation and Computational Time in Many-Objective Reservoir Policy Optimization

    Science.gov (United States)

    Zatarain-Salazar, J.; Reed, P. M.; Quinn, J.; Giuliani, M.; Castelletti, A.

    2016-12-01

    As we confront the challenges of managing river basin systems with a large number of reservoirs and increasingly uncertain tradeoffs impacting their operations (due to, e.g. climate change, changing energy markets, population pressures, ecosystem services, etc.), evolutionary many-objective direct policy search (EMODPS) solution strategies will need to address the computational demands associated with simulating more uncertainties and therefore optimizing over increasingly noisy objective evaluations. Diagnostic assessments of state-of-the-art many-objective evolutionary algorithms (MOEAs) to support EMODPS have highlighted that search time (or number of function evaluations) and auto-adaptive search are key features for successful optimization. Furthermore, auto-adaptive MOEA search operators are themselves sensitive to having a sufficient number of function evaluations to learn successful strategies for exploring complex spaces and for escaping from local optima when stagnation is detected. Fortunately, recent parallel developments allow coordinated runs that enhance auto-adaptive algorithmic learning and can handle scalable and reliable search with limited wall-clock time, but at the expense of the total number of function evaluations. In this study, we analyze this tradeoff between parallel coordination and depth of search using different parallelization schemes of the Multi-Master Borg on a many-objective stochastic control problem. We also consider the tradeoff between better representing uncertainty in the stochastic optimization, and simplifying this representation to shorten the function evaluation time and allow for greater search. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple competing objectives for hydropower production, urban water supply, recreation and environmental flows need to be balanced. Our results provide guidance for balancing exploration, uncertainty, and computational demands when using the EMODPS

  18. Impact of fuel cell power plants on multi-objective optimal operation management of distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Niknam, T. [Electrical and Electronic Engineering Department, Shiraz University of Technology, Shiraz (Iran, Islamic Republic of); Zeinoddini-Meymand, H. [Islamic Azad University, Kerman Branch, Kerman (Iran, Islamic Republic of)

    2012-06-15

    This paper presents an interactive fuzzy satisfying method based on hybrid modified honey bee mating optimization and differential evolution (MHBMO-DE) to solve the multi-objective optimal operation management (MOOM) problem, which can be affected by fuel cell power plants (FCPPs). The objective functions are to minimize total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources, and deviation of bus voltages. A new interactive fuzzy satisfying method is presented to solve the multi-objective problem by assuming that the decision-maker (DM) has fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting the corresponding membership functions. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used to solve this problem because of their independence from type of the objective function and constraints. Recently researchers have presented a new evolutionary method called honey bee mating optimization (HBMO) algorithm. Original HBMO often converges to local optima, in order to overcome this shortcoming, we propose a new method that improves the mating process and also, combines the modified HBMO with DE algorithm. Numerical results for a distribution test system have been presented to illustrate the performance and applicability of the proposed method. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  19. Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making

    OpenAIRE

    Rajesh Kumar; S.C. Kaushik; Raj Kumar; Ranjana Hans

    2016-01-01

    Brayton heat engine model is developed in MATLAB simulink environment and thermodynamic optimization based on finite time thermodynamic analysis along with multiple criteria is implemented. The proposed work investigates optimal values of various decision variables that simultaneously optimize power output, thermal efficiency and ecological function using evolutionary algorithm based on NSGA-II. Pareto optimal frontier between triple and dual objectives is obtained and best optimal value is s...

  20. Multi-objective shape optimization of runner blade for Kaplan turbine

    International Nuclear Information System (INIS)

    Power machines LMZ, Saint Petersburg (Russian Federation))" data-affiliation=" (OJSC Power machines LMZ, Saint Petersburg (Russian Federation))" >Semenova, A; Power machines LMZ, Saint Petersburg (Russian Federation))" data-affiliation=" (OJSC Power machines LMZ, Saint Petersburg (Russian Federation))" >Pylev, I; Chirkov, D; Lyutov, A; Chemy, S; Skorospelov, V

    2014-01-01

    Automatic runner shape optimization based on extensive CFD analysis proved to be a useful design tool in hydraulic turbomachinery. Previously the authors developed an efficient method for Francis runner optimization. It was successfully applied to the design of several runners with different specific speeds. In present work this method is extended to the task of a Kaplan runner optimization. Despite of relatively simpler blade shape, Kaplan turbines have several features, complicating the optimization problem. First, Kaplan turbines normally operate in a wide range of discharges, thus CFD analysis of each variant of the runner should be carried out for several operation points. Next, due to a high specific speed, draft tube losses have a great impact on the overall turbine efficiency, and thus should be accurately evaluated. Then, the flow in blade tip and hub clearances significantly affects the velocity profile behind the runner and draft tube behavior. All these features are accounted in the present optimization technique. Parameterization of runner blade surface using 24 geometrical parameters is described in details. For each variant of runner geometry steady state three-dimensional turbulent flow computations are carried out in the domain, including wicket gate, runner, draft tube, blade tip and hub clearances. The objectives are maximization of efficiency in best efficiency and high discharge operation points, with simultaneous minimization of cavitation area on the suction side of the blade. Multiobjective genetic algorithm is used for the solution of optimization problem, requiring the analysis of several thousands of runner variants. The method is applied to optimization of runner shape for several Kaplan turbines with different heads

  1. Multi-objective shape optimization of runner blade for Kaplan turbine

    Science.gov (United States)

    Semenova, A.; Chirkov, D.; Lyutov, A.; Chemy, S.; Skorospelov, V.; Pylev, I.

    2014-03-01

    Automatic runner shape optimization based on extensive CFD analysis proved to be a useful design tool in hydraulic turbomachinery. Previously the authors developed an efficient method for Francis runner optimization. It was successfully applied to the design of several runners with different specific speeds. In present work this method is extended to the task of a Kaplan runner optimization. Despite of relatively simpler blade shape, Kaplan turbines have several features, complicating the optimization problem. First, Kaplan turbines normally operate in a wide range of discharges, thus CFD analysis of each variant of the runner should be carried out for several operation points. Next, due to a high specific speed, draft tube losses have a great impact on the overall turbine efficiency, and thus should be accurately evaluated. Then, the flow in blade tip and hub clearances significantly affects the velocity profile behind the runner and draft tube behavior. All these features are accounted in the present optimization technique. Parameterization of runner blade surface using 24 geometrical parameters is described in details. For each variant of runner geometry steady state three-dimensional turbulent flow computations are carried out in the domain, including wicket gate, runner, draft tube, blade tip and hub clearances. The objectives are maximization of efficiency in best efficiency and high discharge operation points, with simultaneous minimization of cavitation area on the suction side of the blade. Multiobjective genetic algorithm is used for the solution of optimization problem, requiring the analysis of several thousands of runner variants. The method is applied to optimization of runner shape for several Kaplan turbines with different heads.

  2. Neural evidence for competition-mediated suppression in the perception of a single object.

    Science.gov (United States)

    Cacciamani, Laura; Scalf, Paige E; Peterson, Mary A

    2015-11-01

    Multiple objects compete for representation in visual cortex. Competition may also underlie the perception of a single object. Computational models implement object perception as competition between units on opposite sides of a border. The border is assigned to the winning side, which is perceived as an object (or "figure"), whereas the other side is perceived as a shapeless ground. Behavioral experiments suggest that the ground is inhibited to a degree that depends on the extent to which it competed for object status, and that this inhibition is relayed to low-level brain areas. Here, we used fMRI to assess activation for ground regions of task-irrelevant novel silhouettes presented in the left or right visual field (LVF or RVF) while participants performed a difficult task at fixation. Silhouettes were designed so that the insides would win the competition for object status. The outsides (grounds) suggested portions of familiar objects in half of the silhouettes and novel objects in the other half. Because matches to object memories affect the competition, these two types of silhouettes operationalized, respectively, high competition and low competition from the grounds. The results showed that activation corresponding to ground regions was reduced for high- versus low-competition silhouettes in V4, where receptive fields (RFs) are large enough to encompass the familiar objects in the grounds, and in V1/V2, where RFs are much smaller. These results support a theory of object perception involving competition-mediated ground suppression and feedback from higher to lower levels. This pattern of results was observed in the left hemisphere (RVF), but not in the right hemisphere (LVF). One explanation of the lateralized findings is that task-irrelevant silhouettes in the RVF captured attention, allowing us to observe these effects, whereas those in the LVF did not. Experiment 2 provided preliminary behavioral evidence consistent with this possibility. Copyright

  3. Femoral hip prosthesis design for Thais using multi-objective shape optimization

    International Nuclear Information System (INIS)

    Virulsri, Chanyaphan; Tangpornprasert, Pairat; Romtrairat, Parineak

    2015-01-01

    Highlights: • A multi-objective shape optimization was proposed to design hip prosthesis for Thais. • The prosthesis design was optimized in terms of safety of both cement and prosthesis. • The objective functions used the Soderberg fatigue strength formulations. • Safety factors of the cement and prosthesis are 1.200 and 1.109 respectively. • The newly designed prosthesis also fits well with chosen small-sized Thai femurs. - Abstract: The long-term success of Total Hip Arthroplasty (THA) depends largely on how well the prosthetic components fit the bones. The majority of cemented femoral hip prosthesis failures are due to aseptic loosening, which is possibly caused by cracking of the cement mantle. The strength of cement components is a function of cement mantles having adequate thickness. Since the size and shape of cemented femoral hip prostheses used in Thailand are based on designs for a Caucasian population, they do not properly conform to most Thai patients’ physical requirements. For these reasons, prostheses designed specifically for Thai patients must consider the longevity and functionality of both cement and prosthesis. The objective of this study was to discover a new design for femoral hip prostheses which is not only optimal and safe in terms of both cement and prosthesis, but also fits the selected Thai femur. This study used a small-sized Thai femoral model as a reference model for a new design. Biocompatible stainless steel 316L (SS316L) and polymethylmethacrylate (PMMA) were selected as raw materials for the prosthesis and bone cement respectively. A multi-objective shape optimization program, which is an interface between optimization C program named NSGA-II and a finite element program named ANSYS, was used to optimize longevity of femoral hip prostheses by varying shape parameters at assigned cross-sections of the selected geometry. Maximum walking loads of sixty-kilograms were applied to a finite element model for stress and

  4. Energy, exergy, economic (3E) analyses and multi-objective optimization of vapor absorption heat transformer using NSGA-II technique

    International Nuclear Information System (INIS)

    Jain, Vaibhav; Sachdeva, Gulshan

    2017-01-01

    Highlights: • Study includes energy, exergy and economic analyses of absorption heat transformer. • It addresses multi-objective optimization study using NSGA-II technique. • Total annual cost and total exergy destruction are simultaneously optimized. • Results with multi-objective optimized design are more acceptable than other. - Abstract: Present paper addresses the energy, exergy and economic (3E) analyses of absorption heat transformer (AHT) working with LiBr-H 2 O fluid pair. The heat exchangers namely absorber, condenser, evaporator, generator and solution heat exchanger are designed for the size and cost estimation of AHT. Later, the effect of operating variables is examined on the system performance, size and cost. Simulation studies showed a conflict between thermodynamic and economic performance of the system. The heat exchangers with lower investment cost showed high irreversible losses and vice versa. Thus, the operating variables of systems are determined economically as well as thermodynamically by implementing non-dominated sort genetic algorithm-II (NSGA-II) technique of multi-objective optimization. In present work, if the cost based optimized design is chosen, total exergy destruction is 2.4% higher than its minimum possible value; whereas, if total exergy based optimized design is chosen, total annual cost is 6.1% higher than its minimum possible value. On the other hands, total annual cost and total exergy destruction are only 1.0% and 0.8%, respectively more from their minimum possible values with multi-objective optimized design. Thus, the multi-objective optimized design of the AHT is best outcome than any other single-objective optimized designs.

