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Sample records for ecological optimized multi

  1. Multi-Purpose Optimization for Reconciliation Ecology on an Engineered Floodplain: Yolo Bypass, California

    Directory of Open Access Journals (Sweden)

    Robyn Suddeth Grimm

    2016-03-01

    Full Text Available doi: http://dx.doi.org/10.15447/sfews.2016v14iss1art5Floodplains in California and elsewhere are productive natural habitats with high levels of biodiversity, yet today they are often permanently disconnected from rivers by urban or agricultural development and flood management structures. This disconnection poses a threat to many native fish, bird and other species that evolved to take advantage of seasonal floodplain inundation. The traditional restoration approach to this problem is to recreate historical floodplain by restoring natural hydrologic and successional processes. However levees, dams, and development have made this largely impossible in much of the developed world. Reconciliation ecology recognizes this limitation, and encourages instead the re-engineering of human dominated landscapes to allow for coexistence of native species and human uses. Flood control bypasses are particularly promising places to reconcile historical fish and bird uses of floodplain habitats with human uses. However, the reconciliation approach requires nuanced management of a complex system. Using the Yolo Basin flood bypass in California’s Central Valley as an example, this study develops formal multi-objective optimization to help planners identify management options that best improve habitat quality for fish and birds with minimal costs to farmers or wetland managers. Models like the one developed here can integrate large amounts of data and knowledge, and offer an explicit accounting of relationships and trade-offs between different objectives. This is especially useful in reconciliation planning, where many uses and variables interact on a landscape, and deliberate re-engineering requires consideration of many decisions simultaneously. Initial results suggest that modest land-use changes and inundation management strategies can significantly improve seasonal bird and fish habitat quality at little cost to farmers or other human land uses. The model

  2. A guide to multi-objective optimization for ecological problems with an application to cackling goose management

    Science.gov (United States)

    Williams, Perry J.; Kendall, William L.

    2017-01-01

    Choices in ecological research and management are the result of balancing multiple, often competing, objectives. Multi-objective optimization (MOO) is a formal decision-theoretic framework for solving multiple objective problems. MOO is used extensively in other fields including engineering, economics, and operations research. However, its application for solving ecological problems has been sparse, perhaps due to a lack of widespread understanding. Thus, our objective was to provide an accessible primer on MOO, including a review of methods common in other fields, a review of their application in ecology, and a demonstration to an applied resource management problem.A large class of methods for solving MOO problems can be separated into two strategies: modelling preferences pre-optimization (the a priori strategy), or modelling preferences post-optimization (the a posteriori strategy). The a priori strategy requires describing preferences among objectives without knowledge of how preferences affect the resulting decision. In the a posteriori strategy, the decision maker simultaneously considers a set of solutions (the Pareto optimal set) and makes a choice based on the trade-offs observed in the set. We describe several methods for modelling preferences pre-optimization, including: the bounded objective function method, the lexicographic method, and the weighted-sum method. We discuss modelling preferences post-optimization through examination of the Pareto optimal set. We applied each MOO strategy to the natural resource management problem of selecting a population target for cackling goose (Branta hutchinsii minima) abundance. Cackling geese provide food security to Native Alaskan subsistence hunters in the goose's nesting area, but depredate crops on private agricultural fields in wintering areas. We developed objective functions to represent the competing objectives related to the cackling goose population target and identified an optimal solution

  3. Development of an economical and ecological optimized multi slot fish bypass

    International Nuclear Information System (INIS)

    Tauber, M.; Mader, H.

    2009-01-01

    The European Union's Water Framework Directive (WFD), which came into force in 2000, represents a new framework governing water policies. The ecological objectives of the directive are sometimes in conflict with the historical use of waterbodies for hydropower generation. This paper discussed the mitigation of negative economical effects caused by outfitting hydropower plants with fish migration facilities claimed by the WFD. In particular, it reviewed the new modified version of the Vertical Slot Fishpass which reduces water discharge while maintaining or even enhancing the biological migration of aquatic species. Reducing the flow rate in the fish bypass results in more available water for hydropower generation, which is economically advantageous for operators. The available water resource is simply used more efficiently. The bypass was modified by adding additional slot baffles and guide walls in order to induce intended losses at roughness obstacles and to obtain isolated roughness current in the multi slot area. A Froude similarity model was used to obtain the results of the first phase of this project. Different arrangements of the slot baffles and locations were measured. The results of all trials revealed that the newly developed multi slot variants have clear advantages over conventional vertical slot fish passages. Discharge was reduced by up to 34 per cent while still maintaining the same water levels in the pools. As such, the general flow velocities were also reduced by about 10 per cent, thereby achieving the primary objective of the project. 7 refs., 18 figs

  4. Finite time exergy analysis and multi-objective ecological optimization of a regenerative Brayton cycle considering the impact of flow rate variations

    International Nuclear Information System (INIS)

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

    2015-01-01

    Highlights: • Defining a dimensionless parameter includes the finite-time and size concepts. • Inserting the concept of exergy of fluid streams into finite-time thermodynamics. • Defining, drawing and modifying of maximum ecological function curve. • Suggesting the appropriate performance zone, according to maximum ecological curve. - Abstract: In this study, the optimal performance of a regenerative Brayton cycle is sought through power and then ecological function maximization using finite-time thermodynamic concept and finite-size components. Multi-objective optimization is used for maximizing the ecological function. 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 introduced deploying time variations. The variations of output power, total exergy destruction of the system, and decision variables for the optimum state (maximum ecological function state) are compared to the maximum power state using the dimensionless parameter. The modified ecological function in optimum state is obtained and plotted relating to the dimensionless mass-flow parameter. One can see that the modified ecological function study results in a better performance than that obtained with the maximum power state. Finally, the appropriate performance zone of the heat engine will be obtained

  5. Ecological optimization for generalized irreversible Carnot refrigerators

    International Nuclear Information System (INIS)

    Chen Lingen; Zhu Xiaoqin; Sun Fengrui; Wu Chih

    2005-01-01

    The optimal ecological performance of a Newton's law generalized irreversible Carnot refrigerator with the losses of heat resistance, heat leakage and internal irreversibility is derived by taking an ecological optimization criterion as the objective, which consists of maximizing a function representing the best compromise between the exergy output rate and exergy loss rate (entropy production rate) of the refrigerator. Numerical examples are given to show the effects of heat leakage and internal irreversibility on the optimal performance of generalized irreversible refrigerators

  6. Ecological optimization for an irreversible magnetic Ericsson refrigeration cycle

    International Nuclear Information System (INIS)

    Wang Hao; Wu Guo-Xing

    2013-01-01

    An irreversible Ericsson refrigeration cycle model is established, in which multi-irreversibilities such as finite-rate heat transfer, regenerative loss, heat leakage, and the efficiency of the regenerator are taken into account. Expressions for several important performance parameters, such as the cooling rate, coefficient of performance (COP), power input, exergy output rate, entropy generation rate, and ecological function are derived. The influences of the heat leakage and the time of the regenerative processes on the ecological performance of the refrigerator are analyzed. The optimal regions of the ecological function, cooling rate, and COP are determined and evaluated. Furthermore, some important parameter relations of the refrigerator are revealed and discussed in detail. The results obtained here have general significance and will be helpful in gaining a deep understanding of the magnetic Ericsson refrigeration cycle. (condensed matter: electronic structure, electrical, magnetic, and optical properties)

  7. 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...

  8. Multi-net optimization of VLSI interconnect

    CERN Document Server

    Moiseev, Konstantin; Wimer, Shmuel

    2015-01-01

    This book covers layout design and layout migration methodologies for optimizing multi-net wire structures in advanced VLSI interconnects. Scaling-dependent models for interconnect power, interconnect delay and crosstalk noise are covered in depth, and several design optimization problems are addressed, such as minimization of interconnect power under delay constraints, or design for minimal delay in wire bundles within a given routing area. A handy reference or a guide for design methodologies and layout automation techniques, this book provides a foundation for physical design challenges of interconnect in advanced integrated circuits.  • Describes the evolution of interconnect scaling and provides new techniques for layout migration and optimization, focusing on multi-net optimization; • Presents research results that provide a level of design optimization which does not exist in commercially-available design automation software tools; • Includes mathematical properties and conditions for optimal...

  9. Cover crop-based ecological weed management: exploration and optimization

    NARCIS (Netherlands)

    Kruidhof, H.M.

    2008-01-01

    Keywords: organic farming, ecologically-based weed management, cover crops, green manure, allelopathy, Secale cereale, Brassica napus, Medicago sativa

    Cover crop-based ecological weed management: exploration and optimization. In organic farming systems, weed control is recognized as one

  10. Optimization strategies for discrete multi-material stiffness optimization

    DEFF Research Database (Denmark)

    Hvejsel, Christian Frier; Lund, Erik; Stolpe, Mathias

    2011-01-01

    Design of composite laminated lay-ups are formulated as discrete multi-material selection problems. The design problem can be modeled as a non-convex mixed-integer optimization problem. Such problems are in general only solvable to global optimality for small to moderate sized problems. To attack...... which numerically confirm the sought properties of the new scheme in terms of convergence to a discrete solution....

  11. Optimization of intervention levels in ecological restoration

    Czech Academy of Sciences Publication Activity Database

    Walker, L. R.; Hölzer, N.; Marrs, R.; del Moral, R.; Prach, Karel

    2014-01-01

    Roč. 17, č. 2 (2014), s. 187-192 ISSN 1402-2001 R&D Projects: GA ČR(CZ) GAP505/11/0256 Institutional support: RVO:67985939 Keywords : succession * vegetation * restoration Subject RIV: EH - Ecology, Behaviour Impact factor: 2.548, year: 2014

  12. Optimizing sampling approaches along ecological gradients

    DEFF Research Database (Denmark)

    Schweiger, Andreas; Irl, Severin D. H.; Steinbauer, Manuel

    2016-01-01

    1. Natural scientists and especially ecologists use manipulative experiments or field observations along gradients to differentiate patterns driven by processes from those caused by random noise. A well-conceived sampling design is essential for identifying, analysing and reporting underlying...... patterns in a statistically solid and reproducible manner, given the normal restrictions in labour, time and money. However, a technical guideline about an adequate sampling design to maximize prediction success under restricted resources is lacking. This study aims at developing such a solid...... and reproducible guideline for sampling along gradients in all fields of ecology and science in general. 2. We conducted simulations with artificial data for five common response types known in ecology, each represented by a simple function (no response, linear, exponential, symmetric unimodal and asymmetric...

  13. Multi-Period Trading via Convex Optimization

    DEFF Research Database (Denmark)

    Boyd, Stephen; Busseti, Enzo; Diamond, Steve

    2017-01-01

    We consider a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. We describe a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades off expected return, risk......, transaction cost and holding cost such as the borrowing cost for shorting assets. We then describe a multi-period version of the trading method, where optimization is used to plan a sequence of trades, with only the first one executed, using estimates of future quantities that are unknown when the trades....... In this paper, we do not address a critical component in a trading algorithm, the predictions or forecasts of future quantities. The methods we describe in this paper can be thought of as good ways to exploit predictions, no matter how they are made. We have also developed a companion open-source software...

  14. The Evolutionary Ecology of Multi-Queen Breeding in Ants

    DEFF Research Database (Denmark)

    Huszár, Dóra Borbála

    on other ant species to better understand the social syndromes and how supercolonies function. Foremost, this would help to manage invasive supercolonies that harm humans and biodiversity, but could also provide contribution to our general understanding on how ecology, especially demography impacts upon......). Multi-queen breeding requires both social and life-history adaptations from individuals to decrease intra-colony conflicts and to ensure that sterile workers receive inclusive fitness benefits despite lowered relatedness. However, it remains unclear exactly what ecological and life-history covariates...

  15. Optimization of multi-branch switched diversity systems

    KAUST Repository

    Nam, Haewoon; Alouini, Mohamed-Slim

    2009-01-01

    A performance optimization based on the optimal switching threshold(s) for a multi-branch switched diversity system is discussed in this paper. For the conventional multi-branch switched diversity system with a single switching threshold

  16. Prioritizing ecological restoration among sites in multi-stressor landscapes.

    Science.gov (United States)

    Neeson, Thomas M; Smith, Sigrid D P; Allan, J David; McIntyre, Peter B

    2016-09-01

    Most ecosystems are impacted by multiple local and long-distance stressors, many of which interact in complex ways. We present a framework for prioritizing ecological restoration efforts among sites in multi-stressor landscapes. Using a simple model, we show that both the economic and sociopolitical costs of restoration will typically be lower at sites with a relatively small number of severe problems than at sites with numerous lesser problems. Based on these results, we propose using cumulative stress and evenness of stressor impact as complementary indices that together reflect key challenges of restoring a site to improved condition. To illustrate this approach, we analyze stressor evenness across the world's rivers and the Laurentian Great Lakes. This exploration reveals that evenness and cumulative stress are decoupled, enabling selection of sites where remediating a modest number of high-intensity stressors could substantially reduce cumulative stress. Just as species richness and species evenness are fundamental axes of biological diversity, we argue that cumulative stress and stressor evenness constitute fundamental axes for identifying restoration opportunities in multi-stressor landscapes. Our results highlight opportunities to boost restoration efficiency through strategic use of multi-stressor datasets to identify sites that maximize ecological response per stressor remediated. This prioritization framework can also be expanded to account for the feasibility of remediation and the expected societal benefits of restoration projects. © 2016 by the Ecological Society of America.

  17. Optimization of multi-layered metallic shield

    International Nuclear Information System (INIS)

    Ben-Dor, G.; Dubinsky, A.; Elperin, T.

    2011-01-01

    Research highlights: → We investigated the problem of optimization of a multi-layered metallic shield. → The maximum ballistic limit velocity is a criterion of optimization. → The sequence of materials and the thicknesses of layers in the shield are varied. → The general problem is reduced to the problem of Geometric Programming. → Analytical solutions are obtained for two- and three-layered shields. - Abstract: We investigate the problem of optimization of multi-layered metallic shield whereby the goal is to determine the sequence of materials and the thicknesses of the layers that provide the maximum ballistic limit velocity of the shield. Optimization is performed under the following constraints: fixed areal density of the shield, the upper bound on the total thickness of the shield and the bounds on the thicknesses of the plates manufactured from every material. The problem is reduced to the problem of Geometric Programming which can be solved numerically using known methods. For the most interesting in practice cases of two-layered and three-layered shields the solution is obtained in the explicit analytical form.

  18. Integrating Fuzzy Logic, Optimization, and GIS for Ecological Impact Assessments

    Science.gov (United States)

    Bojórquez-Tapia, Luis A.; Juárez, Lourdes; Cruz-Bello, Gustavo

    2002-09-01

    Appraisal of ecological impacts has been problematic because of the behavior of ecological system and the responses of these systems to human intervention are far from fully understood. While it has been relatively easy to itemize the potential ecological impacts, it has been difficult to arrive at accurate predictions of how these impacts affect populations, communities, or ecosystems. Furthermore, the spatial heterogeneity of ecological systems has been overlooked because its examination is practically impossible through matrix techniques, the most commonly used impact assessment approach. Besides, the public has become increasingly aware of the importance of the EIA in decision-making and thus the interpretation of impact significance is complicated further by the different value judgments of stakeholders. Moreover, impact assessments are carried out with a minimum of data, high uncertainty, and poor conceptual understanding. Hence, the evaluation of ecological impacts entails the integration of subjective and often conflicting judgments from a variety of experts and stakeholders. The purpose of this paper is to present an environmental impact assessment approach based on the integration fuzzy logic, geographical information systems and optimization techniques. This approach enables environmental analysts to deal with the intrinsic imprecision and ambiguity associated with the judgments of experts and stakeholders, the description of ecological systems, and the prediction of ecological impacts. The application of this approach is illustrated through an example, which shows how consensus about impact mitigation can be attained within a conflict resolution framework.

  19. Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Multidisciplinary design and optimization (MDO) tools developed to perform multi-disciplinary analysis based on low fidelity computation methods have been used in...

  20. A multi-criteria optimization analysis for Jordan's energy mix

    International Nuclear Information System (INIS)

    Malkawi, Salaheddin; Al-Nimr, Moh'd; Azizi, Danah

    2017-01-01

    In this study Jordan's energy options were evaluated and ranked with respect to several criteria clusters including financial, technical, environmental, ecological, social, and risk assessment. The Analytical Hierarchy Process; a multi-criteria decision-making analysis, was selected to evaluate the electricity generation options for Jordan. Energy options covered in the analysis include both conventional and renewable sources. Conventional sources evaluated include Oil and Natural Gas. Renewable sources covered wind, biomass, Photovoltaic and concentrated solar systems. The study also investigated generation from nuclear energy and direct combustion of oil shale as well as demand side savings from energy efficiency measures as a resource. Results indicate that to date; conventional fuels remain Jordan's most feasible options from a technical and financial perspective. Nonetheless diversification is essential to promoting energy security as well as environmental welfare. Results indicate that Jordan's best diversification options are nuclear, oil shale, biomass, and wind energy. - Highlights: • A Multi-Criteria Optimization Analysis technique was used to compare nine electricity generation options. • Options considered included Fossil Fuel sources, Nuclear Energy , Renewable Energy and Energy Efficiency. • Six criteria clusters were considered: financial, technical, environmental, ecological, social, and risk assessment. • Results outline potential policy implications for diversifying Jordan's energy mix. • Conventional fuels remain integral and Oil shale, Nuclear, Biomass and Wind offer the best diversification potential.Tag as highlight.

  1. 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.

  2. 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.

  3. Optimal Use of Agricultural Water and Land Resources through Reconfiguring Crop Planting Structure under Socioeconomic and Ecological Objectives

    Directory of Open Access Journals (Sweden)

    Qian Tan

    2017-07-01

    Full Text Available Many economic, social and ecological problems can be attributed to the scarcity and mismanagement of water and land resources. In this study, a multi-objective fuzzy–robust programming (MOFRP method was developed for supporting the optimal use of land and water resources in agriculture. MOFRP improved existing methods through taking ecological services of crop cultivation into account. It was also capable of reflecting fuzziness in preferences, priorities and parameters that were largely neglected in previous agricultural decision making. This method was applied to address a case in arid northwestern China. Optimal plans of crop cultivation reconfiguration were generated for sustaining local development under economic, ecological and social objectives as well as physical restraints in water and land resources. Compared to the status quo, the optimized plan would increase economic and ecological benefits by 12.2% and 18.8%, respectively. The efficiency of irrigation water could also be enhanced with the economic and ecological benefits per unit water being raised and the water consumption per unit land being reduced. The comparisons of the MOFRP model to four alternatives validated that it was capable of achieving satisfactory benefits and reducing system-violation risks without neglecting valuable uncertain information and ecological services of crops. The proposed method was also applicable to other multi-objective management problems under uncertainty without loss of generality.

  4. Thermo-ecological optimization of a solar collector

    International Nuclear Information System (INIS)

    Szargut, J.; Stanek, W.

    2007-01-01

    The depletion of non-renewable natural exergy resources (the thermo-ecological cost) has been accepted as the objective function for thermo-ecological optimization. Its general formulation has been cited. A detailed form of the objective function has been formulated for a solar collector producing hot water for household needs. The following design parameters have been accepted as the decision variables: the collector area per unit of the heat demand, the diameter of collector pipes, the distance of the pipe axes in the collector plate. The design parameters of the internal installation (the pipes, the hot water receiver) have not been taken into account, because they are very individual. The accumulation ability of hot water comprising one day has been assumed. The objective function contains the following components: the thermo-ecological cost of copper plate, copper pipes, glass plate, steel box, thermal insulation, heat transfer liquid, electricity for driving the pump of liquid, fuel for the peak boiler. The duration curves of the flux of solar radiation and absorbed heat have been elaborated according to meteorological data and used in the calculations. The objective function for economic optimization may have a similar form, only the cost values would be different

  5. Activity Sets in Multi-Organizational Ecologies : A Project-Level Perspective on Sustainable Energy Innovations

    NARCIS (Netherlands)

    Gerrit Willem Ziggers; Kristina Manser; Bas Hillebrand; Paul Driessen; Josée Bloemer

    2014-01-01

    Complex innovations involve multi-organizational ecologies consisting of a myriad of different actors. This study investigates how innovation activities can be interpreted in the context of multi-organizational ecologies. Taking a project-level perspective, this study proposes a typology of four

  6. 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...

  7. Optimal Multi-Level Lot Sizing for Requirements Planning Systems

    OpenAIRE

    Earle Steinberg; H. Albert Napier

    1980-01-01

    The wide spread use of advanced information systems such as Material Requirements Planning (MRP) has significantly altered the practice of dependent demand inventory management. Recent research has focused on development of multi-level lot sizing heuristics for such systems. In this paper, we develop an optimal procedure for the multi-period, multi-product, multi-level lot sizing problem by modeling the system as a constrained generalized network with fixed charge arcs and side constraints. T...

  8. Multi-scale research of time and space differences about ecological footprint and ecological carrying capacity of the water resources

    Science.gov (United States)

    Li, Jiahong; Lei, Xiaohui; Fu, Qiang; Li, Tianxiao; Qiao, Yu; Chen, Lei; Liao, Weihong

    2018-03-01

    A multi-scale assessment framework for assessing and comparing the water resource sustainability based on the ecological footprint (EF) is introduced. The study aims to manage the water resource from different views in Heilongjiang Province. First of all, from the scale of each city, the water ecological carrying capacity (ECC) was calculated from 2000 to 2011, and map the spatial distribution of the recent 3 years which show that, the water ecological carrying capacity (ECC) is uneven and has a downward trend year by year. Then, from the perspective of the five secondary partition basins in Heilongjiang Province, the paper calculated the ecological carrying capacity (ECC), the ecological footprint (EF) and ecological surplus and deficit (S&D) situation of water resources from 2000 to 2011, which show that the ecological deficit situation is more prominent in Nenjiang and Suifenhe basins which are in an unsustainable development state. Finally, from the perspective of the province, the paper calculated the ecological carrying capacity (ECC), the ecological footprint (EF) and ecological S&D of water resources from 2000 to 2011 in Heilongjiang Province, which show that the ecological footprint (EF) is in the rising trend, and the correlation coefficient between the ecological carrying capacity (ECC) and the precipitation is 0.8. There are 5 years of unsustainable development state in Heilongjiang. The proposed multi-scale assessment of WEF aims to evaluate the complex relationship between water resource supply and consumption in different spatial scales and time series. It also provides more reasonable assessment result which can be used by managers and regulators.

  9. Multi-instance dictionary learning via multivariate performance measure optimization

    KAUST Repository

    Wang, Jim Jing-Yan

    2016-12-29

    The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.

  10. Multi-instance dictionary learning via multivariate performance measure optimization

    KAUST Repository

    Wang, Jim Jing-Yan; Tsang, Ivor Wai-Hung; Cui, Xuefeng; Lu, Zhiwu; Gao, Xin

    2016-01-01

    The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. Multi-Disciplinary Design Optimization Using WAVE

    Science.gov (United States)

    Irwin, Keith

    2000-01-01

    develop an associative control structure (framework) in the UG WAVE environment enabling multi-disciplinary design of turbine propulsion systems. The capabilities of WAVE were evaluated to assess its use as a rapid optimization and productivity tool. This project also identified future WAVE product enhancements that will make the tool still more beneficial for product development.

  16. 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)

  17. Optimization of multi-response dynamic systems integrating multiple ...

    African Journals Online (AJOL)

    regression and Taguchi's dynamic signal-to-noise ratio concept ..... algorithm for dynamic multi-response optimization based on goal programming approach. .... problem-solving confirmation, if no grave infringement of model suppositions is ...

  18. Design and multi-physics optimization of rotary MRF brakes

    Science.gov (United States)

    Topcu, Okan; Taşcıoğlu, Yiğit; Konukseven, Erhan İlhan

    2018-03-01

    Particle swarm optimization (PSO) is a popular method to solve the optimization problems. However, calculations for each particle will be excessive when the number of particles and complexity of the problem increases. As a result, the execution speed will be too slow to achieve the optimized solution. Thus, this paper proposes an automated design and optimization method for rotary MRF brakes and similar multi-physics problems. A modified PSO algorithm is developed for solving multi-physics engineering optimization problems. The difference between the proposed method and the conventional PSO is to split up the original single population into several subpopulations according to the division of labor. The distribution of tasks and the transfer of information to the next party have been inspired by behaviors of a hunting party. Simulation results show that the proposed modified PSO algorithm can overcome the problem of heavy computational burden of multi-physics problems while improving the accuracy. Wire type, MR fluid type, magnetic core material, and ideal current inputs have been determined by the optimization process. To the best of the authors' knowledge, this multi-physics approach is novel for optimizing rotary MRF brakes and the developed PSO algorithm is capable of solving other multi-physics engineering optimization problems. The proposed method has showed both better performance compared to the conventional PSO and also has provided small, lightweight, high impedance rotary MRF brake designs.

  19. Optimal maintenance of multi-component systems: a review

    NARCIS (Netherlands)

    R.P. Nicolai (Robin); R. Dekker (Rommert)

    2006-01-01

    textabstractIn this article we give an overview of the literature on multi-component maintenance optimization. We focus on work appearing since the 1991 survey "A survey of maintenance models for multi-unit systems" by Cho and Parlar. This paper builds forth on the review article by Dekker et al.

  20. Multi-objective Operation Chart Optimization for Aquatic Species Habitat Conservation of Cascaded Hydropower System on Yuan River, Southwestern China

    Science.gov (United States)

    Wen, X.; Lei, X.; Fang, G.; Huang, X.

    2017-12-01

    Extensive cascading hydropower exploitation in southwestern China has been the subject of debate and conflict in recent years. Introducing limited ecological curves, a novel approach for derivation of hydropower-ecological joint operation chart of cascaded hydropower system was proposed, aiming to optimize the general hydropower and ecological benefits, and to alleviate the ecological deterioration in specific flood/dry conditions. The physical habitat simulation model is proposed initially to simulate the relationship between streamflow and physical habitat of target fish species and to determine the optimal ecological flow range of representative reach. The ecological—hydropower joint optimization model is established to produce the multi-objective operation chart of cascaded hydropower system. Finally, the limited ecological guiding curves were generated and added into the operation chart. The JS-MDS cascaded hydropower system on the Yuan River in southwestern China is employed as the research area. As the result, the proposed guiding curves could increase the hydropower production amount by 1.72% and 5.99% and optimize ecological conservation degree by 0.27% and 1.13% for JS and MDS Reservoir, respectively. Meanwhile, the ecological deterioration rate also sees a decrease from 6.11% to 1.11% for JS Reservoir and 26.67% to 3.89% for MDS Reservoir.

  1. A Multi-Query Optimizer for Monet

    NARCIS (Netherlands)

    S. Manegold (Stefan); A.J. Pellenkoft (Jan); M.L. Kersten (Martin)

    2000-01-01

    textabstractDatabase systems allow for concurrent use of several applications (and query interfaces). Each application generates an ``optimal'' plan---a sequence of low-level database operators---for accessing the database. The queries posed by users through the same application can be optimized

  2. A multi-query optimizer for Monet

    NARCIS (Netherlands)

    S. Manegold (Stefan); A.J. Pellenkoft (Jan); M.L. Kersten (Martin)

    2000-01-01

    textabstractDatabase systems allow for concurrent use of several applications (and query interfaces). Each application generates an ``optimal'' plan---a sequence of low-level database operators---for accessing the database. The queries posed by users through the same application can be optimized

  3. An Aggregated Optimization Model for Multi-Head SMD Placements

    NARCIS (Netherlands)

    Ashayeri, J.; Ma, N.; Sotirov, R.

    2010-01-01

    In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads,

  4. An aggregated optimization model for multi-head SMD placements

    NARCIS (Netherlands)

    Ashayeri, J.; Ma, N.; Sotirov, R.

    2011-01-01

    In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads,

  5. 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.

  6. 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.

  7. 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

  8. 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...

  9. 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.

  10. 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.