  5. Oil Reservoir Production Optimization using Single Shooting and ESDIRK Methods

    DEFF Research Database (Denmark)

    Capolei, Andrea; Völcker, Carsten; Frydendall, Jan

    2012-01-01

    the injections and oil production such that flow is uniform in a given geological structure. Even in the case of conventional water flooding, feedback based optimal control technologies may enable higher oil recovery than with conventional operational strategies. The optimal control problems that must be solved...

  6. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

    Science.gov (United States)

    Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

  7. A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

    Science.gov (United States)

    Zheng, Y.; Chen, J.

    2017-09-01

    A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

  8. Multi-objective optimization for integrated hydro–photovoltaic power system

    International Nuclear Information System (INIS)

    Li, Fang-Fang; Qiu, Jun

    2016-01-01

    Highlights: • A model optimizing both quality and quantity of hydro/PV power was proposed. • The dimension was reduced by decoupling hydropower and PV power in time scales. • Reservoir operations have been optimized for different typical hydrological years. • Hydropower was proved to be an ideal compensating resource for PV power in nature. - Abstract: The most striking feature of the solar energy is its intermittency and instability resulting from environmental influence. Hydropower can be an ideal choice to compensate photovoltaic (PV) power since it is easy to adjust and responds rapidly with low cost. This study proposed a long-term multi-objective optimization model for integrated hydro/PV power system considering the smoothness of power output process and the total amount of annual power generation of the system simultaneously. The PV power output is firstly calculated by hourly solar radiation and temperature data, which is then taken as the boundary condition for reservoir optimization. For hydropower, due to its great adjustable capability, a month is taken as the time step to balance the simulation cost. The problem dimension is thus reduced by decoupling hydropower and PV power in time scales. The modified version of Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to optimize the multi-objective problem. The proposed model was applied to the Longyangxia hydro/PV hybrid power system in Qinghai province of China, which is supposed to be the largest hydro/PV hydropower station in the world. The results verified that the hydropower is an ideal compensation resource for the PV power in nature, especially in wet years, when the solar radiation decreases due to rainfalls while the water resource is abundant to be allocated. The power generation potential is provided for different hydrologic years, which can be taken to evaluate the actual operations. The proposed methodology is general in that it can be used for other hydro/PV power systems

  9. A proposal of multi-objective function for submarine rigid pipelines route optimization via evolutionary algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Fernandes, D.H.; Medeiros, A.R. [Subsea7, Niteroi, RJ (Brazil); Jacob, B.P.; Lima, B.S.L.P.; Albrecht, C.H. [Universidade Federaldo Rio de Janeiro (COPPE/UFRJ), RJ (Brazil). Coordenacao de Programas de Pos-graduacao em Engenharia

    2009-07-01

    This work presents studies regarding the determination of optimal pipeline routes for offshore applications. The assembly of an objective function is presented; this function can be later associated with Evolutionary Algorithm to implement a computational tool for the automatic determination of the most advantageous pipeline route for a given scenario. This tool may reduce computational overheads, avoid mistakes with route interpretation, and minimize costs with respect to submarine pipeline design and installation. The following aspects can be considered in the assembly of the objective function: Geophysical and geotechnical data obtained from the bathymetry and sonography; the influence of the installation method, total pipeline length and number of free spans to be mitigated along the routes as well as vessel time for both cases. Case studies are presented to illustrate the use of the proposed objective function, including a sensitivity analysis intended to identify the relative influence of selected parameters in the evaluation of different routes. (author)

  10. A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants

    Energy Technology Data Exchange (ETDEWEB)

    Niknam, Taher; Meymand, Hamed Zeinoddini; Mojarrad, Hasan Doagou [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of)

    2011-05-15

    In this paper a novel Multi-objective fuzzy self adaptive hybrid particle swarm optimization (MFSAHPSO) evolutionary algorithm to solve the Multi-objective optimal operation management (MOOM) is presented. The purposes of the MOOM problem are to decrease the total electrical energy losses, the total electrical energy cost and the total pollutant emission produced by fuel cells and substation bus. Conventional algorithms used to solve the multi-objective optimization problems convert the multiple objectives into a single objective, using a vector of the user-predefined weights. In this conversion several deficiencies can be detected. For instance, the optimal solution of the algorithms depends greatly on the values of the weights and also some of the information may be lost in the conversion process and so this strategy is not expected to provide a robust solution. This paper presents a new MFSAHPSO algorithm for the MOOM problem. The proposed algorithm maintains a finite-sized repository of non-dominated solutions which gets iteratively updated in the presence of new solutions. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository, within the limits. The proposed algorithm is tested on a distribution test feeder and the results demonstrate the capabilities of the proposed approach, to generate true and well-distributed Pareto-optimal non-dominated solutions of the MOOM problem. (author)

  11. Tabletop single-shot extreme ultraviolet Fourier transform holography of an extended object.

    Science.gov (United States)

    Malm, Erik B; Monserud, Nils C; Brown, Christopher G; Wachulak, Przemyslaw W; Xu, Huiwen; Balakrishnan, Ganesh; Chao, Weilun; Anderson, Erik; Marconi, Mario C

    2013-04-22

    We demonstrate single and multi-shot Fourier transform holography with the use of a tabletop extreme ultraviolet laser. The reference wave was produced by a Fresnel zone plate with a central opening that allowed the incident beam to illuminate the sample directly. The high reference wave intensity allows for larger objects to be imaged compared to mask-based lensless Fourier transform holography techniques. We obtain a spatial resolution of 169 nm from a single laser pulse and a resolution of 128 nm from an accumulation of 20 laser pulses for an object ~11x11μm(2) in size. This experiment utilized a tabletop extreme ultraviolet laser that produces a highly coherent ~1.2 ns laser pulse at 46.9 nm wavelength.

  12. Optimization of a Finned Shell and Tube Heat Exchanger Using a Multi-Objective Optimization Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Heidar Sadeghzadeh

    2015-08-01

    Full Text Available Heat transfer rate and cost significantly affect designs of shell and tube heat exchangers. From the viewpoint of engineering, an optimum design is obtained via maximum heat transfer rate and minimum cost. Here, an analysis of a radial, finned, shell and tube heat exchanger is carried out, considering nine design parameters: tube arrangement, tube diameter, tube pitch, tube length, number of tubes, fin height, fin thickness, baffle spacing ratio and number of fins per unit length of tube. The “Delaware modified” technique is used to determine heat transfer coefficients and the shell-side pressure drop. In this technique, the baffle cut is 20 percent and the baffle ratio limits range from 0.2 to 0.4. The optimization of the objective functions (maximum heat transfer rate and minimum total cost is performed using a non-dominated sorting genetic algorithm (NSGA-II, and compared against a one-objective algorithm, to find the best solutions. The results are depicted as a set of solutions on a Pareto front, and show that the heat transfer rate ranges from 3517 to 7075 kW. Also, the minimum and maximum objective functions are specified, allowing the designer to select the best points among these solutions based on requirements. Additionally, variations of shell-side pressure drop with total cost are depicted, and indicate that the pressure drop ranges from 3.8 to 46.7 kPa.

  13. Multi-objective design optimization of the transverse gaseous jet in supersonic flows

    Science.gov (United States)

    Huang, Wei; Yang, Jun; Yan, Li

    2014-01-01

    The mixing process between the injectant and the supersonic crossflow is one of the important issues for the design of the scramjet engine, and the efficiency mixing has a great impact on the improvement of the combustion efficiency. A hovering vortex is formed between the separation region and the barrel shock wave, and this may be induced by the large negative density gradient. The separation region provides a good mixing area for the injectant and the subsonic boundary layer. In the current study, the transverse injection flow field with a freestream Mach number of 3.5 has been optimized by the non-dominated sorting genetic algorithm (NSGA II) coupled with the Kriging surrogate model; and the variance analysis method and the extreme difference analysis method have been employed to evaluate the values of the objective functions. The obtained results show that the jet-to-crossflow pressure ratio is the most important design variable for the transverse injection flow field, and the injectant molecular weight and the slot width should be considered for the mixing process between the injectant and the supersonic crossflow. There exists an optimal penetration height for the mixing efficiency, and its value is about 14.3 mm in the range considered in the current study. The larger penetration height provides a larger total pressure loss, and there must be a tradeoff between these two objection functions. In addition, this study demonstrates that the multi-objective design optimization method with the data mining technique can be used efficiently to explore the relationship between the design variables and the objective functions.

  14. Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

    Science.gov (United States)

    Zatarain Salazar, Jazmin; Reed, Patrick M.; Quinn, Julianne D.; Giuliani, Matteo; Castelletti, Andrea

    2017-11-01

    Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a

  15. Multi-objective optimization integrated with life cycle assessment for rainwater harvesting systems

    Science.gov (United States)

    Li, Yi; Huang, Youyi; Ye, Quanliang; Zhang, Wenlong; Meng, Fangang; Zhang, Shanxue

    2018-03-01

    The major limitation of optimization models applied previously for rainwater harvesting (RWH) systems is the systematic evaluation of environmental and human health impacts across all the lifecycle stages. This study integrated life cycle assessment (LCA) into a multi-objective optimization model to optimize the construction areas of green rooftops, porous pavements and green lands in Beijing of China, considering the trade-offs among 24 h-interval RWH volume (QR), stormwater runoff volume control ratio (R), economic cost (EC), and environmental impacts (EI). Eleven life cycle impact indicators were assessed with a functional unit of 10,000 m2 of RWH construction areas. The LCA results showed that green lands performed the smallest lifecycle impacts of all assessment indicators, in contrast, porous pavements showed the largest impact values except Abiotic Depletion Potential (ADP) elements. Based on the standardization results, ADP fossil was chosen as the representative indicator for the calculation of EI objective in multi-objective optimization model due to its largest value in all RWH systems lifecycle. The optimization results for QR, R, EC and EI were 238.80 million m3, 78.5%, 66.68 billion RMB Yuan, and 1.05E + 16 MJ, respectively. After the construction of optimal RWH system, 14.7% of annual domestic water consumption and 78.5% of maximum daily rainfall would be supplied and controlled in Beijing, respectively, which would make a great contribution to reduce the stress of water scarcity and water logging problems. Green lands have been the first choice for RWH in Beijing according to the capacity of rainwater harvesting and less environmental and human impacts. Porous pavements played a good role in water logging alleviation (R for 67.5%), however, did not show a large construction result in this study due to the huge ADP fossil across the lifecycle. Sensitivity analysis revealed the daily maximum precipitation to be key factor for the robustness of the

  16. Structure Optimization of Stand-Alone Renewable Power Systems Based on Multi Object Function

    Directory of Open Access Journals (Sweden)

    Jae-Hoon Cho

    2016-08-01

    Full Text Available This paper presents a methodology for the size optimization of a stand-alone hybrid PV/wind/diesel/battery system while considering the following factors: total annual cost (TAC, loss of power supply probability (LPSP, and the fuel cost of the diesel generator required by the user. A new optimization algorithm and an object function (including a penalty method are also proposed; these assist with designing the best structure for a hybrid system satisfying the constraints. In hybrid energy system sources such as photovoltaic (PV, wind, diesel, and energy storage devices are connected as an electrical load supply. Because the power produced by PV and wind turbine sources is dependent on the variation of the resources (sun and wind and the load demand fluctuates, such a hybrid system must be able to satisfy the load requirements at any time and store the excess energy for use in deficit conditions. Therefore, reliability and cost are the two main criteria when designing a stand-alone hybrid system. Moreover, the operation of a diesel generator is important to achieve greater reliability. In this paper, TAC, LPSP, and the fuel cost of the diesel generator are considered as the objective variables and a hybrid teaching–learning-based optimization algorithm is proposed and used to choose the best structure of a stand-alone hybrid PV/wind/diesel/battery system. Simulation results from MATLAB support the effectiveness of the proposed method and confirm that it is more efficient than conventional methods.

  17. A derived heuristics based multi-objective optimization procedure for micro-grid scheduling

    Science.gov (United States)

    Li, Xin; Deb, Kalyanmoy; Fang, Yanjun

    2017-06-01

    With the availability of different types of power generators to be used in an electric micro-grid system, their operation scheduling as the load demand changes with time becomes an important task. Besides satisfying load balance constraints and the generator's rated power, several other practicalities, such as limited availability of grid power and restricted ramping of power output from generators, must all be considered during the operation scheduling process, which makes it difficult to decide whether the optimization results are accurate and satisfactory. In solving such complex practical problems, heuristics-based customized optimization algorithms are suggested. However, due to nonlinear and complex interactions of variables, it is difficult to come up with heuristics in such problems off-hand. In this article, a two-step strategy is proposed in which the first task deciphers important heuristics about the problem and the second task utilizes the derived heuristics to solve the original problem in a computationally fast manner. Specifically, the specific operation scheduling is considered from a two-objective (cost and emission) point of view. The first task develops basic and advanced level knowledge bases offline from a series of prior demand-wise optimization runs and then the second task utilizes them to modify optimized solutions in an application scenario. Results on island and grid connected modes and several pragmatic formulations of the micro-grid operation scheduling problem clearly indicate the merit of the proposed two-step procedure.

  18. Multi-objective optimization of Stirling engine using non-ideal adiabatic method

    International Nuclear Information System (INIS)

    Toghyani, Somayeh; Kasaeian, Alibakhsh; Ahmadi, Mohammad H.

    2014-01-01

    Highlights: • A multi-objective optimization is carried out for a Stirling engine. • The methods of TOPSIS, Fuzzy, and LINMAP are compared with each other in aspect of optimization. • The results are compared with the previous works for checking the model improvement. • A proper improvement is observed using TOPSIS when comparing with the others. - Abstract: In the recent years, remarkable attention is drawn to Stirling engine due to noticeable advantages, for instance a lot of resources such as biomass, fossil fuels and solar energy can be applied as heat source. Great numbers of studies are conducted on Stirling engines and non-ideal adiabatic method is one of them. In the present study, the efficiency and the power loss due to pressure drop into the heat exchangers are optimized for a Stirling system using non-ideal adiabatic analysis and the second-version Non-dominated Sorting Genetic Algorithm. The optimized answers are chosen from the results using three decision-making methods. The applied methods were compared at last and the best results were obtained for the technique for order preference by similarity to ideal solution decision making method

  19. Multi-objective optimization of HVAC system with an evolutionary computation algorithm

    International Nuclear Information System (INIS)

    Kusiak, Andrew; Tang, Fan; Xu, Guanglin

    2011-01-01

    A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables - supply air temperature and supply air duct static pressure set points - are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system. -- Highlights: → A data-mining approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system is presented. → The data used in the project has been collected from an experiment conducted at an energy research facility. → The approach presented in the paper leads to accomplishing significant energy savings without compromising the indoor air quality. → The energy savings are accomplished by computing set points for the supply air temperature and the supply air duct static pressure.

  20. Multi-objective optimization and grey relational analysis on configurations of organic Rankine cycle

    International Nuclear Information System (INIS)

    Wang, Y.Z.; Zhao, J.; Wang, Y.; An, Q.S.

    2017-01-01

    Highlights: • Pareto frontier is an effective way to make comprehensive comparison of ORC. • Comprehensive performance from energy and economics of basic ORC is the best. • R141b shows the best comprehensive performance from energy and economics. - Abstract: Concerning the comprehensive performance of organic Rankine cycle (ORC), comparisons and optimizations on 3 different configurations of ORC (basic, regenerative and extractive ORCs) are investigated in this paper. Medium-temperature geothermal water is used for comparing the influence of configurations, working fluids and operating parameters on different evaluation criteria. Different evaluation and optimization methods are adopted in evaluation of ORCs to obtain the one with the best comprehensive performance, such as exergoeconomic analysis, bi-objective optimization and grey relational analysis. The results reveal that the basic ORC performs the best among these 3 ORCs in terms of comprehensive thermodynamic and economic performances when using R245fa and driven by geothermal water at 150 °C. Furthermore, R141b shows the best comprehensive performance among 14 working fluids based on the Pareto frontier solutions without considering safe factors. Meanwhile, R141b is the best among all 14 working fluids with the optimal comprehensive performance when regarding all the evaluation criteria as equal by using grey relational analysis.

  1. Many-objective Groundwater Monitoring Network Design Using Bias-Aware Ensemble Kalman Filtering and Evolutionary Optimization

    Science.gov (United States)

    Kollat, J. B.; Reed, P. M.

    2009-12-01

    This study contributes the ASSIST (Adaptive Strategies for Sampling in Space and Time) framework for improving long-term groundwater monitoring decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The ASSIST framework combines contaminant flow-and-transport modeling, bias-aware ensemble Kalman filtering (EnKF) and many-objective evolutionary optimization. Our goal in this work is to provide decision makers with a fuller understanding of the information tradeoffs they must confront when performing long-term groundwater monitoring network design. Our many-objective analysis considers up to 6 design objectives simultaneously and consequently synthesizes prior monitoring network design methodologies into a single, flexible framework. This study demonstrates the ASSIST framework using a tracer study conducted within a physical aquifer transport experimental tank located at the University of Vermont. The tank tracer experiment was extensively sampled to provide high resolution estimates of tracer plume behavior. The simulation component of the ASSIST framework consists of stochastic ensemble flow-and-transport predictions using ParFlow coupled with the Lagrangian SLIM transport model. The ParFlow and SLIM ensemble predictions are conditioned with tracer observations using a bias-aware EnKF. The EnKF allows decision makers to enhance plume transport predictions in space and time in the presence of uncertain and biased model predictions by conditioning them on uncertain measurement data. In this initial demonstration, the position and frequency of sampling were optimized to: (i) minimize monitoring cost, (ii) maximize information provided to the EnKF, (iii) minimize failure to detect the tracer, (iv) maximize the detection of tracer flux, (v) minimize error in quantifying tracer mass, and (vi) minimize error in quantifying the moment of the tracer plume. The results demonstrate that the many-objective problem

  2. Application of evolution strategy algorithm for optimization of a single-layer sound absorber

    Directory of Open Access Journals (Sweden)

    Morteza Gholamipoor

    2014-12-01

    Full Text Available Depending on different design parameters and limitations, optimization of sound absorbers has always been a challenge in the field of acoustic engineering. Various methods of optimization have evolved in the past decades with innovative method of evolution strategy gaining more attention in the recent years. Based on their simplicity and straightforward mathematical representations, single-layer absorbers have been widely used in both engineering and industrial applications and an optimized design for these absorbers has become vital. In the present study, the method of evolution strategy algorithm is used for optimization of a single-layer absorber at both a particular frequency and an arbitrary frequency band. Results of the optimization have been compared against different methods of genetic algorithm and penalty functions which are proved to be favorable in both effectiveness and accuracy. Finally, a single-layer absorber is optimized in a desired range of frequencies that is the main goal of an industrial and engineering optimization process.

  3. Single-Grasp Object Classification and Feature Extraction with Simple Robot Hands and Tactile Sensors.

    Science.gov (United States)

    Spiers, Adam J; Liarokapis, Minas V; Calli, Berk; Dollar, Aaron M

    2016-01-01

    Classical robotic approaches to tactile object identification often involve rigid mechanical grippers, dense sensor arrays, and exploratory procedures (EPs). Though EPs are a natural method for humans to acquire object information, evidence also exists for meaningful tactile property inference from brief, non-exploratory motions (a 'haptic glance'). In this work, we implement tactile object identification and feature extraction techniques on data acquired during a single, unplanned grasp with a simple, underactuated robot hand equipped with inexpensive barometric pressure sensors. Our methodology utilizes two cooperating schemes based on an advanced machine learning technique (random forests) and parametric methods that estimate object properties. The available data is limited to actuator positions (one per two link finger) and force sensors values (eight per finger). The schemes are able to work both independently and collaboratively, depending on the task scenario. When collaborating, the results of each method contribute to the other, improving the overall result in a synergistic fashion. Unlike prior work, the proposed approach does not require object exploration, re-grasping, grasp-release, or force modulation and works for arbitrary object start positions and orientations. Due to these factors, the technique may be integrated into practical robotic grasping scenarios without adding time or manipulation overheads.

  4. A Single Unexpected Change in Target- but Not Distractor Motion Impairs Multiple Object Tracking

    Directory of Open Access Journals (Sweden)

    Hauke S. Meyerhoff

    2013-02-01

    Full Text Available Recent research addresses the question whether motion information of multiple objects contributes to maintaining a selection of objects across a period of motion. Here, we investigate whether target and/or distractor motion information is used during attentive tracking. We asked participants to track four objects and changed either the motion direction of targets, the motion direction of distractors, neither, or both during a brief flash in the middle of a tracking interval. We observed that a single direction change of targets is sufficient to impair tracking performance. In contrast, changing the motion direction of distractors had no effect on performance. This indicates that target- but not distractor motion information is evaluated during tracking.

  5. An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

    Directory of Open Access Journals (Sweden)

    Xuanhu He

    2015-03-01

    Full Text Available Optimal power flow (OPF objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.

  6. The Combined Multi-objective Optimization Design for a Light Guide Rod

    International Nuclear Information System (INIS)

    Yang, Yu-Sen; Fung, Rong-Fong; Shih, Chun-Yao; Chien, Hong-Yao

    2013-01-01

    The light guide rod (LGR) has been popularly used for the vehicles, and the automobile lamp industries need mass production to match this trend. This paper aims to develop a systemic way to find the best parameters' combination for the LGR, and the parameters are usually restricted to some levels and random values. In this paper, the LGR example with two optical performances of illuminance flux and uniformity is to be optimized by use of the real-coded genetic algorithm (RGA) and grey relational analysis (GRA). The illuminance flux and uniformity of the best parameters' combination are obtained and compared with the initial set. Comparisons with Taguchi-Grey can improve 5% of gain and comparisons with Pareto genetic algorithm (PaGA) can improve 1.7% of gain. The combined multi-objective optimization can saving 7% time and it is found that the new proposed method has positive gains in performances.