  11. Trajectory Optimization Based on Multi-Interval Mesh Refinement Method

    Directory of Open Access Journals (Sweden)

    Ningbo Li

    2017-01-01

    Full Text Available In order to improve the optimization accuracy and convergence rate for trajectory optimization of the air-to-air missile, a multi-interval mesh refinement Radau pseudospectral method was introduced. This method made the mesh endpoints converge to the practical nonsmooth points and decreased the overall collocation points to improve convergence rate and computational efficiency. The trajectory was divided into four phases according to the working time of engine and handover of midcourse and terminal guidance, and then the optimization model was built. The multi-interval mesh refinement Radau pseudospectral method with different collocation points in each mesh interval was used to solve the trajectory optimization model. Moreover, this method was compared with traditional h method. Simulation results show that this method can decrease the dimensionality of nonlinear programming (NLP problem and therefore improve the efficiency of pseudospectral methods for solving trajectory optimization problems.

  12. 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.

  13. 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).

  14. Optimization of multi-phase compressible lattice Boltzmann codes on massively parallel multi-core systems

    NARCIS (Netherlands)

    Biferale, L.; Mantovani, F.; Pivanti, M.; Pozzati, F.; Sbragaglia, M.; Schifano, S.F.; Toschi, F.; Tripiccione, R.

    2011-01-01

    We develop a Lattice Boltzmann code for computational fluid-dynamics and optimize it for massively parallel systems based on multi-core processors. Our code describes 2D multi-phase compressible flows. We analyze the performance bottlenecks that we find as we gradually expose a larger fraction of

  15. 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.

  16. [Ecological suitability assessment and optimization of urban land expansion space in Guiyang City].

    Science.gov (United States)

    Qiu, Cong-hao; Li, Yang-bing; Feng, Yuan-song

    2015-09-01

    Based on the case study of Guiyang City, the minimum cumulative resistance model integrating construction land source, ecological rigid constraints and ecological function type resistance factor, was built by use of cost-distance analysis of urban spatial expansion resistance value through ArcGIS 9.3 software in this paper. Then, the ecological resistance of city spatial expansion of Guiyang from 2010 was simulated dynamically and the ecological suitability classification of city spatial expansion was assessed. According to the conflict between the newly increased city construction land in 2014 and its ecological suitability, the unreasonable city land spatial allocation was discussed also. The results showed that the ecological suitability zonation and the city expansion in the study area were basically consistent during 2010-2014, but the conflict between the new city construction and its land ecological suitability was more serious. The ecological conflict area accounted for 58.2% of the new city construction sites, 35.4% of which happened in the ecological control area, 13.9% in the limited development area and 8.9% in the prohibition development area. The intensification of ecological land use conflict would impair the ecological service function and ecological safety, so this paper put forward the city spatial expansion optimal path to preserve the ecological land and improve the construction land space pattern of Guiyang City so as to ensure its ecological safety.

  17. 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 ...

  18. Optimization of multi-branch switched diversity systems

    KAUST Repository

    Nam, Haewoon

    2009-10-01

    A performance optimization based on the optimal switching threshold(s) for a multi-branch switched diversity system is discussed in this paper. For the conventional multi-branch switched diversity system with a single switching threshold, the optimal switching threshold is a function of both the average channel SNR and the number of diversity branches, where computing the optimal switching threshold is not a simple task when the number of diversity branches is high. The newly proposed multi-branch switched diversity system is based on a sequence of switching thresholds, instead of a single switching threshold, where a different diversity branch uses a different switching threshold for signal comparison. Thanks to the fact that each switching threshold in the sequence can be optimized only based on the number of the remaining diversity branches, the proposed system makes it easy to find these switching thresholds. Furthermore, some selected numerical and simulation results show that the proposed switched diversity system with the sequence of optimal switching thresholds outperforms the conventional system with the single optimal switching threshold. © 2009 IEEE.

  19. 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

  20. 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.

  1. Design and optimization of flexible multi-generation systems

    DEFF Research Database (Denmark)

    Lythcke-Jørgensen, Christoffer Ernst

    variations and dynamics, and energy system analysis, which fails to consider process integration synergies in local systems. The primary objective of the thesis is to derive a methodology for linking process design practices with energy system analysis for enabling coherent and holistic design optimization...... of flexible multi-generation system. In addition, the case study results emphasize the importance of considering flexible operation, systematic process integration, and systematic assessment of uncertainties in the design optimization. It is recommended that future research focus on assessing system impacts...... from flexible multi-generation systems and performance improvements from storage options....

  2. [Multi-mathematical modelings for compatibility optimization of Jiangzhi granules].

    Science.gov (United States)

    Yang, Ming; Zhang, Li; Ge, Yingli; Lu, Yanliu; Ji, Guang

    2011-12-01

    To investigate into the method of "multi activity index evaluation and combination optimized of mult-component" for Chinese herbal formulas. According to the scheme of uniform experimental design, efficacy experiment, multi index evaluation, least absolute shrinkage, selection operator (LASSO) modeling, evolutionary optimization algorithm, validation experiment, we optimized the combination of Jiangzhi granules based on the activity indexes of blood serum ALT, ALT, AST, TG, TC, HDL, LDL and TG level of liver tissues, ratio of liver tissue to body. Analytic hierarchy process (AHP) combining with criteria importance through intercriteria correlation (CRITIC) for multi activity index evaluation was more reasonable and objective, it reflected the information of activity index's order and objective sample data. LASSO algorithm modeling could accurately reflect the relationship between different combination of Jiangzhi granule and the activity comprehensive indexes. The optimized combination of Jiangzhi granule showed better values of the activity comprehensive indexed than the original formula after the validation experiment. AHP combining with CRITIC can be used for multi activity index evaluation and LASSO algorithm, it is suitable for combination optimized of Chinese herbal formulas.

  3. 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.

  4. Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling

    Science.gov (United States)

    Srivastav, Roshan; Srinivasan, K.; Sudheer, K. P.

    2016-11-01

    A simulation-optimization (S-O) framework is developed for the hybrid stochastic modeling of multi-site multi-season streamflows. The multi-objective optimization model formulated is the driver and the multi-site, multi-season hybrid matched block bootstrap model (MHMABB) is the simulation engine within this framework. The multi-site multi-season simulation model is the extension of the existing single-site multi-season simulation model. A robust and efficient evolutionary search based technique, namely, non-dominated sorting based genetic algorithm (NSGA - II) is employed as the solution technique for the multi-objective optimization within the S-O framework. The objective functions employed are related to the preservation of the multi-site critical deficit run sum and the constraints introduced are concerned with the hybrid model parameter space, and the preservation of certain statistics (such as inter-annual dependence and/or skewness of aggregated annual flows). The efficacy of the proposed S-O framework is brought out through a case example from the Colorado River basin. The proposed multi-site multi-season model AMHMABB (whose parameters are obtained from the proposed S-O framework) preserves the temporal as well as the spatial statistics of the historical flows. Also, the other multi-site deficit run characteristics namely, the number of runs, the maximum run length, the mean run sum and the mean run length are well preserved by the AMHMABB model. Overall, the proposed AMHMABB model is able to show better streamflow modeling performance when compared with the simulation based SMHMABB model, plausibly due to the significant role played by: (i) the objective functions related to the preservation of multi-site critical deficit run sum; (ii) the huge hybrid model parameter space available for the evolutionary search and (iii) the constraint on the preservation of the inter-annual dependence. Split-sample validation results indicate that the AMHMABB model is

  5. 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...

  6. 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...

  7. Deterministic methods for multi-control fuel loading optimization

    Science.gov (United States)

    Rahman, Fariz B. Abdul

    We have developed a multi-control fuel loading optimization code for pressurized water reactors based on deterministic methods. The objective is to flatten the fuel burnup profile, which maximizes overall energy production. The optimal control problem is formulated using the method of Lagrange multipliers and the direct adjoining approach for treatment of the inequality power peaking constraint. The optimality conditions are derived for a multi-dimensional multi-group optimal control problem via calculus of variations. Due to the Hamiltonian having a linear control, our optimal control problem is solved using the gradient method to minimize the Hamiltonian and a Newton step formulation to obtain the optimal control. We are able to satisfy the power peaking constraint during depletion with the control at beginning of cycle (BOC) by building the proper burnup path forward in time and utilizing the adjoint burnup to propagate the information back to the BOC. Our test results show that we are able to achieve our objective and satisfy the power peaking constraint during depletion using either the fissile enrichment or burnable poison as the control. Our fuel loading designs show an increase of 7.8 equivalent full power days (EFPDs) in cycle length compared with 517.4 EFPDs for the AP600 first cycle.

  8. 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

  9. Optimal Reoperation of Multi-Reservoirs for Integrated Watershed Management with Multiple Benefits

    Directory of Open Access Journals (Sweden)

    Xinyi Xu

    2014-04-01

    Full Text Available Constructing reservoirs can make more efficient use of water resources for human society. However, the negative impacts of these projects on the environment are often ignored. Optimal reoperation of reservoirs, which considers not only in socio-economic values but also environmental benefits, is increasingly important. A model of optimal reoperation of multi-reservoirs for integrated watershed management with multiple benefits was proposed to alleviate the conflict between water use and environmental deterioration. The social, economic, water quality and ecological benefits were respectively taken into account as the scheduling objectives and quantified according to economic models. River minimum ecological flows and reservoir water levels based on flood control were taken as key constraint conditions. Feasible search discrete differential dynamic programming (FS-DDDP was used to run the model. The proposed model was used in the upstream of the Nanpan River, to quantitatively evaluate the difference between optimal reoperation and routine operation. The results indicated that the reoperation could significantly increase the water quality benefit and have a minor effect on the benefits of power generation and irrigation under different hydrological years. The model can be readily adapted to other multi-reservoir systems for water resources management.

  10. 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.

  11. 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

  12. Distributed Optimization of Multi Beam Directional Communication Networks

    Science.gov (United States)

    2017-06-30

    Distributed Optimization of Multi-Beam Directional Communication Networks Theodoros Tsiligkaridis MIT Lincoln Laboratory Lexington, MA 02141, USA...based routing. I. INTRODUCTION Missions where multiple communication goals are of in- terest are becoming more prevalent in military applications...Multilayer communications may occur within a coalition; for example, a team consisting of ground vehicles and an airborne set of assets may desire to

  13. 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.

  14. 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

  15. Optimal Conditional Reachability for Multi-Priced Timed Automata

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand; Rasmussen, Jacob Illum

    2005-01-01

    In this paper, we prove decidability of the optimal conditional reachability problem for multi-priced timed automata, an extension of timed automata with multiple cost variables evolving according to given rates for each location. More precisely, we consider the problem of determining the minimal...

  16. A solution to the optimal power flow using multi-verse optimizer

    Directory of Open Access Journals (Sweden)

    Bachir Bentouati

    2016-12-01

    Full Text Available In this work, the most common problem of the modern power system named optimal power flow (OPF is optimized using the novel meta-heuristic optimization Multi-verse Optimizer(MVO algorithm. In order to solve the optimal power flow problem, the IEEE 30-bus and IEEE 57-bus systems are used. MVO is applied to solve the proposed problem. The problems considered in the OPF problem are fuel cost reduction, voltage profile improvement, voltage stability enhancement. The obtained results are compared with recently published meta-heuristics. Simulation results clearly reveal the effectiveness and the rapidity of the proposed algorithm for solving the OPF problem.

  17. 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)

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. Multi-level iteration optimization for diffusive critical calculation

    International Nuclear Information System (INIS)

    Li Yunzhao; Wu Hongchun; Cao Liangzhi; Zheng Youqi

    2013-01-01

    In nuclear reactor core neutron diffusion calculation, there are usually at least three levels of iterations, namely the fission source iteration, the multi-group scattering source iteration and the within-group iteration. Unnecessary calculations occur if the inner iterations are converged extremely tight. But the convergence of the outer iteration may be affected if the inner ones are converged insufficiently tight. Thus, a common scheme suit for most of the problems was proposed in this work to automatically find the optimized settings. The basic idea is to optimize the relative error tolerance of the inner iteration based on the corresponding convergence rate of the outer iteration. Numerical results of a typical thermal neutron reactor core problem and a fast neutron reactor core problem demonstrate the effectiveness of this algorithm in the variational nodal method code NODAL with the Gauss-Seidel left preconditioned multi-group GMRES algorithm. The multi-level iteration optimization scheme reduces the number of multi-group and within-group iterations respectively by a factor of about 1-2 and 5-21. (authors)

  3. 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.

  4. Multi-parameter optimization design of parabolic trough solar receiver

    International Nuclear Information System (INIS)

    Guo, Jiangfeng; Huai, Xiulan

    2016-01-01

    Highlights: • The optimal condition can be obtained by multi-parameter optimization. • Exergy and thermal efficiencies are employed as objective function. • Exergy efficiency increases at the expense of heat losses. • The heat obtained by working fluid increases as thermal efficiency grows. - Abstract: The design parameters of parabolic trough solar receiver are interrelated and interact with one another, so the optimal performance of solar receiver cannot be obtained by the convectional single-parameter optimization. To overcome the shortcoming of single-parameter optimization, a multi-parameter optimization of parabolic trough solar receiver is employed based on genetic algorithm in the present work. When the thermal efficiency is taken as the objective function, the heat obtained by working fluid increases while the average temperature of working fluid and wall temperatures of solar receiver decrease. The average temperature of working fluid and the wall temperatures of solar receiver increase while the heat obtained by working fluid decreases generally by taking the exergy efficiency as an objective function. Assuming that the solar radiation intensity remains constant, the exergy obtained by working fluid increases by taking exergy efficiency as the objective function, which comes at the expense of heat losses of solar receiver.

  5. Cross-Surface: Workshop on Interacting with Multi-Device Ecologies in the Wild

    DEFF Research Database (Denmark)

    Houben, Steven; Vermeulen, Jo; Klokmose, Clemens Nylandsted

    2015-01-01

    In this workshop, we will review and discuss opportunities, technical challenges and problems with cross-device interactions in interactive multi-surface and multi-device ecologies. We aim to bring together researchers and practitioners currently working on novel techniques for cross......-surface interactions, identify application domains and enabling technologies for cross-surface interactions in the wild, and establish a research community to develop effective strategies for successful design of cross-device interactions....

  6. Multi-objective optimization and exergetic-sustainability of an irreversible nano scale Braysson cycle operating with Ma

    Directory of Open Access Journals (Sweden)

    Mohammad H. Ahmadi

    2016-06-01

    Full Text Available Nano technology is developed in this decade and changes the way of life. Moreover, developing nano technology has effect on the performance of the materials and consequently improves the efficiency and robustness of them. So, nano scale thermal cycles will be probably engaged in the near future. In this paper, a nano scale irreversible Braysson cycle is studied thermodynamically for optimizing the performance of the Braysson cycle. In the aforementioned cycle an ideal Maxwell–Boltzmann gas is used as a working fluid. Furthermore, three different plans are used for optimizing with multi-objectives; though, the outputs of the abovementioned plans are assessed autonomously. Throughout the first plan, with the purpose of maximizing the ecological coefficient of performance and energy efficiency of the system, multi-objective optimization algorithms are used. Furthermore, in the second plan, two objective functions containing the ecological coefficient of performance and the dimensionless Maximum available work are maximized synchronously by utilizing multi-objective optimization approach. Finally, throughout the third plan, three objective functions involving the dimensionless Maximum available work, the ecological coefficient of performance and energy efficiency of the system are maximized synchronously by utilizing multi-objective optimization approach. The multi-objective evolutionary approach based on the non-dominated sorting genetic algorithm approach is used in this research. Making a decision is performed by three different decision makers comprising linear programming approaches for multidimensional analysis of preference and an approach for order of preference by comparison with ideal answer and Bellman–Zadeh. Lastly, analysis of error is employed to determine deviation of the outcomes gained from each plan.

  7. Optimization of Multiple Related Negotiation through Multi-Negotiation Network

    Science.gov (United States)

    Ren, Fenghui; Zhang, Minjie; Miao, Chunyan; Shen, Zhiqi

    In this paper, a Multi-Negotiation Network (MNN) and a Multi- Negotiation Influence Diagram (MNID) are proposed to optimally handle Multiple Related Negotiations (MRN) in a multi-agent system. Most popular, state-of-the-art approaches perform MRN sequentially. However, a sequential procedure may not optimally execute MRN in terms of maximizing the global outcome, and may even lead to unnecessary losses in some situations. The motivation of this research is to use a MNN to handle MRN concurrently so as to maximize the expected utility of MRN. Firstly, both the joint success rate and the joint utility by considering all related negotiations are dynamically calculated based on a MNN. Secondly, by employing a MNID, an agent's possible decision on each related negotiation is reflected by the value of expected utility. Lastly, through comparing expected utilities between all possible policies to conduct MRN, an optimal policy is generated to optimize the global outcome of MRN. The experimental results indicate that the proposed approach can improve the global outcome of MRN in a successful end scenario, and avoid unnecessary losses in an unsuccessful end scenario.

  8. 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.

  9. Design of ecological concrete by particle packing optimization

    NARCIS (Netherlands)

    Fennis, S.A.A.M.

    2011-01-01

    The goal of this research project on Ecological Concrete was to reduce the CO2-emission of concrete and to reuse secondary materials form concrete production and other industries simultaneously. This also minimizes the use of natural resources and the production costs. To replace cement in concrete

  10. Fuzzy portfolio optimization advances in hybrid multi-criteria methodologies

    CERN Document Server

    Gupta, Pankaj; Inuiguchi, Masahiro; Chandra, Suresh

    2014-01-01

    This monograph presents a comprehensive study of portfolio optimization, an important area of quantitative finance. Considering that the information available in financial markets is incomplete and that the markets are affected by vagueness and ambiguity, the monograph deals with fuzzy portfolio optimization models. At first, the book makes the reader familiar with basic concepts, including the classical mean–variance portfolio analysis. Then, it introduces advanced optimization techniques and applies them for the development of various multi-criteria portfolio optimization models in an uncertain environment. The models are developed considering both the financial and non-financial criteria of investment decision making, and the inputs from the investment experts. The utility of these models in practice is then demonstrated using numerical illustrations based on real-world data, which were collected from one of the premier stock exchanges in India. The book addresses both academics and professionals pursuin...

  11. Multi-material size optimization of a ladder frame chassis

    Science.gov (United States)

    Baker, Michael

    The Corporate Average Fuel Economy (CAFE) is an American fuel standard that sets regulations on fuel economy in vehicles. This law ultimately shapes the development and design research for automakers. Reducing the weight of conventional cars offers a way to improve fuel efficiency. This research investigated the optimality of an automobile's ladder frame chassis (LFC) by conducting multi-objective optimization on the LFC in order to reduce the weight of the chassis. The focus of the design and optimization was a ladder frame chassis commonly used for mass production light motor vehicles with an open-top rear cargo area. This thesis is comprised of two major sections. The first looked to perform thickness optimization in the outer walls of the ladder frame. In the second section, many multi-material distributions, including steel and aluminium varieties, were investigated. A simplified model was used to do an initial hand calculation analysis of the problem. This was used to create a baseline validation to compare the theory with the modeling. A CAD model of the LFC was designed. From the CAD model, a finite element model was extracted and joined using weld and bolt connectors. Following this, a linear static analysis was performed to look at displacement and stresses when subjected to loading conditions that simulate harsh driving conditions. The analysis showed significant values of stress and displacement on the ends of the rails, suggesting improvements could be made elsewhere. An optimization scheme was used to find the values of an all steel frame an optimal thickness distribution was found. This provided a 13% weight reduction over the initial model. To advance the analysis a multi-material approach was used to push the weight savings even further. Several material distributions were analyzed and the lightest utilized aluminium in all but the most strenuous subjected components. This enabled a reduction in weight of 15% over the initial model, equivalent to

  12. Diagnosing Disaster Resilience of Communities as Multi-scale Complex Socio-ecological Systems

    Science.gov (United States)

    Liu, Wei; Mochizuki, Junko; Keating, Adriana; Mechler, Reinhard; Williges, Keith; Hochrainer, Stefan

    2014-05-01

    Global environmental change, growing anthropogenic influence, and increasing globalisation of society have made it clear that disaster vulnerability and resilience of communities cannot be understood without knowledge on the broader social-ecological system in which they are embedded. We propose a framework for diagnosing community resilience to disasters, as a form of disturbance to social-ecological systems, with feedbacks from the local to the global scale. Inspired by iterative multi-scale analysis employed by Resilience Alliance, the related socio-ecological systems framework of Ostrom, and the sustainable livelihood framework, we developed a multi-tier framework for thinking of communities as multi-scale social-ecological systems and analyzing communities' disaster resilience and also general resilience. We highlight the cross-scale influences and feedbacks on communities that exist from lower (e.g., household) to higher (e.g., regional, national) scales. The conceptual framework is then applied to a real-world resilience assessment situation, to illustrate how key components of socio-ecological systems, including natural hazards, natural and man-made environment, and community capacities can be delineated and analyzed.

  13. BWROPT: A multi-cycle BWR fuel cycle optimization code

    Energy Technology Data Exchange (ETDEWEB)

    Ottinger, Keith E.; Maldonado, G. Ivan, E-mail: Ivan.Maldonado@utk.edu

    2015-09-15

    Highlights: • A multi-cycle BWR fuel cycle optimization algorithm is presented. • New fuel inventory and core loading pattern determination. • The parallel simulated annealing algorithm was used for the optimization. • Variable sampling probabilities were compared to constant sampling probabilities. - Abstract: A new computer code for performing BWR in-core and out-of-core fuel cycle optimization for multiple cycles simultaneously has been developed. Parallel simulated annealing (PSA) is used to optimize the new fuel inventory and placement of new and reload fuel for each cycle considered. Several algorithm improvements were implemented and evaluated. The most significant of these are variable sampling probabilities and sampling new fuel types from an ordered array. A heuristic control rod pattern (CRP) search algorithm was also implemented, which is useful for single CRP determinations, however, this feature requires significant computational resources and is currently not practical for use in a full multi-cycle optimization. The PSA algorithm was demonstrated to be capable of significant objective function reduction and finding candidate loading patterns without constraint violations. The use of variable sampling probabilities was shown to reduce runtime while producing better results compared to using constant sampling probabilities. Sampling new fuel types from an ordered array was shown to have a mixed effect compared to random new fuel type sampling, whereby using both random and ordered sampling produced better results but required longer runtimes.

  14. Fuzzy Linguistic Optimization on Multi-Attribute Machining

    Directory of Open Access Journals (Sweden)

    Tian-Syung Lan

    2010-06-01

    Full Text Available Most existing multi-attribute optimization researches for the modern CNC (computer numerical control turning industry were either accomplished within certain manufacturing circumstances, or achieved through numerous equipment operations. Therefore, a general deduction optimization scheme proposed is deemed to be necessary for the industry. In this paper, four parameters (cutting depth, feed rate, speed, tool nose runoff with three levels (low, medium, high are considered to optimize the multi-attribute (surface roughness, tool wear, and material removal rate finish turning. Through FAHP (Fuzzy Analytic Hierarchy Process with eighty intervals for each attribute, the weight of each attribute is evaluated from the paired comparison matrix constructed by the expert judgment. Additionally, twenty-seven fuzzy control rules using trapezoid membership function with respective to seventeen linguistic grades for each attribute are constructed. Considering thirty input and eighty output intervals, the defuzzifierion using center of gravity is thus completed. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution is moreover utilized to integrate and evaluate the multiple machining attributes for the Taguchi experiment, and thus the optimum general deduction parameters can then be received. The confirmation experiment for optimum general deduction parameters is furthermore performed on an ECOCA-3807 CNC lathe. It is shown that the attributes from the fuzzy linguistic optimization parameters are all significantly advanced comparing to those from benchmark. This paper not only proposes a general deduction optimization scheme using orthogonal array, but also contributes the satisfactory fuzzy linguistic approach for multiple CNC turning attributes with profound insight.

  15. 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.

  16. 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

  17. Ecological response of a multi-purpose river development project using macro-invertebrates richness and fish habitat value

    International Nuclear Information System (INIS)

    Pellaud, M.

    2007-05-01

    It has been acknowledged that river morphology and hydrology have been intensively altered due to the anthropic demands in floodplain land use and management, flood protection, promotion of navigability or energy production. Rivers were transformed in water highways, having lost contact with their surrounding floodplain as well as the plethora of ecological processes and occupants once thriving in these ecotonal zones. The identification of this emerging threat of morphological and hydrological alteration on ecological integrity adds further complexity in the exploitation of hydrosystem resources. These resources are heavily coveted and guarded by different lobbies each having strategic views on future project development. Stakeholders may want to promote hydro-electricity, ecologists a natural reserve, communes may wish to have an increased flood protection and leisure promoters a nautical center. As a result, the proposition of a river development project is certain to face opposition of one party or the other. The motivations of this dissertation are anchored in this context, where various and sometimes conflicting potentials for hydrosystem exploitation remain. This works aims at contributing scientifically to an innovative approach at the conception phase of a multi-purpose river development project by developing the ecological module to be implemented in the general project's optimizer. The SYNERGIE project hypothesis is that it should be possible to identify a synergetic pattern joining the interests of ecological integrity, flood safety, energy production and leisure development. Such a multi-objective river development project would stand more chance of acceptance. This dissertation focuses on the ecological aspects of such a river development project and an application on the regulated Swiss Upper Rhone River. Is expected an ecological answer to a river development project design / management which has to be compatible with Heller's Heller (2007) general

  18. Multi-Target Tracking via Mixed Integer Optimization

    Science.gov (United States)

    2016-05-13

    an easily interpretable global objective function. Furthermore, we propose a greedy heuristic which quickly finds good solutions. We extend both the... heuristic and the MIO model to scenarios with missed detections and false alarms. Index Terms—optimization; multi-target tracking; data asso- ciation...energy in [14] and then again as a minimization of discrete-continuous energy in [15]. These algorithms aim to more accurately represent the nature of the

  19. Cross-layer optimization of wireless multi-hop networks

    OpenAIRE

    Soldati, Pablo

    2007-01-01

    The interest in wireless communications has grown constantly for the past decades, leading to an enormous number of applications and services embraced by billions of users. In order to meet the increasing demand for mobile Internet access, several high data-rate radio networking technologies have been proposed to offer wide area high-speed wireless communications, eventually replacing fixed (wired) networks for many applications. This thesis considers cross-layer optimization of multi-hop rad...

  20. Bayesian emulation for optimization in multi-step portfolio decisions

    OpenAIRE

    Irie, Kaoru; West, Mike

    2016-01-01

    We discuss the Bayesian emulation approach to computational solution of multi-step portfolio studies in financial time series. "Bayesian emulation for decisions" involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portf...

  1. Cat swarm optimization based evolutionary framework for multi document summarization

    Science.gov (United States)

    Rautray, Rasmita; Balabantaray, Rakesh Chandra

    2017-07-01

    Today, World Wide Web has brought us enormous quantity of on-line information. As a result, extracting relevant information from massive data has become a challenging issue. In recent past text summarization is recognized as one of the solution to extract useful information from vast amount documents. Based on number of documents considered for summarization, it is categorized as single document or multi document summarization. Rather than single document, multi document summarization is more challenging for the researchers to find accurate summary from multiple documents. Hence in this study, a novel Cat Swarm Optimization (CSO) based multi document summarizer is proposed to address the problem of multi document summarization. The proposed CSO based model is also compared with two other nature inspired based summarizer such as Harmony Search (HS) based summarizer and Particle Swarm Optimization (PSO) based summarizer. With respect to the benchmark Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of different evaluation metrics such as ROUGE score, F score, sensitivity, positive predicate value, summary accuracy, inter sentence similarity and readability metric to validate non-redundancy, cohesiveness and readability of the summary respectively. The experimental analysis clearly reveals that the proposed approach outperforms the other summarizers included in the study.