  7. Multi-objective optimization approach for cost management during product design at the conceptual phase

    Science.gov (United States)

    Durga Prasad, K. G.; Venkata Subbaiah, K.; Narayana Rao, K.

    2014-03-01

    The effective cost management during the conceptual design phase of a product is essential to develop a product with minimum cost and desired quality. The integration of the methodologies of quality function deployment (QFD), value engineering (VE) and target costing (TC) could be applied to the continuous improvement of any product during product development. To optimize customer satisfaction and total cost of a product, a mathematical model is established in this paper. This model integrates QFD, VE and TC under multi-objective optimization frame work. A case study on domestic refrigerator is presented to show the performance of the proposed model. Goal programming is adopted to attain the goals of maximum customer satisfaction and minimum cost of the product.

  8. Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.

    Science.gov (United States)

    Feng, Yen-Yi; Wu, I-Chin; Chen, Tzu-Li

    2017-03-01

    The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

  9. Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system

    International Nuclear Information System (INIS)

    Sayyaadi, Hoseyn; Babaie, Meisam; Farmani, Mohammad Reza

    2011-01-01

    Multi-objective optimization for design of a benchmark cogeneration system namely as the CGAM cogeneration system is performed. In optimization approach, Exergetic, Exergoeconomic and Environmental objectives are considered, simultaneously. In this regard, the set of Pareto optimal solutions known as the Pareto frontier is obtained using the MOPSO (multi-objective particle swarm optimizer). The exergetic efficiency as an exergetic objective is maximized while the unit cost of the system product and the cost of the environmental impact respectively as exergoeconomic and environmental objectives are minimized. Economic model which is utilized in the exergoeconomic analysis is built based on both simple model (used in original researches of the CGAM system) and the comprehensive modeling namely as TTR (total revenue requirement) method (used in sophisticated exergoeconomic analysis). Finally, a final optimal solution from optimal set of the Pareto frontier is selected using a fuzzy decision-making process based on the Bellman-Zadeh approach and results are compared with corresponding results obtained in a traditional decision-making process. Further, results are compared with the corresponding performance of the base case CGAM system and optimal designs of previous works and discussed. -- Highlights: → A multi-objective optimization approach has been implemented in optimization of a benchmark cogeneration system. → Objective functions based on the environmental impact evaluation, thermodynamic and economic analysis are obtained and optimized. → Particle swarm optimizer implemented and its robustness is compared with NSGA-II. → A final optimal configuration is found using various decision-making approaches. → Results compared with previous works in the field.

  10. A multi objective optimization of gear cutting in WEDM of Inconel 718 using TOPSIS method

    Directory of Open Access Journals (Sweden)

    K.D. Mohapatra

    2017-07-01

    Full Text Available The present paper deals with the experimental analysis and multi objective optimization of gear cutting process of Inconel 718 using WEDM. The objective of the present work is to optimize the parameters in order to maximize the material removal rate and minimize the kerf in a gear cutting process to get the optimum value. The MRR and kerf play a major role in optimizing the parameters in WEDM process. The experiment is carried out in the wire EDM machine using brass wire as the electrode, Inconel 718 as the work-piece material and distilled water as the dielectric. The design array is created by using Design of Experiment in a Taguchi L16 orthogonal array repeated once. The gear has a base diameter of 20 mm, addendum diameter of 22.5 mm and a pressure angle of 20º with 16 numbers of teeth. The machining operation is carried out by taking 3 input parameters at 4 different levels each. The output parameters such as Material Removal rate and Kerf width were obtained and optimized using TOPSIS method to know the optimum setting. Microstructural analysis of both material and wire were studied to know the various defects during the machining operation. Various plots were obtained to know the effects of the process parameters in WEDM. A regression model was also obtained to validate the statistical model values with the experimental. ANOVA table and Response table were carried out to know the significant parameters and rank respectively in the Wire EDM process. Surface roughness, Addendum and Tooth width of gears were also found out at the optimum settings. The optimum setting of the gear obtained can be used to produce high quality gears and can also be applied for future findings.

  11. Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

    Science.gov (United States)

    Liu, Qingshan; Wang, Jun

    2011-04-01

    This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

  12. A Risk-Based Multi-Objective Optimization Concept for Early-Warning Monitoring Networks

    Science.gov (United States)

    Bode, F.; Loschko, M.; Nowak, W.

    2014-12-01

    Groundwater is a resource for drinking water and hence needs to be protected from contaminations. However, many well catchments include an inventory of known and unknown risk sources which cannot be eliminated, especially in urban regions. As matter of risk control, all these risk sources should be monitored. A one-to-one monitoring situation for each risk source would lead to a cost explosion and is even impossible for unknown risk sources. However, smart optimization concepts could help to find promising low-cost monitoring network designs.In this work we develop a concept to plan monitoring networks using multi-objective optimization. Our considered objectives are to maximize the probability of detecting all contaminations and the early warning time and to minimize the installation and operating costs of the monitoring network. A qualitative risk ranking is used to prioritize the known risk sources for monitoring. The unknown risk sources can neither be located nor ranked. Instead, we represent them by a virtual line of risk sources surrounding the production well.We classify risk sources into four different categories: severe, medium and tolerable for known risk sources and an extra category for the unknown ones. With that, early warning time and detection probability become individual objectives for each risk class. Thus, decision makers can identify monitoring networks which are valid for controlling the top risk sources, and evaluate the capabilities (or search for least-cost upgrade) to also cover moderate, tolerable and unknown risk sources. Monitoring networks which are valid for the remaining risk also cover all other risk sources but the early-warning time suffers.The data provided for the optimization algorithm are calculated in a preprocessing step by a flow and transport model. Uncertainties due to hydro(geo)logical phenomena are taken into account by Monte-Carlo simulations. To avoid numerical dispersion during the transport simulations we use the

  13. Design of a hybrid double-sideband/single-sideband (schlieren) objective aperture suitable for electron microscopy

    Energy Technology Data Exchange (ETDEWEB)

    Buijsse, Bart; Laarhoven, Frank M.H.M. van [FEI Company, PO Box 80066, 5600 KA Eindhoven (Netherlands); Schmid, Andreas K.; Cambie, Rossana; Cabrini, Stefano; Jin, Jian [Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720 (United States); Glaeser, Robert M., E-mail: rmglaeser@lbl.gov [Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720 (United States)

    2011-12-15

    A novel design is described for an aperture that blocks a half-plane of the electron diffraction pattern out to a desired scattering angle, and then - except for a narrow support beam - transmits all of the scattered electrons beyond that angle. Our proposed tulip-shaped design is thus a hybrid between the single-sideband (ssb) aperture, which blocks a full half-plane of the diffraction pattern, and the conventional (i.e. fully open) double-sideband (dsb) aperture. The benefits of this hybrid design include the fact that such an aperture allows one to obtain high-contrast images of weak-phase objects with the objective lens set to Scherzer defocus. We further demonstrate that such apertures can be fabricated from thin-foil materials by milling with a focused ion beam (FIB), and that such apertures are fully compatible with the requirements of imaging out to a resolution of at least 0.34 nm. As is known from earlier work with single-sideband apertures, however, the edge of such an aperture can introduce unwanted, electrostatic phase shifts due to charging. The principal requirement for using such an aperture in a routine data-collection mode is thus to discover appropriate materials, protocols for fabrication and processing and conditions of use such that the hybrid aperture remains free of charging over long periods of time. -- Highlights: Black-Right-Pointing-Pointer New objective-aperture design is proposed for imaging weak-phase objects. Black-Right-Pointing-Pointer Design produces single-sideband contrast at low spatial frequencies. Black-Right-Pointing-Pointer Design also retains Scherzer-defocus phase contrast at higher resolution. Black-Right-Pointing-Pointer Proof-of-concept results are presented for microfabricated apertures. Black-Right-Pointing-Pointer Charging of such apertures during use remains an experimental challenge.

  14. A calderón-preconditioned single source combined field integral equation for analyzing scattering from homogeneous penetrable objects

    KAUST Repository

    Valdé s, Felipe; Andriulli, Francesco P.; Bagci, Hakan; Michielssen, Eric

    2011-01-01

    A new regularized single source equation for analyzing scattering from homogeneous penetrable objects is presented. The proposed equation is a linear combination of a Calderón-preconditioned single source electric field integral equation and a

  15. A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

    Science.gov (United States)

    Ingram, James N; Howard, Ian S; Flanagan, J Randall; Wolpert, Daniel M

    2011-09-01

    Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics

  16. A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

    Directory of Open Access Journals (Sweden)

    James N Ingram

    2011-09-01

    Full Text Available Motor learning has been extensively studied using dynamic (force-field perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar

  17. The System of Simulation and Multi-objective Optimization for the Roller Kiln

    Science.gov (United States)

    Huang, He; Chen, Xishen; Li, Wugang; Li, Zhuoqiu

    It is somewhat a difficult researching problem, to get the building parameters of the ceramic roller kiln simulation model. A system integrated of evolutionary algorithms (PSO, DE and DEPSO) and computational fluid dynamics (CFD), is proposed to solve the problem. And the temperature field uniformity and the environment disruption are studied in this paper. With the help of the efficient parallel calculation, the ceramic roller kiln temperature field uniformity and the NOx emissions field have been researched in the system at the same time. A multi-objective optimization example of the industrial roller kiln proves that the system is of excellent parameter exploration capability.

  18. Application of Bayesian Decision Theory Based on Prior Information in the Multi-Objective Optimization Problem

    Directory of Open Access Journals (Sweden)

    Xia Lei

    2010-12-01

    Full Text Available General multi-objective optimization methods are hard to obtain prior information, how to utilize prior information has been a challenge. This paper analyzes the characteristics of Bayesian decision-making based on maximum entropy principle and prior information, especially in case that how to effectively improve decision-making reliability in deficiency of reference samples. The paper exhibits effectiveness of the proposed method using the real application of multi-frequency offset estimation in distributed multiple-input multiple-output system. The simulation results demonstrate Bayesian decision-making based on prior information has better global searching capability when sampling data is deficient.

  19. A performance-oriented power transformer design methodology using multi-objective evolutionary optimization.

    Science.gov (United States)

    Adly, Amr A; Abd-El-Hafiz, Salwa K

    2015-05-01

    Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper.

  20. Multi objective genetic algorithm to optimize the local heat treatment of a hardenable aluminum alloy

    Science.gov (United States)

    Piccininni, A.; Palumbo, G.; Franco, A. Lo; Sorgente, D.; Tricarico, L.; Russello, G.