  2. Optimizing multi-pinhole SPECT geometries using an analytical model

    International Nuclear Information System (INIS)

    Rentmeester, M C M; Have, F van der; Beekman, F J

    2007-01-01

    State-of-the-art multi-pinhole SPECT devices allow for sub-mm resolution imaging of radio-molecule distributions in small laboratory animals. The optimization of multi-pinhole and detector geometries using simulations based on ray-tracing or Monte Carlo algorithms is time-consuming, particularly because many system parameters need to be varied. As an efficient alternative we develop a continuous analytical model of a pinhole SPECT system with a stationary detector set-up, which we apply to focused imaging of a mouse. The model assumes that the multi-pinhole collimator and the detector both have the shape of a spherical layer, and uses analytical expressions for effective pinhole diameters, sensitivity and spatial resolution. For fixed fields-of-view, a pinhole-diameter adapting feedback loop allows for the comparison of the system resolution of different systems at equal system sensitivity, and vice versa. The model predicts that (i) for optimal resolution or sensitivity the collimator layer with pinholes should be placed as closely as possible around the animal given a fixed detector layer, (ii) with high-resolution detectors a resolution improvement up to 31% can be achieved compared to optimized systems, (iii) high-resolution detectors can be placed close to the collimator without significant resolution losses, (iv) interestingly, systems with a physical pinhole diameter of 0 mm can have an excellent resolution when high-resolution detectors are used

  3. 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.

  4. 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

  5. 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.

  6. Discrepancies between selected Pareto optimal plans and final deliverable plans in radiotherapy multi-criteria optimization.

    Science.gov (United States)

    Kyroudi, Archonteia; Petersson, Kristoffer; Ghandour, Sarah; Pachoud, Marc; Matzinger, Oscar; Ozsahin, Mahmut; Bourhis, Jean; Bochud, François; Moeckli, Raphaël

    2016-08-01

    Multi-criteria optimization provides decision makers with a range of clinical choices through Pareto plans that can be explored during real time navigation and then converted into deliverable plans. Our study shows that dosimetric differences can arise between the two steps, which could compromise the clinical choices made during navigation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  7. Development of a Multi-Event Trajectory Optimization Tool for Noise-Optimized Approach Route Design

    NARCIS (Netherlands)

    Braakenburg, M.L.; Hartjes, S.; Visser, H.G.; Hebly, S.J.

    2011-01-01

    This paper presents preliminary results from an ongoing research effort towards the development of a multi-event trajectory optimization methodology that allows to synthesize RNAV approach routes that minimize a cumulative measure of noise, taking into account the total noise effect aggregated for

  8. 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

  9. [Urban ecological land in Changsha City: its quantitative analysis and optimization].

    Science.gov (United States)

    Li, Xiao-Li; Zeng, Guang-Ming; Shi, Lin; Liang, Jie; Cai, Qing

    2010-02-01

    In this paper, a hierarchy index system suitable for catastrophe progression method was constructed to comprehensively analyze and evaluate the status of ecological land construction in Changsha City in 2007. Based on the evaluation results, the irrationalities of the distribution pattern of Changsha urban ecological land were discussed. With the support of geographic information system (GIS), the ecological corridors of the urban ecological land were constructed by using the 'least-cost' modeling, and, in combining with conflict analysis, the optimum project of the urban ecological land was put forward, forming an integrated evaluation system. The results indicated that the ecological efficiency of urban ecological land in Changsha in 2007 was at medium level, with an evaluation value being 0.9416, and the quantitative index being relatively high but the coordination index being relatively low. The analysis and verification with software Fragstats showed that the ecological efficiency of the urban ecological land after optimization was higher, with the evaluation value being 0.9618, and the SHDI, CONTAG, and other indices also enhanced.

  10. Adaptive Multi-Agent Systems for Constrained Optimization

    Science.gov (United States)

    Macready, William; Bieniawski, Stefan; Wolpert, David H.

    2004-01-01

    Product Distribution (PD) theory is a new framework for analyzing and controlling distributed systems. Here we demonstrate its use for distributed stochastic optimization. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. The updating of the Lagrange parameters in the Lagrangian can be viewed as a form of automated annealing, that focuses the MAS more and more on the optimal pure strategy. This provides a simple way to map the solution of any constrained optimization problem onto the equilibrium of a Multi-Agent System (MAS). We present computer experiments involving both the Queen s problem and K-SAT validating the predictions of PD theory and its use for off-the-shelf distributed adaptive optimization.

  11. Energy mesh optimization for multi-level calculation schemes

    International Nuclear Information System (INIS)

    Mosca, P.; Taofiki, A.; Bellier, P.; Prevost, A.

    2011-01-01

    The industrial calculations of third generation nuclear reactors are based on sophisticated strategies of homogenization and collapsing at different spatial and energetic levels. An important issue to ensure the quality of these calculation models is the choice of the collapsing energy mesh. In this work, we show a new approach to generate optimized energy meshes starting from the SHEM 281-group library. The optimization model is applied on 1D cylindrical cells and consists of finding an energy mesh which minimizes the errors between two successive collision probability calculations. The former is realized over the fine SHEM mesh with Livolant-Jeanpierre self-shielded cross sections and the latter is performed with collapsed cross sections over the energy mesh being optimized. The optimization is done by the particle swarm algorithm implemented in the code AEMC and multigroup flux solutions are obtained from standard APOLLO2 solvers. By this new approach, a set of new optimized meshes which encompass from 10 to 50 groups has been defined for PWR and BWR calculations. This set will allow users to adapt the energy detail of the solution to the complexity of the calculation (assembly, multi-assembly, two-dimensional whole core). Some preliminary verifications, in which the accuracy of the new meshes is measured compared to a direct 281-group calculation, show that the 30-group optimized mesh offers a good compromise between simulation time and accuracy for a standard 17 x 17 UO 2 assembly with and without control rods. (author)

  12. AN ADAPTIVE MULTI-CHANNEL SPECTROELLIPSOMETER FOR ECOLOGICAL MONITORING

    Directory of Open Access Journals (Sweden)

    F. A. Mkrtchyan

    2012-09-01

    Full Text Available The creation of multichannel polarization optical instrumentation and use of spectroellipsometric technology are very important for the real-time ecological control of aquatic environment. Spectroellipsometric devices give us high precision of measurements. This report is aimed to describe: •A technology of combined use of spectroellipsometry and algorithms of identification and recognition that allowed the creation of a standard integral complex of instrumental, algorithmic, modular and software tools for the collection and processing of data on the aquatic environment quality with forecasting and decision - making functions. •A compact measuring - information multichannel spectroellipsometric system (device for monitoring the quality of aquatic environment, that is based on the combined use of spectroellipsometry and training, classification, and identification algorithms. This spectroellipsometric system will differ from modern foreign analogues by the use of a new and very promising method of ellipsometric measurements, an original element base of polarization optics and a complex mathematical approach to estimating the quality of a water object subjected to anthropogenic influence.Unlike foreign analogues, the system has no rotating polarization elements. This allows one to increase the signal-to-noise ratio and the long-term stability of measurements, to simplify and reduce the price of multichannel spectroellipsometers. The system will be trainable to the recognition of the pollutants of aquatic environment. A spectroellipsometer in laboratories of V.A. Kotelnikov's Institute of Radioengineering and Electronics, Russian Academy of Sciences is designed for in-situ real time measurements of spectra of ellipsometric parameters Psi and Delta with consequent changeover to spectra of transmitted and reflected signal from water media in frames of used physical model of water environment.

  13. Ecological optimization and parametric study of irreversible Stirling and Ericsson heat pumps

    International Nuclear Information System (INIS)

    Tyagi, S.K.; Kaushik, S.C.; Salohtra, R.

    2002-01-01

    This communication presents the ecological optimization and parametric study of irreversible Stirling and Ericsson heat pump cycles, in which the external irreversibility is due to finite temperature difference between working fluid and external reservoirs while the internal irreversibilities are due to regenerative heat loss and other entropy generations within the cycle. The ecological function is defined as the heating load minus the irreversibility (power loss) which is ambient temperature times the entropy generation. The ecological function is optimized with respect to working fluid temperatures, and the expressions for various parameters at the optimal operating condition are obtained. The effects of different operating parameters on the performance of these cycles have been studied. It is found that the effect of internal irreversibility parameter is more pronounced than the other parameters on the performance of these cycles. (author)

  14. Understanding protected area resilience: a multi-scale, social-ecological approach

    Science.gov (United States)

    Cumming, Graeme S.; Allen, Craig R.; Ban, Natalie C.; Biggs, Duan; Biggs, Harry C.; Cumming, David H.M; De Vos, Alta; Epstein, Graham; Etienne, Michel; Maciejewski, Kristine; Mathevet, Raphael; Moore, Christine; Nenadovic, Mateja; Schoon, Michael

    2015-01-01

    Protected areas (PAs) remain central to the conservation of biodiversity. Classical PAs were conceived as areas that would be set aside to maintain a natural state with minimal human influence. However, global environmental change and growing cross-scale anthropogenic influences mean that PAs can no longer be thought of as ecological islands that function independently of the broader social-ecological system in which they are located. For PAs to be resilient (and to contribute to broader social-ecological resilience), they must be able to adapt to changing social and ecological conditions over time in a way that supports the long-term persistence of populations, communities, and ecosystems of conservation concern. We extend Ostrom's social-ecological systems framework to consider the long-term persistence of PAs, as a form of land use embedded in social-ecological systems, with important cross-scale feedbacks. Most notably, we highlight the cross-scale influences and feedbacks on PAs that exist from the local to the global scale, contextualizing PAs within multi-scale social-ecological functional landscapes. Such functional landscapes are integral to understand and manage individual PAs for long-term sustainability. We illustrate our conceptual contribution with three case studies that highlight cross-scale feedbacks and social-ecological interactions in the functioning of PAs and in relation to regional resilience. Our analysis suggests that while ecological, economic, and social processes are often directly relevant to PAs at finer scales, at broader scales, the dominant processes that shape and alter PA resilience are primarily social and economic.

  15. 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.

  16. MIDA - Optimizing control room performance through multi-modal design

    International Nuclear Information System (INIS)

    Ronan, A. M.

    2006-01-01

    Multi-modal interfaces can support the integration of humans with information processing systems and computational devices to maximize the unique qualities that comprise a complex system. In a dynamic environment, such as a nuclear power plant control room, multi-modal interfaces, if designed correctly, can provide complementary interaction between the human operator and the system which can improve overall performance while reducing human error. Developing such interfaces can be difficult for a designer without explicit knowledge of Human Factors Engineering principles. The Multi-modal Interface Design Advisor (MIDA) was developed as a support tool for system designers and developers. It provides design recommendations based upon a combination of Human Factors principles, a knowledge base of historical research, and current interface technologies. MIDA's primary objective is to optimize available multi-modal technologies within a human computer interface in order to balance operator workload with efficient operator performance. The purpose of this paper is to demonstrate MIDA and illustrate its value as a design evaluation tool within the nuclear power industry. (authors)

  17. Matlab enhanced multi-threaded tomography optimization sequence (MEMTOS)

    International Nuclear Information System (INIS)

    Lum, Edward S.; Pope, Chad L.

    2016-01-01

    Highlights: • Monte Carlo simulation of spent nuclear fuel assembly neutron computed tomography. • Optimized parallel calculations conducted from within the MATLAB environment. • Projection difference technique used to identify anomalies in spent nuclear fuel assemblies. - Abstract: One challenge associated with spent nuclear fuel assemblies is the lack of non-destructive analysis techniques to determine if fuel pins have been removed or replaced or if there are significant defects associated with fuel pins deep within a fuel assembly. Neutron computed tomography is a promising technique for addressing these qualitative issues. Monte Carlo simulation of spent nuclear fuel neutron computed tomography allows inexpensive process investigation and optimization. The main purpose of this work is to provide a fully automated advanced simulation framework for the analysis of spent nuclear fuel inspection using neutron computed tomography. The simulation framework, called Matlab Enhanced Multi-Threaded Tomography Optimization Sequence (MEMTOS) not only automates the simulation process, but also generates superior tomography image results. MEMTOS is written in the MATLAB scripting language and addresses file management, parallel Monte Carlo execution, results extraction, and tomography image generation. This paper describes the mathematical basis for neutron computed tomography, the Monte Carlo technique used to simulate neutron computed tomography, and the overall tomography simulation optimization algorithm. Sequence results presented include overall simulation speed enhancement, tomography and image results obtained for Experimental Breeder Reactor II spent fuel assemblies and light water reactor fuel assemblies. Optimization using a projection difference technique are also described.

  18. 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.

  19. 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.

  20. Three essays on multi-level optimization models and applications

    Science.gov (United States)

    Rahdar, Mohammad

    The general form of a multi-level mathematical programming problem is a set of nested optimization problems, in which each level controls a series of decision variables independently. However, the value of decision variables may also impact the objective function of other levels. A two-level model is called a bilevel model and can be considered as a Stackelberg game with a leader and a follower. The leader anticipates the response of the follower and optimizes its objective function, and then the follower reacts to the leader's action. The multi-level decision-making model has many real-world applications such as government decisions, energy policies, market economy, network design, etc. However, there is a lack of capable algorithms to solve medium and large scale these types of problems. The dissertation is devoted to both theoretical research and applications of multi-level mathematical programming models, which consists of three parts, each in a paper format. The first part studies the renewable energy portfolio under two major renewable energy policies. The potential competition for biomass for the growth of the renewable energy portfolio in the United States and other interactions between two policies over the next twenty years are investigated. This problem mainly has two levels of decision makers: the government/policy makers and biofuel producers/electricity generators/farmers. We focus on the lower-level problem to predict the amount of capacity expansions, fuel production, and power generation. In the second part, we address uncertainty over demand and lead time in a multi-stage mathematical programming problem. We propose a two-stage tri-level optimization model in the concept of rolling horizon approach to reducing the dimensionality of the multi-stage problem. In the third part of the dissertation, we introduce a new branch and bound algorithm to solve bilevel linear programming problems. The total time is reduced by solving a smaller relaxation

  1. 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.

  2. 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.