    2018-05-01

    The continuous research for lightweight components for transport applications to reduce the harmful emissions drives the attention to the light alloys as in the case of Aluminium (Al) alloys, capable to combine low density with high values of the strength-to-weight ratio. Such advantages are partially counterbalanced by the poor formability at room temperature. A viable solution is to adopt a localized heat treatment by laser of the blank before the forming process to obtain a tailored distribution of material properties so that the blank can be formed at room temperature by means of conventional press machines. Such an approach has been extensively investigated for age hardenable alloys, but in the present work the attention is focused on the 5000 series; in particular, the optimization of the deep drawing process of the alloy AA5754 H32 is proposed through a numerical/experimental approach. A preliminary investigation was necessary to correctly tune the laser parameters (focus length, spot dimension) to effectively obtain the annealed state. Optimal process parameters were then obtained coupling a 2D FE model with an optimization platform managed by a multi-objective genetic algorithm. The optimal solution (i.e. able to maximize the LDR) in terms of blankholder force and extent of the annealed region was thus evaluated and validated through experimental trials. A good matching between experimental and numerical results was found. The optimal solution allowed to obtain an LDR of the locally heat treated blank larger than the one of the material either in the wrought condition (H32) either in the annealed condition (H111).

  1. Stochastic optimal control of single neuron spike trains

    DEFF Research Database (Denmark)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

    2014-01-01

    stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...

  2. Comparison between regenerative organic Rankine cycle (RORC) and basic organic Rankine cycle (BORC) based on thermoeconomic multi-objective optimization considering exergy efficiency and levelized energy cost (LEC)

    International Nuclear Information System (INIS)

    Feng, Yongqiang; Zhang, Yaning; Li, Bingxi; Yang, Jinfu; Shi, Yang

    2015-01-01

    Highlights: • The thermoeconomic comparison of regenerative RORC and BORC is investigated. • The Pareto frontier solution with bi-objective compares with the corresponding single-objective solutions. • The three-objective optimization of the RORC and BORC is studied. • The RORC owns 8.1% higher exergy efficiency and 21.1% more LEC than the BORC under the Pareto-optimal solution. - Abstract: Based on the thermoeconomic multi-objective optimization by using non-dominated sorting genetic algorithm (NSGA-II), considering both thermodynamic performance and economic factors, the thermoeconomic comparison of regenerative organic Rankine cycles (RORC) and basic organic Rankine cycles (BORC) are investigated. The effects of five key parameters including evaporator outlet temperature, condenser temperature, degree of superheat, pinch point temperature difference and degree of supercooling on the exergy efficiency and levelized energy cost (LEC) are examined. Meanwhile, the Pareto frontier solution with bi-objective for maximizing exergy efficiency and minimizing LEC is obtained and compared with the corresponding single-objective solutions. Research demonstrates that there is a significant negative correlation between thermodynamic performance and economic factors. And the optimum exergy efficiency and LEC for the Pareto-optimal solution of the RORC are 55.97% and 0.142 $/kW h, respectively, which are 8.1% higher exergy efficiency and 21.1% more LEC than that of the BORC under considered condition. Highest exergy and thermal efficiencies are accompanied with lowest net power output and worst economic performance. Furthermore, taking the net power output into account, detailed investigation on the three-objective optimization for maximizing exergy efficiency, maximizing net power output and minimizing LEC is discussed

  3. Cost Optimal Design of a Single-Phase Dry Power Transformer

    Directory of Open Access Journals (Sweden)

    Raju Basak

    2015-08-01

    Full Text Available The Dry type transformers are preferred to their oil-immersed counterparts for various reasons, particularly because their operation is hazardless. The application of dry transformers was limited to small ratings in the earlier days. But now these are being used for considerably higher ratings.  Therefore, their cost-optimal design has gained importance. This paper deals with the design procedure for achieving cost optimal design of a dry type single-phase power transformer of small rating, subject to usual design constraints on efficiency and voltage regulation. The selling cost for the transformer has been taken as the objective function. Only two key variables have been chosen, the turns/volt and the height: width ratio of window, which affects the cost function to high degrees. Other variables have been chosen on the basis of designers’ experience. Copper has been used as conductor material and CRGOS as core material to achieve higher efficiency, lower running cost and compact design. The electrical and magnetic loadings have been kept at their maximum values without violating the design constraints. The optimal solution has been obtained by the method of exhaustive search using nested loops.

  4. Multi-objective optimization of cellular scanning strategy in selective laser melting

    DEFF Research Database (Denmark)

    Ahrari, Ali; Deb, Kalyanmoy; Mohanty, Sankhya

    2017-01-01

    The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study is to dev......The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study......, the problem is a combination of combinatorial and choice optimization, which makes the problem difficult to solve. On a process simulation domain consisting of 32 cells, our multi-objective evolutionary method is able to find a set of trade-off solutions for the defined conflicting objectives, which cannot...

  5. Sensor Placement via Optimal Experiment Design in EMI Sensing of Metallic Objects

    Directory of Open Access Journals (Sweden)

    Lin-Ping Song

    2016-01-01

    Full Text Available This work, under the optimal experimental design framework, investigates the sensor placement problem that aims to guide electromagnetic induction (EMI sensing of multiple objects. We use the linearized model covariance matrix as a measure of estimation error to present a sequential experimental design (SED technique. The technique recursively minimizes data misfit to update model parameters and maximizes an information gain function for a future survey relative to previous surveys. The fundamental process of the SED seeks to increase weighted sensitivities to targets when placing sensors. The synthetic and field experiments demonstrate that SED can be used to guide the sensing process for an effective interrogation. It also can serve as a theoretic basis to improve empirical survey operation. We further study the sensitivity of the SED to the number of objects within the sensing range. The tests suggest that an appropriately overrepresented model about expected anomalies might be a feasible choice.

  6. A multi-objective particle swarm optimization for production-distribution planning in supply chain network

    Directory of Open Access Journals (Sweden)

    Alireza Pourrousta

    2012-04-01

    Full Text Available Integrated supply chain includes different components of order, production and distribution and it plays an important role on reducing the cost of manufacturing system. In this paper, an integrated supply chain in a form of multi-objective decision-making problem is presented. The proposed model of this paper considers different parameters with uncertainty using trapezoid numbers. We first implement a ranking method to covert the fuzzy model into a crisp one and using multi-objective particle swarm optimization, we solve the resulted model. The results are compared with the performance of NSGA-II for some randomly generated problems and the preliminary results indicate that the proposed model of the paper performs better than the alternative method.

  7. A fuzzy multi-objective optimization model for sustainable reverse logistics network design

    DEFF Research Database (Denmark)

    Govindan, Kannan; Paam, Parichehr; Abtahi, Amir Reza

    2016-01-01

    Decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects. Developing a model, which can simultaneously consider...... a multi-echelon multi-period multi-objective model for a sustainable reverse logistics network. To reflect all aspects of sustainability, we try to minimize the present value of costs, as well as environmental impacts, and optimize the social responsibility as objective functions of the model. In order...... these environmental, social, and economic aspects and their indicators, is an important problem for both researchers and practitioners. In this paper, we try to address this comprehensive approach by using indicators for measurement of aforementioned aspects and by applying fuzzy mathematical programming to design...

  8. Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

    Directory of Open Access Journals (Sweden)

    Man Ding

    2017-03-01

    Full Text Available This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP, which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII and multi-objective ABC (MOABC, are presented to illustrate the effectiveness and robustness of the proposed method.

  9. Single prolonged stress impairs social and object novelty recognition in rats.

    Science.gov (United States)

    Eagle, Andrew L; Fitzpatrick, Chris J; Perrine, Shane A

    2013-11-01

    Posttraumatic stress disorder (PTSD) results from exposure to a traumatic event and manifests as re-experiencing, arousal, avoidance, and negative cognition/mood symptoms. Avoidant symptoms, as well as the newly defined negative cognitions/mood, are a serious complication leading to diminished interest in once important or positive activities, such as social interaction; however, the basis of these symptoms remains poorly understood. PTSD patients also exhibit impaired object and social recognition, which may underlie the avoidance and symptoms of negative cognition, such as social estrangement or diminished interest in activities. Previous studies have demonstrated that single prolonged stress (SPS), models PTSD phenotypes, including impairments in learning and memory. Therefore, it was hypothesized that SPS would impair social and object recognition memory. Male Sprague Dawley rats were exposed to SPS then tested in the social choice test (SCT) or novel object recognition test (NOR). These tests measure recognition of novelty over familiarity, a natural preference of rodents. Results show that SPS impaired preference for both social and object novelty. In addition, SPS impairment in social recognition may be caused by impaired behavioral flexibility, or an inability to shift behavior during the SCT. These results demonstrate that traumatic stress can impair social and object recognition memory, which may underlie certain avoidant symptoms or negative cognition in PTSD and be related to impaired behavioral flexibility. Copyright © 2013 Elsevier B.V. All rights reserved.

  10. Objective quantification of the tinnitus decompensation by synchronization measures of auditory evoked single sweeps.

    Science.gov (United States)

    Strauss, Daniel J; Delb, Wolfgang; D'Amelio, Roberto; Low, Yin Fen; Falkai, Peter

    2008-02-01

    Large-scale neural correlates of the tinnitus decompensation might be used for an objective evaluation of therapies and neurofeedback based therapeutic approaches. In this study, we try to identify large-scale neural correlates of the tinnitus decompensation using wavelet phase stability criteria of single sweep sequences of late auditory evoked potentials as synchronization stability measure. The extracted measure provided an objective quantification of the tinnitus decompensation and allowed for a reliable discrimination between a group of compensated and decompensated tinnitus patients. We provide an interpretation for our results by a neural model of top-down projections based on the Jastreboff tinnitus model combined with the adaptive resonance theory which has not been applied to model tinnitus so far. Using this model, our stability measure of evoked potentials can be linked to the focus of attention on the tinnitus signal. It is concluded that the wavelet phase stability of late auditory evoked potential single sweeps might be used as objective tinnitus decompensation measure and can be interpreted in the framework of the Jastreboff tinnitus model and adaptive resonance theory.

  11. Bio Inspired Algorithms in Single and Multiobjective Reliability Optimization

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albeanu, Grigore; Burtschy, Bernard

    2014-01-01

    Non-traditional search and optimization methods based on natural phenomena have been proposed recently in order to avoid local or unstable behavior when run towards an optimum state. This paper describes the principles of bio inspired algorithms and reports on Migration Algorithms and Bees...

  12. Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems.

    Directory of Open Access Journals (Sweden)

    Zili Zhang

    Full Text Available Bi-objective Traveling Salesman Problem (bTSP is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM. PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs.

  13. The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

    Directory of Open Access Journals (Sweden)

    Samira Bolouri

    2018-01-01

    Full Text Available Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000 to stations in a discrete space in a GIS, assuming that the stations’ capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions.

  14. Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

    Science.gov (United States)

    Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan

    2018-04-01

    Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

  15. Topology of classical molecular optimal control landscapes for multi-target objectives

    Energy Technology Data Exchange (ETDEWEB)

    Joe-Wong, Carlee [Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544-1000 (United States); Ho, Tak-San; Rabitz, Herschel, E-mail: hrabitz@princeton.edu [Department of Chemistry, Princeton University, Princeton, New Jersey 08544-1009 (United States); Wu, Rebing [Department of Automation, Tsinghua University, Beijing (China)

    2015-04-21

    This paper considers laser-driven optimal control of an ensemble of non-interacting molecules whose dynamics lie in classical phase space. The molecules evolve independently under control to distinct final states. We consider a control landscape defined in terms of multi-target (MT) molecular states and analyze the landscape as a functional of the control field. The topology of the MT control landscape is assessed through its gradient and Hessian with respect to the control. Under particular assumptions, the MT control landscape is found to be free of traps that could hinder reaching the objective. The Hessian associated with an optimal control field is shown to have finite rank, indicating an inherent degree of robustness to control noise. Both the absence of traps and rank of the Hessian are shown to be analogous to the situation of specifying multiple targets for an ensemble of quantum states. Numerical simulations are presented to illustrate the classical landscape principles and further characterize the system behavior as the control field is optimized.