  3. Data Mining Process Optimization in Computational Multi-agent Systems

    OpenAIRE

    Kazík, O.; Neruda, R. (Roman)

    2015-01-01

    In this paper, we present an agent-based solution of metalearning problem which focuses on optimization of data mining processes. We exploit the framework of computational multi-agent systems in which various meta-learning problems have been already studied, e.g. parameter-space search or simple method recommendation. In this paper, we examine the effect of data preprocessing for machine learning problems. We perform the set of experiments in the search-space of data mining processes which is...

  4. 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.

  5. Multi-step optimization strategy for fuel-optimal orbital transfer of low-thrust spacecraft

    Science.gov (United States)

    Rasotto, M.; Armellin, R.; Di Lizia, P.

    2016-03-01

    An effective method for the design of fuel-optimal transfers in two- and three-body dynamics is presented. The optimal control problem is formulated using calculus of variation and primer vector theory. This leads to a multi-point boundary value problem (MPBVP), characterized by complex inner constraints and a discontinuous thrust profile. The first issue is addressed by embedding the MPBVP in a parametric optimization problem, thus allowing a simplification of the set of transversality constraints. The second problem is solved by representing the discontinuous control function by a smooth function depending on a continuation parameter. The resulting trajectory optimization method can deal with different intermediate conditions, and no a priori knowledge of the control structure is required. Test cases in both the two- and three-body dynamics show the capability of the method in solving complex trajectory design problems.

  6. 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

  7. Multi-component controllers in reactor physics optimality analysis

    International Nuclear Information System (INIS)

    Aldemir, T.

    1978-01-01

    An algorithm is developed for the optimality analysis of thermal reactor assemblies with multi-component control vectors. The neutronics of the system under consideration is assumed to be described by the two-group diffusion equations and constraints are imposed upon the state and control variables. It is shown that if the problem is such that the differential and algebraic equations describing the system can be cast into a linear form via a change of variables, the optimal control components are piecewise constant functions and the global optimal controller can be determined by investigating the properties of the influence functions. Two specific problems are solved utilizing this approach. A thermal reactor consisting of fuel, burnable poison and moderator is found to yield maximal power when the assembly consists of two poison zones and the power density is constant throughout the assembly. It is shown that certain variational relations have to be considered to maintain the activeness of the system equations as differential constraints. The problem of determining the maximum initial breeding ratio for a thermal reactor is solved by treating the fertile and fissile material absorption densities as controllers. The optimal core configurations are found to consist of three fuel zones for a bare assembly and two fuel zones for a reflected assembly. The optimum fissile material density is determined to be inversely proportional to the thermal flux

  8. 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)

  9. Computer Based Porosity Design by Multi Phase Topology Optimization

    Science.gov (United States)

    Burblies, Andreas; Busse, Matthias

    2008-02-01

    A numerical simulation technique called Multi Phase Topology Optimization (MPTO) based on finite element method has been developed and refined by Fraunhofer IFAM during the last five years. MPTO is able to determine the optimum distribution of two or more different materials in components under thermal and mechanical loads. The objective of optimization is to minimize the component's elastic energy. Conventional topology optimization methods which simulate adaptive bone mineralization have got the disadvantage that there is a continuous change of mass by growth processes. MPTO keeps all initial material concentrations and uses methods adapted from molecular dynamics to find energy minimum. Applying MPTO to mechanically loaded components with a high number of different material densities, the optimization results show graded and sometimes anisotropic porosity distributions which are very similar to natural bone structures. Now it is possible to design the macro- and microstructure of a mechanical component in one step. Computer based porosity design structures can be manufactured by new Rapid Prototyping technologies. Fraunhofer IFAM has applied successfully 3D-Printing and Selective Laser Sintering methods in order to produce very stiff light weight components with graded porosities calculated by MPTO.

  10. 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...

  11. Stepwise multi-criteria optimization for robotic radiosurgery

    International Nuclear Information System (INIS)

    Schlaefer, A.; Schweikard, A.

    2008-01-01

    Achieving good conformality and a steep dose gradient around the target volume remains a key aspect of radiosurgery. Clearly, this involves a trade-off between target coverage, conformality of the dose distribution, and sparing of critical structures. Yet, image guidance and robotic beam placement have extended highly conformal dose delivery to extracranial and moving targets. Therefore, the multi-criteria nature of the optimization problem becomes even more apparent, as multiple conflicting clinical goals need to be considered coordinate to obtain an optimal treatment plan. Typically, planning for robotic radiosurgery is based on constrained optimization, namely linear programming. An extension of that approach is presented, such that each of the clinical goals can be addressed separately and in any sequential order. For a set of common clinical goals the mapping to a mathematical objective and a corresponding constraint is defined. The trade-off among the clinical goals is explored by modifying the constraints and optimizing a simple objective, while retaining feasibility of the solution. Moreover, it becomes immediately obvious whether a desired goal can be achieved and where a trade-off is possible. No importance factors or predefined prioritizations of clinical goals are necessary. The presented framework forms the basis for interactive and automated planning procedures. It is demonstrated for a sample case that the linear programming formulation is suitable to search for a clinically optimal treatment, and that the optimization steps can be performed quickly to establish that a Pareto-efficient solution has been found. Furthermore, it is demonstrated how the stepwise approach is preferable compared to modifying importance factors

  12. Ecological optimization of an irreversible quantum Carnot heat engine with spin-1/2 systems

    International Nuclear Information System (INIS)

    Liu Xiaowei; Chen Lingen; Wu Feng; Sun Fengrui

    2010-01-01

    A model of a quantum heat engine with heat resistance, internal irreversibility and heat leakage and many non-interacting spin-1/2 systems is established in this paper. The quantum heat engine cycle is composed of two isothermal processes and two irreversible adiabatic processes and is referred to as a spin quantum Carnot heat engine. Based on the quantum master equation and the semi-group approach, equations of some important performance parameters, such as power output, efficiency, entropy generation rate and ecological function (a criterion representing the optimal compromise between exergy output rate and exergy loss rate), for the irreversible spin quantum Carnot heat engine are derived. The optimal ecological performance of the heat engine in the classical limit is analyzed with numerical examples. The effects of internal irreversibility and heat leakage on ecological performance are discussed in detail.

  13. 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.

  14. 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.

  15. The optimal algorithm for Multi-source RS image fusion.

    Science.gov (United States)

    Fu, Wei; Huang, Shui-Guang; Li, Zeng-Shun; Shen, Hao; Li, Jun-Shuai; Wang, Peng-Yuan

    2016-01-01

    In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing data analysis algorithm for multi-source RS image fusion. The proposed algorithm considers the wavelet transform of the translation invariance as the model operator, also regards the contrast pyramid conversion as the observed operator. The algorithm then designs the objective function by taking use of the weighted sum of evaluation indices, and optimizes the objective function by employing GSDA so as to get a higher resolution of RS image. As discussed above, the bullet points of the text are summarized as follows.•The contribution proposes the iterative self-organizing data analysis algorithm for multi-source RS image fusion.•This article presents GSDA algorithm for the self-adaptively adjustment of the fusion rules.•This text comes up with the model operator and the observed operator as the fusion scheme of RS image based on GSDA. The proposed algorithm opens up a novel algorithmic pathway for multi-source RS image fusion by means of GSDA.

  16. Rotorcraft Optimization Tools: Incorporating Rotorcraft Design Codes into Multi-Disciplinary Design, Analysis, and Optimization

    Science.gov (United States)

    Meyn, Larry A.

    2018-01-01

    One of the goals of NASA's Revolutionary Vertical Lift Technology Project (RVLT) is to provide validated tools for multidisciplinary design, analysis and optimization (MDAO) of vertical lift vehicles. As part of this effort, the software package, RotorCraft Optimization Tools (RCOTOOLS), is being developed to facilitate incorporating key rotorcraft conceptual design codes into optimizations using the OpenMDAO multi-disciplinary optimization framework written in Python. RCOTOOLS, also written in Python, currently supports the incorporation of the NASA Design and Analysis of RotorCraft (NDARC) vehicle sizing tool and the Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics II (CAMRAD II) analysis tool into OpenMDAO-driven optimizations. Both of these tools use detailed, file-based inputs and outputs, so RCOTOOLS provides software wrappers to update input files with new design variable values, execute these codes and then extract specific response variable values from the file outputs. These wrappers are designed to be flexible and easy to use. RCOTOOLS also provides several utilities to aid in optimization model development, including Graphical User Interface (GUI) tools for browsing input and output files in order to identify text strings that are used to identify specific variables as optimization input and response variables. This paper provides an overview of RCOTOOLS and its use

  17. Optimal maintenance of a multi-unit system under dependencies

    Science.gov (United States)

    Sung, Ho-Joon

    The availability, or reliability, of an engineering component greatly influences the operational cost and safety characteristics of a modern system over its life-cycle. Until recently, the reliance on past empirical data has been the industry-standard practice to develop maintenance policies that provide the minimum level of system reliability. Because such empirically-derived policies are vulnerable to unforeseen or fast-changing external factors, recent advancements in the study of topic on maintenance, which is known as optimal maintenance problem, has gained considerable interest as a legitimate area of research. An extensive body of applicable work is available, ranging from those concerned with identifying maintenance policies aimed at providing required system availability at minimum possible cost, to topics on imperfect maintenance of multi-unit system under dependencies. Nonetheless, these existing mathematical approaches to solve for optimal maintenance policies must be treated with caution when considered for broader applications, as they are accompanied by specialized treatments to ease the mathematical derivation of unknown functions in both objective function and constraint for a given optimal maintenance problem. These unknown functions are defined as reliability measures in this thesis, and theses measures (e.g., expected number of failures, system renewal cycle, expected system up time, etc.) do not often lend themselves to possess closed-form formulas. It is thus quite common to impose simplifying assumptions on input probability distributions of components' lifetime or repair policies. Simplifying the complex structure of a multi-unit system to a k-out-of-n system by neglecting any sources of dependencies is another commonly practiced technique intended to increase the mathematical tractability of a particular model. This dissertation presents a proposal for an alternative methodology to solve optimal maintenance problems by aiming to achieve the

  18. Multi-model Simulation for Optimal Control of Aeroacoustics.

    Energy Technology Data Exchange (ETDEWEB)

    Collis, Samuel Scott; Chen, Guoquan

    2005-05-01

    Flow-generated noise, especially rotorcraft noise has been a serious concern for bothcommercial and military applications. A particular important noise source for rotor-craft is Blade-Vortex-Interaction (BVI)noise, a high amplitude, impulsive sound thatoften dominates other rotorcraft noise sources. Usually BVI noise is caused by theunsteady flow changes around various rotor blades due to interactions with vorticespreviously shed by the blades. A promising approach for reducing the BVI noise isto use on-blade controls, such as suction/blowing, micro-flaps/jets, and smart struc-tures. Because the design and implementation of such experiments to evaluate suchsystems are very expensive, efficient computational tools coupled with optimal con-trol systems are required to explore the relevant physics and evaluate the feasibilityof using various micro-fluidic devices before committing to hardware.In this thesis the research is to formulate and implement efficient computationaltools for the development and study of optimal control and design strategies for com-plex flow and acoustic systems with emphasis on rotorcraft applications, especiallyBVI noise control problem. The main purpose of aeroacoustic computations is todetermine the sound intensity and directivity far away from the noise source. How-ever, the computational cost of using a high-fidelity flow-physics model across thefull domain is usually prohibitive and itmight also be less accurate because of thenumerical diffusion and other problems. Taking advantage of the multi-physics andmulti-scale structure of this aeroacoustic problem, we develop a multi-model, multi-domain (near-field/far-field) method based on a discontinuous Galerkin discretiza-tion. In this approach the coupling of multi-domains and multi-models is achievedby weakly enforcing continuity of normal fluxes across a coupling surface. For ourinterested aeroacoustics control problem, the adjoint equations that determine thesensitivity of the cost

  19. 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.

  20. Optimal Wonderful Life Utility Functions in Multi-Agent Systems

    Science.gov (United States)

    Wolpert, David H.; Tumer, Kagan; Swanson, Keith (Technical Monitor)

    2000-01-01

    The mathematics of Collective Intelligence (COINs) is concerned with the design of multi-agent systems so as to optimize an overall global utility function when those systems lack centralized communication and control. Typically in COINs each agent runs a distinct Reinforcement Learning (RL) algorithm, so that much of the design problem reduces to how best to initialize/update each agent's private utility function, as far as the ensuing value of the global utility is concerned. Traditional team game solutions to this problem assign to each agent the global utility as its private utility function. In previous work we used the COIN framework to derive the alternative Wonderful Life Utility (WLU), and experimentally established that having the agents use it induces global utility performance up to orders of magnitude superior to that induced by use of the team game utility. The WLU has a free parameter (the clamping parameter) which we simply set to zero in that previous work. Here we derive the optimal value of the clamping parameter, and demonstrate experimentally that using that optimal value can result in significantly improved performance over that of clamping to zero, over and above the improvement beyond traditional approaches.

  1. 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.

  2. 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.

  3. Optimal control in microgrid using multi-agent reinforcement learning.

    Science.gov (United States)

    Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

    2012-11-01

    This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  4. 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.

  5. 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.

  6. 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.

  7. MULTI-CRITERIA PROGRAMMING METHODS AND PRODUCTION PLAN OPTIMIZATION PROBLEM SOLVING IN METAL INDUSTRY

    OpenAIRE

    Tunjo Perić; Željko Mandić

    2017-01-01

    This paper presents the production plan optimization in the metal industry considered as a multi-criteria programming problem. We first provided the definition of the multi-criteria programming problem and classification of the multicriteria programming methods. Then we applied two multi-criteria programming methods (the STEM method and the PROMETHEE method) in solving a problem of multi-criteria optimization production plan in a company from the metal industry. The obtained resul...

  8. 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

  9. 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.