  16. Multi-Objective Optimization for Pure Permanent-Magnet Undulator Magnets Ordering Using Modified Simulated Annealing

    CERN Document Server

    Chen Nian; Li, Ge

    2004-01-01

    Undulator field errors influence the electron beam trajectories and lower the radiation quality. Angular deflection of electron beam is determined by first field integral, orbital displacement of electron beam is determined by second field integral and radiation quality can be evaluated by rms field error or phase error. Appropriate ordering of magnets can greatly reduce the errors. We apply a modified simulated annealing algorithm to this multi-objective optimization problem, taking first field integral, second field integral and rms field error as objective functions. Undulator with small field errors can be designed by this method within a reasonable calculation time even for the case of hundreds of magnets (first field integral reduced to 10-6T·m, second integral to 10-6T·m2 and rms field error to 0.01%). Thus, the field correction after assembling of undulator will be greatly simplified. This paper gives the optimizing process in detail and puts forward a new method to quickly calculate the rms field e...

  17. A multi-objective chaotic particle swarm optimization for environmental/economic dispatch

    International Nuclear Information System (INIS)

    Cai Jiejin; Ma Xiaoqian; Li Qiong; Li Lixiang; Peng Haipeng

    2009-01-01

    A multi-objective chaotic particle swarm optimization (MOCPSO) method has been developed to solve the environmental/economic dipatch (EED) problems considering both economic and environmental issues. The proposed MOCPSO method has been applied in two test power systems. Compared with the conventional multi-objective particle swarm optimization (MOPSO) method, for the compromising minimum fuel cost and emission case, the fuel cost and pollutant emission obtained from MOCPSO method can be reduced about 50.08 $/h and 2.95 kg/h, respectively, in test system 1, about 0.02 $/h and 1.11 kg/h, respectively, in test system 2. The MOCPSO method also results in higher quality solutions for the minimum fuel cost case and the minimum emission case in both of the test power systems. Hence, MOCPSO method can result in great environmental and economic effects. For EED problems, the MOCPSO method is more feasible and more effective alternative approach than the conventional MOPSO method.

  18. Identification of mutated driver pathways in cancer using a multi-objective optimization model.

    Science.gov (United States)

    Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

    2016-05-01

    New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight.

    Science.gov (United States)

    Guo, Siqiu; Zhang, Tao; Song, Yulong; Qian, Feng

    2018-04-23

    This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios.

  20. Coastal aquifer management based on surrogate models and multi-objective optimization

    Science.gov (United States)

    Mantoglou, A.; Kourakos, G.

    2011-12-01

    The demand for fresh water in coastal areas and islands can be very high, especially in summer months, due to increased local needs and tourism. In order to satisfy demand, a combined management plan is proposed which involves: i) desalinization (if needed) of pumped water to a potable level using reverse osmosis and ii) injection of biologically treated waste water into the aquifer. The management plan is formulated into a multiobjective optimization framework, where simultaneous minimization of economic and environmental costs is desired; subject to a constraint to satisfy demand. The method requires modeling tools, which are able to predict the salinity levels of the aquifer in response to different alternative management scenarios. Variable density models can simulate the interaction between fresh and saltwater; however, they are computationally intractable when integrated in optimization algorithms. In order to alleviate this problem, a multi objective optimization algorithm is developed combining surrogate models based on Modular Neural Networks [MOSA(MNN)]. The surrogate models are trained adaptively during optimization based on a Genetic Algorithm. In the crossover step of the genetic algorithm, each pair of parents generates a pool of offspring. All offspring are evaluated based on the fast surrogate model. Then only the most promising offspring are evaluated based on the exact numerical model. This eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. Three new criteria for selecting the most promising offspring were proposed, which improve the Pareto set and maintain the diversity of the optimum solutions. The method has important advancements compared to previous methods, e.g. alleviation of propagation of errors due to surrogate model approximations. The method is applied to a real coastal aquifer in the island of Santorini which is a very touristy island with high water demands. The results show that the algorithm

  1. Barack Obama Blindness (BOB): Absence of Visual Awareness to a Single Object.

    Science.gov (United States)

    Persuh, Marjan; Melara, Robert D

    2016-01-01

    In two experiments, we evaluated whether a perceiver's prior expectations could alone obliterate his or her awareness of a salient visual stimulus. To establish expectancy, observers first made a demanding visual discrimination on each of three baseline trials. Then, on a fourth, critical trial, a single, salient and highly visible object appeared in full view at the center of the visual field and in the absence of any competing visual input. Surprisingly, fully half of the participants were unaware of the solitary object in front of their eyes. Dramatically, observers were blind even when the only stimulus on display was the face of U.S. President Barack Obama. We term this novel, counterintuitive phenomenon, Barack Obama Blindness (BOB). Employing a method that rules out putative memory effects by probing awareness immediately after presentation of the critical stimulus, we demonstrate that the BOB effect is a true failure of conscious vision.

  2. Barack Obama Blindness (BOB: Absence of visual awareness to a single object

    Directory of Open Access Journals (Sweden)

    Marjan ePersuh

    2016-03-01

    Full Text Available In two experiments we evaluated whether a perceiver’s prior expectations could alone obliterate his or her awareness of a salient visual stimulus. To establish expectancy, observers first made a demanding visual discrimination on each of three baseline trials. Then, on a fourth, critical trial, a single, salient and highly visible object appeared in full view at the center of the visual field and in the absence of any competing visual input. Surprisingly, fully half of the participants were unaware of the solitary object in front of their eyes. Dramatically, observers were blind even when the only stimulus on display was the face of U.S. President Barack Obama. We term this novel, counterintuitive phenomenon, Barack Obama Blindness (BOB. Employing a method that rules out putative memory effects by probing awareness immediately after presentation of the critical stimulus, we demonstrate that the BOB effect is a true failure of conscious vision.

  3. Multi-objective optimization of a continuous bio-dissimilation process of glycerol to 1, 3-propanediol.

    Science.gov (United States)

    Xu, Gongxian; Liu, Ying; Gao, Qunwang

    2016-02-10

    This paper deals with multi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. In order to maximize the production rate of 1, 3-propanediol, maximize the conversion rate of glycerol to 1, 3-propanediol, maximize the conversion rate of glycerol, and minimize the concentration of by-product ethanol, we first propose six new multi-objective optimization models that can simultaneously optimize any two of the four objectives above. Then these multi-objective optimization problems are solved by using the weighted-sum and normal-boundary intersection methods respectively. Both the Pareto filter algorithm and removal criteria are used to remove those non-Pareto optimal points obtained by the normal-boundary intersection method. The results show that the normal-boundary intersection method can successfully obtain the approximate Pareto optimal sets of all the proposed multi-objective optimization problems, while the weighted-sum approach cannot achieve the overall Pareto optimal solutions of some multi-objective problems. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach

    International Nuclear Information System (INIS)

    Meo, Santolo; Zohoori, Alireza; Vahedi, Abolfazl

    2016-01-01

    Highlights: • A new optimal design of flux switching permanent magnet generator is developed. • A prototype is employed to validate numerical data used for optimization. • A novel hybrid multi-objective particle swarm optimization approach is proposed. • Optimization targets are weight, cost, voltage and its total harmonic distortion. • The hybrid approach preference is proved compared with other optimization methods. - Abstract: In this paper a new hybrid approach obtained combining a multi-objective particle swarm optimization and artificial neural network is proposed for the design optimization of a direct-drive permanent magnet flux switching generators for low power wind applications. The targets of the proposed multi-objective optimization are to reduce the costs and weight of the machine while maximizing the amplitude of the induced voltage as well as minimizing its total harmonic distortion. The permanent magnet width, the stator and rotor tooth width, the rotor teeth number and stator pole number of the machine define the search space for the optimization problem. Four supervised artificial neural networks are designed for modeling the complex relationships among the weight, the cost, the amplitude and the total harmonic distortion of the output voltage respect to the quantities of the search space. Finite element analysis is adopted to generate training dataset for the artificial neural networks. Finite element analysis based model is verified by experimental results with a 1.5 kW permanent magnet flux switching generator prototype suitable for renewable energy applications, having 6/19 stator poles/rotor teeth. Finally the effectiveness of the proposed hybrid procedure is compared with the results given by conventional multi-objective optimization algorithms. The obtained results show the soundness of the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology for optimal design of permanent

  5. Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Qin Hui [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Zhou Jianzhong, E-mail: jz.zhou@hust.edu.c [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Youlin; Wang Ying; Zhang Yongchuan [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2010-04-15

    A new multi-objective optimization method based on differential evolution with adaptive Cauchy mutation (MODE-ACM) is presented to solve short-term multi-objective optimal hydro-thermal scheduling (MOOHS) problem. Besides fuel cost, the pollutant gas emission is also optimized as an objective. The water transport delay between connected reservoirs and the effect of valve-point loading of thermal units are also taken into account in the presented problem formulation. The proposed algorithm adopts an elitist archive to retain non-dominated solutions obtained during the evolutionary process. It modifies the DE's operators to make it suit for multi-objective optimization (MOO) problems and improve its performance. Furthermore, to avoid premature convergence, an adaptive Cauchy mutation is proposed to preserve the diversity of population. An effective constraints handling method is utilized to handle the complex equality and inequality constraints. The effectiveness of the proposed algorithm is tested on a hydro-thermal system consisting of four cascaded hydro plants and three thermal units. The results obtained by MODE-ACM are compared with several previous studies. It is found that the results obtained by MODE-ACM are superior in terms of fuel cost as well as emission output, consuming a shorter time. Thus it can be a viable alternative to generate optimal trade-offs for short-term MOOHS problem.

  6. Electrochemical Single-Molecule Transistors with Optimized Gate Coupling

    DEFF Research Database (Denmark)

    Osorio, Henrry M.; Catarelli, Samantha; Cea, Pilar

    2015-01-01

    Electrochemical gating at the single molecule level of viologen molecular bridges in ionic liquids is examined. Contrary to previous data recorded in aqueous electrolytes, a clear and sharp peak in the single molecule conductance versus electrochemical potential data is obtained in ionic liquids....... These data are rationalized in terms of a two-step electrochemical model for charge transport across the redox bridge. In this model the gate coupling in the ionic liquid is found to be fully effective with a modeled gate coupling parameter, ξ, of unity. This compares to a much lower gate coupling parameter...

  7. Optimization of time-correlated single photon counting spectrometer

    International Nuclear Information System (INIS)

    Zhang Xiufeng; Du Haiying; Sun Jinsheng

    2011-01-01

    The paper proposes a performance improving scheme for the conventional time-correlated single photon counting spectrometer and develops a high speed data acquisition card based on PCI bus and FPGA technologies. The card is used to replace the multi-channel analyzer to improve the capability and decrease the volume of the spectrometer. The process of operation is introduced along with the integration of the spectrometer system. Many standard samples are measured. The experimental results show that the sensitivity of the spectrometer is single photon counting, and the time resolution of fluorescence lifetime measurement can be picosecond level. The instrument could measure the time-resolved spectroscopy. (authors)

  8. From Single- to Multi-Objective Auto-Tuning of Programs: Advantages and Implications

    Directory of Open Access Journals (Sweden)

    Juan Durillo

    2014-01-01

    Full Text Available Automatic tuning (auto-tuning of software has emerged in recent years as a promising method that tries to automatically adapt the behaviour of a program to attain different performance objectives on a given computing system. This method is gaining momentum due to the increasing complexity of modern multicore-based hardware architectures. Many solutions to auto-tuning have been explored ranging from simple random search to more sophisticate methods like machine learning or evolutionary search. To this day, it is still unclear whether these approaches are general enough to encompass all the complexities of the problem (e.g. search space, parameters influencing the search space, input data sensitivity, etc., or which approach is best suited for a given problem. Furthermore, the growing interest in auto-tuning a program for several objectives is increasing this confusion even further. The goal of this paper is to formally describe the problem addressed by auto-tuning programs and review existing solutions highlighting the advantages and drawbacks of different techniques for single-objective as well as multi-objective auto-tuning approaches.

  9. Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China

    Directory of Open Access Journals (Sweden)

    Xiaoya Ma

    2015-11-01

    Full Text Available As the main feature of land use planning, land use allocation (LUA optimization is an important means of creating a balance between the land-use supply and demand in a region and promoting the sustainable utilization of land resources. In essence, LUA optimization is a multi-objective optimization problem under the land use supply and demand constraints in a region. In order to obtain a better sustainable multi-objective LUA optimization solution, the present study proposes a LUA model based on the multi-objective artificial immune optimization algorithm (MOAIM-LUA model. The main achievements of the present study are as follows: (a the land-use supply and demand factors are analyzed and the constraint conditions of LUA optimization problems are constructed based on the analysis framework of the balance between the land use supply and demand; (b the optimization objectives of LUA optimization problems are defined and modeled using ecosystem service value theory and land rent and price theory; and (c a multi-objective optimization algorithm is designed for solving multi-objective LUA optimization problems based on the novel immune clonal algorithm (NICA. On the basis of the aforementioned achievements, MOAIM-LUA was applied to a real case study of land-use planning in Anlu County, China. Compared to the current land use situation in Anlu County, optimized LUA solutions offer improvements in the social and ecological objective areas. Compared to the existing models, such as the non-dominated sorting genetic algorithm-II, experimental results demonstrate that the model designed in the present study can obtain better non-dominated solution sets and is superior in terms of algorithm stability.

  10. Time-optimal thermalization of single-mode Gaussian states

    Science.gov (United States)

    Carlini, Alberto; Mari, Andrea; Giovannetti, Vittorio

    2014-11-01

    We consider the problem of time-optimal control of a continuous bosonic quantum system subject to the action of a Markovian dissipation. In particular, we consider the case of a one-mode Gaussian quantum system prepared in an arbitrary initial state and which relaxes to the steady state due to the action of the dissipative channel. We assume that the unitary part of the dynamics is represented by Gaussian operations which preserve the Gaussian nature of the quantum state, i.e., arbitrary phase rotations, bounded squeezing, and unlimited displacements. In the ideal ansatz of unconstrained quantum control (i.e., when the unitary phase rotations, squeezing, and displacement of the mode can be performed instantaneously), we study how control can be optimized for speeding up the relaxation towards the fixed point of the dynamics and we analytically derive the optimal relaxation time. Our model has potential and interesting applications to the control of modes of electromagnetic radiation and of trapped levitated nanospheres.

  11. Inverse optimization of objective function weights for treatment planning using clinical dose-volume histograms

    Science.gov (United States)

    Babier, Aaron; Boutilier, Justin J.; Sharpe, Michael B.; McNiven, Andrea L.; Chan, Timothy C. Y.

    2018-05-01

    We developed and evaluated a novel inverse optimization (IO) model to estimate objective function weights from clinical dose-volume histograms (DVHs). These weights were used to solve a treatment planning problem to generate ‘inverse plans’ that had similar DVHs to the original clinical DVHs. Our methodology was applied to 217 clinical head and neck cancer treatment plans that were previously delivered at Princess Margaret Cancer Centre in Canada. Inverse plan DVHs were compared to the clinical DVHs using objective function values, dose-volume differences, and frequency of clinical planning criteria satisfaction. Median differences between the clinical and inverse DVHs were within 1.1 Gy. For most structures, the difference in clinical planning criteria satisfaction between the clinical and inverse plans was at most 1.4%. For structures where the two plans differed by more than 1.4% in planning criteria satisfaction, the difference in average criterion violation was less than 0.5 Gy. Overall, the inverse plans were very similar to the clinical plans. Compared with a previous inverse optimization method from the literature, our new inverse plans typically satisfied the same or more clinical criteria, and had consistently lower fluence heterogeneity. Overall, this paper demonstrates that DVHs, which are essentially summary statistics, provide sufficient information to estimate objective function weights that result in high quality treatment plans. However, as with any summary statistic that compresses three-dimensional dose information, care must be taken to avoid generating plans with undesirable features such as hotspots; our computational results suggest that such undesirable spatial features were uncommon. Our IO-based approach can be integrated into the current clinical planning paradigm to better initialize the planning process and improve planning efficiency. It could also be embedded in a knowledge-based planning or adaptive radiation therapy framework to

  12. A curve fitting method for extrinsic camera calibration from a single image of a cylindrical object

    International Nuclear Information System (INIS)

    Winkler, A W; Zagar, B G

    2013-01-01

    An important step in the process of optical steel coil quality assurance is to measure the proportions of width and radius of steel coils as well as the relative position and orientation of the camera. This work attempts to estimate these extrinsic parameters from single images by using the cylindrical coil itself as the calibration target. Therefore, an adaptive least-squares algorithm is applied to fit parametrized curves to the detected true coil outline in the acquisition. The employed model allows for strictly separating the intrinsic and the extrinsic parameters. Thus, the intrinsic camera parameters can be calibrated beforehand using available calibration software. Furthermore, a way to segment the true coil outline in the acquired images is motivated. The proposed optimization method yields highly accurate results and can be generalized even to measure other solids which cannot be characterized by the identification of simple geometric primitives. (paper)

  13. A curve fitting method for extrinsic camera calibration from a single image of a cylindrical object

    Science.gov (United States)

    Winkler, A. W.; Zagar, B. G.

    2013-08-01

    An important step in the process of optical steel coil quality assurance is to measure the proportions of width and radius of steel coils as well as the relative position and orientation of the camera. This work attempts to estimate these extrinsic parameters from single images by using the cylindrical coil itself as the calibration target. Therefore, an adaptive least-squares algorithm is applied to fit parametrized curves to the detected true coil outline in the acquisition. The employed model allows for strictly separating the intrinsic and the extrinsic parameters. Thus, the intrinsic camera parameters can be calibrated beforehand using available calibration software. Furthermore, a way to segment the true coil outline in the acquired images is motivated. The proposed optimization method yields highly accurate results and can be generalized even to measure other solids which cannot be characterized by the identification of simple geometric primitives.

  14. Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

    Science.gov (United States)

    Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

    2015-04-01

    In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

  15. Sonochemical optimization of the conductivity of single wall nanotube networks

    NARCIS (Netherlands)

    Kaempgen, M.; Lebert, M.; Haluska, M.; Nicoloso, N.; Roth, S.

    2008-01-01

    Networks of single-wall carbon nanotubes (SWCNTs) are covalently functionalized with oxygen-containing groups. In lower concentration, these functional groups act as stable dopands improving the conductivity of the SWCNT material. In higher concentration however, their role as defects with a certain

  16. Multi-objective genetic algorithm optimization of 2D- and 3D-Pareto fronts for vibrational quantum processes

    International Nuclear Information System (INIS)

    Gollub, C; De Vivie-Riedle, R

    2009-01-01

    A multi-objective genetic algorithm is applied to optimize picosecond laser fields, driving vibrational quantum processes. Our examples are state-to-state transitions and unitary transformations. The approach allows features of the shaped laser fields and of the excitation mechanisms to be controlled simultaneously with the quantum yield. Within the parameter range accessible to the experiment, we focus on short pulse durations and low pulse energies to optimize preferably robust laser fields. Multidimensional Pareto fronts for these conflicting objectives could be constructed. Comparison with previous work showed that the solutions from Pareto optimizations and from optimal control theory match very well.

  17. Multi-objective optimization of organic Rankine cycles for waste heat recovery: Application in an offshore platform

    DEFF Research Database (Denmark)

    Pierobon, Leonardo; Nguyen, Tuong-Van; Larsen, Ulrik

    2013-01-01

    This paper aims at finding the optimal design of MW-size organic Rankine cycles by employing the multi-objective optimization with the genetic algorithm as the optimizer. We consider three objective functions: thermal efficiency, total volume of the system and net present value. The optimization...... for acetone. Other promising working fluids are cyclohexane, hexane and isohexane. The present methodology can be utilized in waste heat recovery applications where a compromise between performance, compactness and economic revenue is required. © 2013 Elsevier Ltd. All rights reserved....

  18. Hydrologic Model Development and Calibration: Contrasting a Single- and Multi-Objective Approach for Comparing Model Performance

    Science.gov (United States)

    Asadzadeh, M.; Maclean, A.; Tolson, B. A.; Burn, D. H.

    2009-05-01

    Hydrologic model calibration aims to find a set of parameters that adequately simulates observations of watershed behavior, such as streamflow, or a state variable, such as snow water equivalent (SWE). There are different metrics for evaluating calibration effectiveness that involve quantifying prediction errors, such as the Nash-Sutcliffe (NS) coefficient and bias evaluated for the entire calibration period, on a seasonal basis, for low flows, or for high flows. Many of these metrics are conflicting such that the set of parameters that maximizes the high flow NS differs from the set of parameters that maximizes the low flow NS. Conflicting objectives are very likely when different calibration objectives are based on different fluxes and/or state variables (e.g., NS based on streamflow versus SWE). One of the most popular ways to balance different metrics is to aggregate them based on their importance and find the set of parameters that optimizes a weighted sum of the efficiency metrics. Comparing alternative hydrologic models (e.g., assessing model improvement when a process or more detail is added to the model) based on the aggregated objective might be misleading since it represents one point on the tradeoff of desired error metrics. To derive a more comprehensive model comparison, we solved a bi-objective calibration problem to estimate the tradeoff between two error metrics for each model. Although this approach is computationally more expensive than the aggregation approach, it results in a better understanding of the effectiveness of selected models at each level of every error metric and therefore provides a better rationale for judging relative model quality. The two alternative models used in this study are two MESH hydrologic models (version 1.2) of the Wolf Creek Research basin that differ in their watershed spatial discretization (a single Grouped Response Unit, GRU, versus multiple GRUs). The MESH model, currently under development by Environment

  19. Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective, multi-site supply-chain planning problem

    Science.gov (United States)

    Ayadi, Omar; Felfel, Houssem; Masmoudi, Faouzi

    2017-07-01

    The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.