  10. Ecological optimization and performance study of irreversible Stirling and Ericsson heat engines

    International Nuclear Information System (INIS)

    Tyagi, S K; Kaushik, S C; Salhotra, R

    2002-01-01

    The concept of finite time thermodynamics is used to determine the ecological function of irreversible Stirling and Ericsson heat engine cycles. The ecological function is defined as the power output minus power loss (irreversibility), which is the ambient temperature times, the entropy generation rate. The ecological function is maximized with respect to cycle temperature ratio and the expressions for the corresponding power output and thermal efficiency are derived at the optimal operating conditions. The effect of different operating parameters, the effectiveness on the hot, cold and the regenerative side heat exchangers, the cycle temperature ratio, heat capacitance ratio and the internal irreversibility parameter on the maximum ecological function are studied. It is found that the effect of regenerator effectiveness is more than the hot and cold side heat exchangers and the effect of the effectiveness on cold side heat exchanger is more than the effectiveness on the hot side heat exchanger on the maximum ecological function. It is also found that the effect of internal irreversibility parameter is more than the other parameters not only on the maximum ecological function but also on the corresponding power output and the thermal efficiency

  11. Ecological optimization and performance study of irreversible Stirling and Ericsson heat engines

    Science.gov (United States)

    Tyagi, S. K.; Kaushik, S. C.; Salhotra, R.

    2002-10-01

    The concept of finite time thermodynamics is used to determine the ecological function of irreversible Stirling and Ericsson heat engine cycles. The ecological function is defined as the power output minus power loss (irreversibility), which is the ambient temperature times, the entropy generation rate. The ecological function is maximized with respect to cycle temperature ratio and the expressions for the corresponding power output and thermal efficiency are derived at the optimal operating conditions. The effect of different operating parameters, the effectiveness on the hot, cold and the regenerative side heat exchangers, the cycle temperature ratio, heat capacitance ratio and the internal irreversibility parameter on the maximum ecological function are studied. It is found that the effect of regenerator effectiveness is more than the hot and cold side heat exchangers and the effect of the effectiveness on cold side heat exchanger is more than the effectiveness on the hot side heat exchanger on the maximum ecological function. It is also found that the effect of internal irreversibility parameter is more than the other parameters not only on the maximum ecological function but also on the corresponding power output and the thermal efficiency.

  12. Multi-Robot, Multi-Target Particle Swarm Optimization Search in Noisy Wireless Environments

    Energy Technology Data Exchange (ETDEWEB)

    Kurt Derr; Milos Manic

    2009-05-01

    Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots. This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The approach was developed and analyzed on multiple robot single and multiple target search. The approach was further enhanced by the multi-robot-multi-target search in noisy environments. The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target.

  13. TOPFARM: Multi-fidelity optimization of wind farms

    DEFF Research Database (Denmark)

    Réthoré, Pierre-Elouan; Fuglsang, Peter; Larsen, Gunner Chr.

    2014-01-01

    design of the Middelgrunden wind farm, this work focuses on demonstrating a method that optimizes the profit of wind farms over their lifetime based on a balance of the energy production income, the electrical grid costs, the foundations cost, and the cost of wake turbulence induced fatigue degradation...... of different wind turbine components. A multi-fidelity concept is adapted, which uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU-FP6 TOPFARM project, three levels of complexity are considered. The first level uses...... a simple stationary wind farm wake model to estimate the Annual Energy Production (AEP), a foundations cost model depending on the water depth and an electrical grid cost function dictated by cable length. The second level calculates the AEP and adds a wake-induced fatigue degradation cost function...

  14. Credibilistic multi-period portfolio optimization based on scenario tree

    Science.gov (United States)

    Mohebbi, Negin; Najafi, Amir Abbas

    2018-02-01

    In this paper, we consider a multi-period fuzzy portfolio optimization model with considering transaction costs and the possibility of risk-free investment. We formulate a bi-objective mean-VaR portfolio selection model based on the integration of fuzzy credibility theory and scenario tree in order to dealing with the markets uncertainty. The scenario tree is also a proper method for modeling multi-period portfolio problems since the length and continuity of their horizon. We take the return and risk as well cardinality, threshold, class, and liquidity constraints into consideration for further compliance of the model with reality. Then, an interactive dynamic programming method, which is based on a two-phase fuzzy interactive approach, is employed to solve the proposed model. In order to verify the proposed model, we present an empirical application in NYSE under different circumstances. The results show that the consideration of data uncertainty and other real-world assumptions lead to more practical and efficient solutions.

  15. Multi-Period Portfolio Optimization of Power Generation Assets

    Directory of Open Access Journals (Sweden)

    Barbara Glensk

    2013-01-01

    Full Text Available The liberalization and deregulation of the energy industry in the past decades have been significantly affected by changes in the strategies of energy firms. The traditionally used approach of cost minimization was no longer sufficient, risk and market behavior could no longer be ignored and the need for more appropriate optimization methods for uncertain environments was increased. Meanvariance portfolio (MVP theory is one of the more advanced financial methods that has been successfully applied to the energy sector. Unfortunately, this static approach is inadequate for studying multi-stage investment decision problems. The methodology proposed in this paper considering power generation assets is based on the model introduced by Mulvey, who suggests a reallocation approach using the analysis of various scenarios. The adoption of this methodology to power generation assets allows us to capture the impact of variations in the economic and technical parameters considered. The results of our study show that the application of a model for selection of multi-period portfolio can indeed improve the decision making process. Especially for the case of adding new investments to the portfolio mix, this rebalancing model captures new entries very well. (original abstract

  16. Ecology

    Science.gov (United States)

    Ternjej, Ivancica; Mihaljevic, Zlatko

    2017-10-01

    Ecology is a science that studies the mutual interactions between organisms and their environment. The fundamental subject of interest in ecology is the individual. Topics of interest to ecologists include the diversity, distribution and number of particular organisms, as well as cooperation and competition between organisms, both within and among ecosystems. Today, ecology is a multidisciplinary science. This is particularly true when the subject of interest is the ecosystem or biosphere, which requires the knowledge and input of biologists, chemists, physicists, geologists, geographists, climatologists, hydrologists and many other experts. Ecology is applied in a science of restoration, repairing disturbed sites through human intervention, in natural resource management, and in environmental impact assessments.

  17. 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

  18. A multi-criteria index for ecological evaluation of tropical agriculture in southeastern Mexico.

    Science.gov (United States)

    Huerta, Esperanza; Kampichler, Christian; Ochoa-Gaona, Susana; De Jong, Ben; Hernandez-Daumas, Salvador; Geissen, Violette

    2014-01-01

    The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity (including crop associations), variables which characterize soil conservation of conventional agricultural systems, pesticide use, method and frequency of tillage. We monitored the ecological state of 52 agricultural plots to test the performance of the index. The variables were hierarchically aggregated with simple mathematical algorithms, if-then rules, and rule-based fuzzy models, yielding the final multi-criteria index with values from 0 (worst) to 1 (best conditions). We validated the model through independent evaluation by experts, and we obtained a linear regression with an r2 = 0.61 (p = 2.4e-06, d.f. = 49) between index output and the experts' evaluation.

  19. 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.

  20. Optimizing survivability of multi-state systems with multi-level protection by multi-processor genetic algorithm

    International Nuclear Information System (INIS)

    Levitin, Gregory; Dai Yuanshun; Xie Min; Leng Poh, Kim

    2003-01-01

    In this paper we consider vulnerable systems which can have different states corresponding to different combinations of available elements composing the system. Each state can be characterized by a performance rate, which is the quantitative measure of a system's ability to perform its task. Both the impact of external factors (stress) and internal causes (failures) affect system survivability, which is determined as probability of meeting a given demand. In order to increase the survivability of the system, a multi-level protection is applied to its subsystems. This means that a subsystem and its inner level of protection are in their turn protected by the protection of an outer level. This double-protected subsystem has its outer protection and so forth. In such systems, the protected subsystems can be destroyed only if all of the levels of their protection are destroyed. Each level of protection can be destroyed only if all of the outer levels of protection are destroyed. We formulate the problem of finding the structure of series-parallel multi-state system (including choice of system elements, choice of structure of multi-level protection and choice of protection methods) in order to achieve a desired level of system survivability by the minimal cost. An algorithm based on the universal generating function method is used for determination of the system survivability. A multi-processor version of genetic algorithm is used as optimization tool in order to solve the structure optimization problem. An application example is presented to illustrate the procedure presented in this paper

  1. 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...

  2. 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...

  3. [Construction and optimization of ecological network for nature reserves in Fujian Province, China].

    Science.gov (United States)

    Gu, Fan; Huang, Yi Xiong; Chen, Chuan Ming; Cheng, Dong Liang; Guo, Jia Lei

    2017-03-18

    The nature reserve is very important to biodiversity maintenance. However, due to the urbanization, the nature reserve has been fragmented with reduction in area, leading to the loss of species diversity. Establishing ecological network can effectively connect the fragmented habitats and plays an important role in species conversation. In this paper, based on deciding habitat patches and the landscape cost surface in ArcGIS, a minimum cumulative resistance model was used to simulate the potential ecological network of Fujian provincial nature reserves. The connectivity and importance of network were analyzed and evaluated based on comparison of connectivity indices (including the integral index of connectivity and probability of connectivity) and gravity model both before and after the potential ecological network construction. The optimum ecological network optimization measures were proposed. The result demonstrated that woodlands, grasslands and wetlands together made up the important part of the nature reserve ecological network. The habitats with large area had a higher degree of importance in the network. After constructing the network, the connectivity level was significantly improved. Although interaction strength between different patches va-ried greatly, the corridors between patches with large interaction were very important. The research could provide scientific reference and basis for nature protection and planning in Fujian Province.

  4. Optimal conservation resource allocation under variable economic and ecological time discounting rates in boreal forest

    DEFF Research Database (Denmark)

    Mazziotta, Adriano; Montesino Pouzols, Federico; Mönkkönen, Mikko

    2016-01-01

    Resource allocation to multiple alternative conservation actions is a complex task. A common trade-off occurs between protection of smaller, expensive, high-quality areas versus larger, cheaper, partially degraded areas. We investigate optimal allocation into three actions in boreal forest: current......, and accounting for present revenues from timber harvesting. The present analysis assesses the cost-effective conditions to allocate resources into an inexpensive conservation strategy that nevertheless has potential to produce high ecological values in the future....

  5. 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

  6. 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.

  7. 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.

  8. A Multi-Scalar Approach to Theorizing Socio-Ecological Dynamics of Urban Residential Landscapes

    Directory of Open Access Journals (Sweden)

    Rinku Roy Chowdhury

    2011-01-01

    Full Text Available Urban residential expansion increasingly drives land use, land cover and ecological changes worldwide, yet social science theories explaining such change remain under-developed. Existing theories often focus on processes occurring at one scale, while ignoring other scales. Emerging evidence from four linked U.S. research sites suggests it is essential to examine processes at multiple scales simultaneously when explaining the evolution of urban residential landscapes. Additionally, focusing on urbanization dynamics across multiple sites with a shared research design may yield fruitful comparative insights. The following processes and social-hierarchical scales significantly influence the spatial configurations of residential landscapes: household-level characteristics and environmental attitudes; formal and informal institutions at the neighborhood scale; and municipal-scale land-use governance. While adopting a multi-scale and multi-site approach produces research challenges, doing so is critical to advancing understanding of coupled socio-ecological systems and associated vulnerabilities in a dynamic and environmentally important setting: residential landscapes.

  9. A brief introduction to mixed effects modelling and multi-model inference in ecology.

    Science.gov (United States)

    Harrison, Xavier A; Donaldson, Lynda; Correa-Cano, Maria Eugenia; Evans, Julian; Fisher, David N; Goodwin, Cecily E D; Robinson, Beth S; Hodgson, David J; Inger, Richard

    2018-01-01

    The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

  10. Economic and ecological optimal strategies of management of the system of regional solid waste disposal

    Directory of Open Access Journals (Sweden)

    Samoylik Marina S.

    2014-01-01

    Full Text Available The article develops an economic and ecological model of optimal management of the system of solid waste disposal at the regional level, identifies its target functions and forms optimisation scenarios of management of this sphere with theoretically optimal parameters’ values. Based on the model of management of the sphere of solid waste disposal the article forms an algorithm of identification of optimal managerial strategies and mechanisms of their realisation, which allows solution of the set tasks of optimisation of development of the sphere of solid waste disposal at a given set of values and parameters of the state of the system for a specific type of life cycle of solid waste and different subjects of this sphere. The developed model has a number of feasible solutions and, consequently, offers selection of the best of them with consideration of target functions. The article conducts a SWOT analysis of the current state of solid waste disposal in the Poltava region and identifies a necessity of development of a relevant strategy on the basis of the developed economic and ecological model with consideration of optimisation of mutually opposite criteria: ecological risk for the population from the sphere of solid waste disposal and total expenditures for this sphere functioning. The article conducts modelling of this situation by basic (current situation and alternative scenarios and finds out that, at this stage, it is most expedient to build in the region four sorting lines and five regional solid waste grounds, while expenditures on this sphere are UAH 62.0 million per year, income from secondary raw material sales – UAH 71.2 per year and reduction of the ecological risk – UAH 13 million per year.

  11. 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.

  12. 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.