  20. Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

    Science.gov (United States)

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

    2016-08-15

    Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Object discrimination using optimized multi-frequency auditory cross-modal haptic feedback.

    Science.gov (United States)

    Gibson, Alison; Artemiadis, Panagiotis

    2014-01-01

    As the field of brain-machine interfaces and neuro-prosthetics continues to grow, there is a high need for sensor and actuation mechanisms that can provide haptic feedback to the user. Current technologies employ expensive, invasive and often inefficient force feedback methods, resulting in an unrealistic solution for individuals who rely on these devices. This paper responds through the development, integration and analysis of a novel feedback architecture where haptic information during the neural control of a prosthetic hand is perceived through multi-frequency auditory signals. Through representing force magnitude with volume and force location with frequency, the feedback architecture can translate the haptic experiences of a robotic end effector into the alternative sensory modality of sound. Previous research with the proposed cross-modal feedback method confirmed its learnability, so the current work aimed to investigate which frequency map (i.e. frequency-specific locations on the hand) is optimal in helping users distinguish between hand-held objects and tasks associated with them. After short use with the cross-modal feedback during the electromyographic (EMG) control of a prosthetic hand, testing results show that users are able to use audial feedback alone to discriminate between everyday objects. While users showed adaptation to three different frequency maps, the simplest map containing only two frequencies was found to be the most useful in discriminating between objects. This outcome provides support for the feasibility and practicality of the cross-modal feedback method during the neural control of prosthetics.

  2. Multi-objective optimization of solid waste flows: environmentally sustainable strategies for municipalities.

    Science.gov (United States)

    Minciardi, Riccardo; Paolucci, Massimo; Robba, Michela; Sacile, Roberto

    2008-11-01

    An approach to sustainable municipal solid waste (MSW) management is presented, with the aim of supporting the decision on the optimal flows of solid waste sent to landfill, recycling and different types of treatment plants, whose sizes are also decision variables. This problem is modeled with a non-linear, multi-objective formulation. Specifically, four objectives to be minimized have been taken into account, which are related to economic costs, unrecycled waste, sanitary landfill disposal and environmental impact (incinerator emissions). An interactive reference point procedure has been developed to support decision making; these methods are considered appropriate for multi-objective decision problems in environmental applications. In addition, interactive methods are generally preferred by decision makers as they can be directly involved in the various steps of the decision process. Some results deriving from the application of the proposed procedure are presented. The application of the procedure is exemplified by considering the interaction with two different decision makers who are assumed to be in charge of planning the MSW system in the municipality of Genova (Italy).

  3. Many-objective optimization and visual analytics reveal key trade-offs for London's water supply

    Science.gov (United States)

    Matrosov, Evgenii S.; Huskova, Ivana; Kasprzyk, Joseph R.; Harou, Julien J.; Lambert, Chris; Reed, Patrick M.

    2015-12-01

    In this study, we link a water resource management simulator to multi-objective search to reveal the key trade-offs inherent in planning a real-world water resource system. We consider new supplies and demand management (conservation) options while seeking to elucidate the trade-offs between the best portfolios of schemes to satisfy projected water demands. Alternative system designs are evaluated using performance measures that minimize capital and operating costs and energy use while maximizing resilience, engineering and environmental metrics, subject to supply reliability constraints. Our analysis shows many-objective evolutionary optimization coupled with state-of-the art visual analytics can help planners discover more diverse water supply system designs and better understand their inherent trade-offs. The approach is used to explore future water supply options for the Thames water resource system (including London's water supply). New supply options include a new reservoir, water transfers, artificial recharge, wastewater reuse and brackish groundwater desalination. Demand management options include leakage reduction, compulsory metering and seasonal tariffs. The Thames system's Pareto approximate portfolios cluster into distinct groups of water supply options; for example implementing a pipe refurbishment program leads to higher capital costs but greater reliability. This study highlights that traditional least-cost reliability constrained design of water supply systems masks asset combinations whose benefits only become apparent when more planning objectives are considered.

  4. Time-domain single-source integral equations for analyzing scattering from homogeneous penetrable objects

    KAUST Repository

    Valdés, Felipe

    2013-03-01

    Single-source time-domain electric-and magnetic-field integral equations for analyzing scattering from homogeneous penetrable objects are presented. Their temporal discretization is effected by using shifted piecewise polynomial temporal basis functions and a collocation testing procedure, thus allowing for a marching-on-in-time (MOT) solution scheme. Unlike dual-source formulations, single-source equations involve space-time domain operator products, for which spatial discretization techniques developed for standalone operators do not apply. Here, the spatial discretization of the single-source time-domain integral equations is achieved by using the high-order divergence-conforming basis functions developed by Graglia alongside the high-order divergence-and quasi curl-conforming (DQCC) basis functions of Valdés The combination of these two sets allows for a well-conditioned mapping from div-to curl-conforming function spaces that fully respects the space-mapping properties of the space-time operators involved. Numerical results corroborate the fact that the proposed procedure guarantees accuracy and stability of the MOT scheme. © 2012 IEEE.

  5. Optimized variational analysis scheme of single Doppler radar wind data

    Science.gov (United States)

    Sasaki, Yoshi K.; Allen, Steve; Mizuno, Koki; Whitehead, Victor; Wilk, Kenneth E.

    1989-01-01

    A computer scheme for extracting singularities has been developed and applied to single Doppler radar wind data. The scheme is planned for use in real-time wind and singularity analysis and forecasting. The method, known as Doppler Operational Variational Extraction of Singularities is outlined, focusing on the principle of local symmetry. Results are presented from the application of the scheme to a storm-generated gust front in Oklahoma on May 28, 1987.

  6. Defending a single object against an attacker trying to detect a subset of false targets

    International Nuclear Information System (INIS)

    Peng, R.; Zhai, Q.Q.; Levitin, G.

    2016-01-01

    Deployment of false targets can be a very important and effective measure for enhancing the survivability of an object subjected to intentional attacks. Existing papers have assumed that false targets are either perfect or can be detected with a constant probability. In practice, the attacker may allocate part of its budget into intelligence actions trying to detect a subset of false targets. Analogously, the defender can allocate part of its budget into disinformation actions to prevent the false targets from being detected. In this paper, the detection probability of each false target is assumed to be a function of the intelligence and disinformation efforts allocated on the false target. The optimal resource distribution between target identification/disinformation and attack/protection efforts is studied as solutions of a non-cooperative two period min–max game between the two competitors for the case of constrained defense and attack resources. - Highlights: • A defense-attack problem is studied as a two-period min–max game. • Both intelligence contest over false targets and impact contest are considered. • Optimal defense and attack strategies are investigated with different parameters.

  7. From tomography to full-waveform inversion with a single objective function

    KAUST Repository

    Alkhalifah, Tariq Ali

    2014-02-17

    In full-waveform inversion (FWI), a gradient-based update of the velocity model requires an initial velocity that produces synthetic data that are within a half-cycle, everywhere, from the field data. Such initial velocity models are usually extracted from migration velocity analysis or traveltime tomography, among other means, and are not guaranteed to adhere to the FWI requirements for an initial velocity model. As such, we evaluated an objective function based on the misfit in the instantaneous traveltime between the observed and modeled data. This phase-based attribute of the wavefield, along with its phase unwrapping characteristics, provided a frequency-dependent traveltime function that was easy to use and quantify, especially compared to conventional phase representation. With a strong Laplace damping of the modeled, potentially low-frequency, data along the time axis, this attribute admitted a first-arrival traveltime that could be compared with picked ones from the observed data, such as in wave equation tomography (WET). As we relax the damping on the synthetic and observed data, the objective function measures the misfit in the phase, however unwrapped. It, thus, provided a single objective function for a natural transition from WET to FWI. A Marmousi example demonstrated the effectiveness of the approach.

  8. Strategies for discovery and optimization of thermoelectric materials: Role of real objects and local fields

    Science.gov (United States)

    Zhu, Hao; Xiao, Chong

    2018-06-01

    Thermoelectric materials provide a renewable and eco-friendly solution to mitigate energy shortages and to reduce environmental pollution via direct heat-to-electricity conversion. Discovery of the novel thermoelectric materials and optimization of the state-of-the-art material systems lie at the core of the thermoelectric society, the basic concept behind these being comprehension and manipulation of the physical principles and transport properties regarding thermoelectric materials. In this mini-review, certain examples for designing high-performance bulk thermoelectric materials are presented from the perspectives of both real objects and local fields. The highlights of this topic involve the Rashba effect, Peierls distortion, local magnetic field, and local stress field, which cover several aspects in the field of thermoelectric research. We conclude with an overview of future developments in thermoelectricity.

  9. Multi-Objective Optimization for Equipment Capacity in Off-Grid Smart House

    Directory of Open Access Journals (Sweden)

    Yasuaki Miyazato

    2017-01-01

    Full Text Available Recently, the off-grid smart house has been attracting attention in Japan for considering global warming. Moreover, the selling price of surplus power from the renewable energy system by Feed-In Tariff (FIT has declined. Therefore, this paper proposes an off-grid smart house with the introduced Photovoltaic (PV system, Solar Collector (SC system, Hot Water Heat Pump (HWHP, fixed battery and Electric Vehicle (EV. In this research, a multi-objective optimization problem is considered to minimize the introduced capacity and shortage of the power supply in the smart house. It can perform the electric power procurement from the EV charging station for the compensation of a shortage of power supply. From the simulation results, it is shown that the shortage of the power supply can be reduced by the compensation of the EV power. Furthermore, considering the uncertainty for PV output power, reliable simulation results can be obtained.

  10. Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition

    Directory of Open Access Journals (Sweden)

    Vito Janko

    2017-12-01

    Full Text Available The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

  11. Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.

    Science.gov (United States)

    Janko, Vito; Luštrek, Mitja

    2017-12-29

    The recognition of the user's context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system's energy expenditure and the system's accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

  12. Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition †

    Science.gov (United States)

    Janko, Vito

    2017-01-01

    The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy. PMID:29286301

  13. Bi-objective Optimization of the Water Distribution Networks (Case Study: Sahand City

    Directory of Open Access Journals (Sweden)

    Ali Nikjoofar

    2012-12-01

    Full Text Available To design an urban water network in addition to minimizing the cost, improving the water pressure is very important. Then in this paper a bi-objective optimization model for the new city of Sahand in Northwestern Iran is developed.  Due to its non-linearity and the huge number of variables, the genetic algorithm has been utilized to solve it. Several Pareto solutions have been obtained and then based on the game theory approach (the area monotonic solution, the most efficient point was provided. The solution is simulated by the WaterGems software and the elements of the network are designed. This optimum solution shows a decrease of 13% in total cost in addition to the improved water pressure.

  14. Optimal design and management of chlorination in drinking water networks: a multi-objective approach using Genetic Algorithms and the Pareto optimality concept

    Science.gov (United States)

    Nouiri, Issam

    2017-11-01

    This paper presents the development of multi-objective Genetic Algorithms to optimize chlorination design and management in drinking water networks (DWN). Three objec