  13. A guideline to study the feasibility domain of multi-trophic and changing ecological communities.

    Science.gov (United States)

    Song, Chuliang; Rohr, Rudolf P; Saavedra, Serguei

    2018-04-24

    The feasibility domain of an ecological community can be described by the set of environmental abiotic and biotic conditions under which all co-occurring and interacting species in a given site and time can have positive abundances. Mathematically, the feasibility domain corresponds to the parameter space compatible with positive (feasible) solutions at equilibrium for all the state variables in a system under a given model of population dynamics. Under specific dynamics, the existence of a feasible equilibrium is a necessary condition for species persistence regardless of whether the feasible equilibrium is dynamically stable or not. Thus, the size of the feasibility domain can also be used as an indicator of the tolerance of a community to random environmental variations. This has motivated a rich research agenda to estimate the feasibility domain of ecological communities. However, these methodologies typically assume that species interactions are static, or that input and output energy flows on each trophic level are unconstrained. Yet, this is different to how communities behave in nature. Here, we present a step-by-step quantitative guideline providing illustrative examples, computational code, and mathematical proofs to study systematically the feasibility domain of ecological communities under changes of interspecific interactions and subject to different constraints on the trophic energy flows. This guideline covers multi-trophic communities that can be formed by any type of interspecific interactions. Importantly, we show that the relative size of the feasibility domain can significantly change as a function of the biological information taken into consideration. We believe that the availability of these methods can allow us to increase our understanding about the limits at which ecological communities may no longer tolerate further environmental perturbations, and can facilitate a stronger integration of theoretical and empirical research. Copyright

  14. Multi-objective and multi-physics optimization methodology for SFR core: application to CFV concept

    International Nuclear Information System (INIS)

    Fabbris, Olivier

    2014-01-01

    Nuclear reactor core design is a highly multidisciplinary task where neutronics, thermal-hydraulics, fuel thermo-mechanics and fuel cycle are involved. The problem is moreover multi-objective (several performances) and highly dimensional (several tens of design parameters).As the reference deterministic calculation codes for core characterization require important computing resources, the classical design method is not well suited to investigate and optimize new innovative core concepts. To cope with these difficulties, a new methodology has been developed in this thesis. Our work is based on the development and validation of simplified neutronics and thermal-hydraulics calculation schemes allowing the full characterization of Sodium-cooled Fast Reactor core regarding both neutronics performances and behavior during thermal hydraulic dimensioning transients.The developed methodology uses surrogate models (or meta-models) able to replace the neutronics and thermal-hydraulics calculation chain. Advanced mathematical methods for the design of experiment, building and validation of meta-models allows substituting this calculation chain by regression models with high prediction capabilities.The methodology is applied on a very large design space to a challenging core called CFV (French acronym for low void effect core) with a large gain on the sodium void effect. Global sensitivity analysis leads to identify the significant design parameters on the core design and its behavior during unprotected transient which can lead to severe accidents. Multi-objective optimizations lead to alternative core configurations with significantly improved performances. Validation results demonstrate the relevance of the methodology at the pre-design stage of a Sodium-cooled Fast Reactor core. (author) [fr

  15. 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...

  16. Layered Multi-mode Optimal Control Strategy for Multi-MW Wind Turbine

    Institute of Scientific and Technical Information of China (English)

    KONG Yi-gang; WANG Zhi-xin

    2008-01-01

    The control strategy is one of the most important renewable technology, and an increasing number of multi-MW wind turbines are being developed with a variable speed-variable pitch (VS-VP) technology. The main objective of adopting a VS-VP technology is to improve the fast response speed and capture maximum energy. But the power generated by wind turbine changes rapidly because of the centinuous fluctuation of wind speed and direction. At the same time, wind energy conversion systems are of high order, time delays and strong nonlinear characteristics because of many uncertain factors. Based on analyzing the all dynamic processes of wind turbine, a kind of layered multi-mode optimal control strategy is presented which is that three control strategies: bang-bang, fuzzy and adaptive proportienai integral derivative (PID) are adopted according to different stages and expected performance of wind turbine to capture optimum wind power, compensate the nonlinearity and improve the wind turbine performance at low, rated and high wind speed.

  17. Ecology

    International Nuclear Information System (INIS)

    Kalusche, D.

    1978-01-01

    The book turns to the freshment, the teacher, for preparation of ecological topics for lessons, but also to pupils of the secondary stage II, and the main course ecology. The book was knowingly held simple with the restriction to: the ecosystem and its abiotic basic functions, simple articles on population biology, bioceonotic balance ith the questions of niche formation and the life form types coherent with it, of the substance and energy household, the production biology and space-wise and time-wise differentations within an ecological system form the main points. A central role in the volume is given to the illustrations. Their variety is to show and deepen the coherences shown. (orig./HP) [de

  18. The spatial optimism model research for the regional land use based on the ecological constraint

    Science.gov (United States)

    XU, K.; Lu, J.; Chi, Y.

    2013-12-01

    The study focuses on the Yunnan-Guizhou (i.e. Yunnan province and Guizhou province) Plateau in China. Since the Yunnan-Guizhou region consists of closed basins, the land resources suiting for development are in a shortage, and the ecological problems in the area are quite complicated. In such circumstance, in order to get the applicable basins area and distribution, certain spatial optimism model is needed. In this research, Digital Elevation Model (DEM) and land use data are used to get the boundary rules of the basins distribution. Furthermore, natural risks, ecological risks and human-made ecological risks are integrated to be analyzed. Finally, the spatial overlay analysis method is used to model the developable basins area and distribution for industries and urbanization. The study process can be divided into six steps. First, basins and their distribution need to be recognized. In this way, the DEM data is used to extract the geomorphology characteristics. The plaque regions with gradient under eight degrees are selected. Among these regions, the total area of the plaque with the area above 8 km2 is 54,000 km2, 10% of the total area. These regions are selected to the potential application of industries and urbanization. In the later five steps, analyses are aimed at these regions. Secondly, the natural risks are analyzed. The conditions of the earthquake, debris flow and rainstorm and flood are combined to classify the natural risks. Thirdly, the ecological risks are analyzed containing the ecological sensibility and ecosystem service function importance. According to the regional ecologic features, the sensibility containing the soil erosion, acid rain, stony desertification and survive condition factors is derived and classified according to the medium value to get the ecological sensibility partition. The ecosystem service function importance is classified and divided considering the biology variation protection and water conservation factors. The fourth

  19. Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs

    KAUST Repository

    Celik, Abdulkadir

    2016-06-27

    In this paper, we address energy efficient (EE) cooperative spectrum sensing policies for large scale heterogeneous cognitive radio networks (CRNs) which consist of multiple primary channels and large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. We approach this issue from macro and micro perspectives. Macro perspective groups SUs into clusters with the objectives: 1) total energy consumption minimization; 2) total throughput maximization; and 3) inter-cluster energy and throughput fairness. We adopt and demonstrate how to solve these using the nondominated sorting genetic algorithm-II. The micro perspective, on the other hand, operates as a sub-procedure on cluster formations decided by the macro perspective. For the micro perspectives, we first propose a procedure to select the cluster head (CH) which yields: 1) the best CH which gives the minimum total multi-hop error rate and 2) the optimal routing paths from SUs to the CH. Exploiting Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different local detection performances. Then, a convex optimization framework is established to minimize the intra-cluster energy cost by jointly obtaining the optimal sensing durations and thresholds of feature detectors for the proposed voting rule. Likewise, instead of a common fixed sample size test, we developed a weighted sample size test for quantized soft decision fusion to obtain a more EE regime under heterogeneity. We have shown that the combination of proposed CH selection and cooperation schemes gives a superior performance in terms of energy efficiency and robustness against reporting error wall.

  20. Parameter Optimization of Multi-Element Synthetic Aperture Imaging Systems

    Directory of Open Access Journals (Sweden)

    Vera Behar

    2007-03-01

    Full Text Available In conventional ultrasound imaging systems with phased arrays, the further improvement of lateral resolution requires enlarging of the number of array elements that in turn increases both, the complexity and the cost, of imaging systems. Multi-element synthetic aperture focusing (MSAF systems are a very good alternative to conventional systems with phased arrays. The benefit of the synthetic aperture is in reduction of the system complexity, cost and acquisition time. In a MSAF system considered in the paper, a group of elements transmit and receive signals simultaneously, and the transmit beam is defocused to emulate a single element response. The echo received at each element of a receive sub-aperture is recorded in the computer memory. The process of transmission/reception is repeated for all positions of a transmit sub-aperture. All the data recordings associated with each corresponding pair "transmit-receive sub-aperture" are then focused synthetically producing a low-resolution image. The final high-resolution image is formed by summing of the all low-resolution images associated with transmit/receive sub-apertures. A problem of parameter optimization of a MSAF system is considered in this paper. The quality of imaging (lateral resolution and contrast is expressed in terms of the beam characteristics - beam width and side lobe level. The comparison between the MSAF system described in the paper and an equivalent conventional phased array system shows that the MSAF system acquires images of equivalent quality much faster using only a small part of the power per image.

  1. Aida-CMK multi-algorithm optimization kernel applied to analog IC sizing

    CERN Document Server

    Lourenço, Ricardo; Horta, Nuno

    2015-01-01

    This work addresses the research and development of an innovative optimization kernel applied to analog integrated circuit (IC) design. Particularly, this works describes the modifications inside the AIDA Framework, an electronic design automation framework fully developed by at the Integrated Circuits Group-LX of the Instituto de Telecomunicações, Lisbon. It focusses on AIDA-CMK, by enhancing AIDA-C, which is the circuit optimizer component of AIDA, with a new multi-objective multi-constraint optimization module that constructs a base for multiple algorithm implementations. The proposed solution implements three approaches to multi-objective multi-constraint optimization, namely, an evolutionary approach with NSGAII, a swarm intelligence approach with MOPSO and stochastic hill climbing approach with MOSA. Moreover, the implemented structure allows the easy hybridization between kernels transforming the previous simple NSGAII optimization module into a more evolved and versatile module supporting multiple s...

  2. 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.

  3. MULTI-CRITERIA PROGRAMMING METHODS AND PRODUCTION PLAN OPTIMIZATION PROBLEM SOLVING IN METAL INDUSTRY

    Directory of Open Access Journals (Sweden)

    Tunjo Perić

    2017-09-01

    Full Text Available This paper presents the production plan optimization in the metal industry considered as a multi-criteria programming problem. We first provided the definition of the multi-criteria programming problem and classification of the multicriteria programming methods. Then we applied two multi-criteria programming methods (the STEM method and the PROMETHEE method in solving a problem of multi-criteria optimization production plan in a company from the metal industry. The obtained results indicate a high efficiency of the applied methods in solving the problem.

  4. 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.

  5. 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)

  6. Performance and environment as objectives in multi-criterion optimization of steam injected gas turbine cycles

    International Nuclear Information System (INIS)

    Kayadelen, Hasan Kayhan; Ust, Yasin

    2014-01-01

    Rapidly growing demand for gas turbines promotes research on their performance improvement and reducing their exhaust pollutants. Even small increments in net power or thermal efficiency and small changes in pollutant emissions have become significant concerns for both new designs and cycle modifications. To fulfill these requirements an accurate performance evaluation method which enables to see the effects on the exhaust gas composition is an important necessity. To fill this gap, a thermo-ecologic performance evaluation approach for gas turbine cycles with chemical equilibrium approximation which enables performance and environmental aspects to be considered simultaneously, is presented in this work. Steam injection is an effective modification to boost power and limit NO x emissions for gas turbine systems. Steam injection also increases thermal efficiency so less fuel is burnt to maintain the same power output. Because of its performance related and environmental advantages, presented approach is applied on the steam injected gas turbine cycle and a precise multi-criterion optimization is carried out for varying steam injection, as well as equivalence and pressure ratios. Irreversibilities and pressure losses are also considered. Effects of each parameter on the net work and thermal efficiency as well as non-equilibrium NO x and CO emissions are demonstrated. Precision improvement of the presented thermo-ecological model is shown and two main concerns; constant turbine inlet condition for higher net work output and constant net work output condition for lower fuel consumption are compared. - Highlights: • A thermodynamically precise performance estimation tool for GT cycles is presented. • STIG application is provided to show its flexibility for any GT cycle and diluents. • Constant TIT and net work output conditions have been compared and discussed. • The model provides results to evaluate economic and environmental aspects together. • It provides a

  7. Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology.

    Science.gov (United States)

    Grimm, Volker; Railsback, Steven F

    2012-01-19

    Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.

  8. Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology

    Science.gov (United States)

    Grimm, Volker; Railsback, Steven F.

    2012-01-01

    Modern ecology recognizes that modelling systems across scales and at multiple levels—especially to link population and ecosystem dynamics to individual adaptive behaviour—is essential for making the science predictive. ‘Pattern-oriented modelling’ (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions. PMID:22144392

  9. 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.

  10. Optimization of an implicit constrained multi-physics system for motor wheels of electric vehicle

    International Nuclear Information System (INIS)

    Lei, Fei; Du, Bin; Liu, Xin; Xie, Xiaoping; Chai, Tian

    2016-01-01

    In this paper, implicit constrained multi-physics model of a motor wheel for an electric vehicle is built and then optimized. A novel optimization approach is proposed to solve the compliance problem between implicit constraints and stochastic global optimization. Firstly, multi-physics model of motor wheel is built from the theories of structural mechanics, electromagnetism and thermal physics. Then, implicit constraints are applied from the vehicle performances and magnetic characteristics. Implicit constrained optimization is carried out by a series of unconstrained optimization and verifications. In practice, sequentially updated subspaces are designed to completely substitute the original design space in local areas. In each subspace, a solution is obtained and is then verified by the implicit constraints. Optimal solutions which satisfy the implicit constraints are accepted as final candidates. The final global optimal solution is optimized from those candidates. Discussions are carried out to discover the differences between optimal solutions with unconstrained problem and different implicit constrained problems. Results show that the implicit constraints have significant influences on the optimal solution and the proposed approach is effective in finding the optimals. - Highlights: • An implicit constrained multi-physics model is built for sizing a motor wheel. • Vehicle dynamic performances are applied as implicit constraints for nonlinear system. • An efficient novel optimization is proposed to explore the constrained design space. • The motor wheel is optimized to achieve maximum efficiency on vehicle dynamics. • Influences of implicit constraints on vehicle performances are compared and analyzed.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Ecological networks: a spatial concept for multi-actor planning of sustainable landscapes

    NARCIS (Netherlands)

    Opdam, P.F.M.; Steingröver, E.G.; Rooij, van S.A.M.

    2006-01-01

    In this paper, we propose the ecological network concept as a suitable basis for inserting biodiversity conservation into sustainable landscape development. For landscapes to be ecologically sustainable, the landscape structure should support those ecological processes required for the landscape to

  16. 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.

  17. Ecological response of a multi-purpose river development project using macro-invertebrates richness and fish habitat value[Dissertation 3807

    Energy Technology Data Exchange (ETDEWEB)

    Pellaud, M.

    2007-05-15

    It has been acknowledged that river morphology and hydrology have been intensively altered due to the anthropic demands in floodplain land use and management, flood protection, promotion of navigability or energy production. Rivers were transformed in water highways, having lost contact with their surrounding floodplain as well as the plethora of ecological processes and occupants once thriving in these ecotonal zones. The identification of this emerging threat of morphological and hydrological alteration on ecological integrity adds further complexity in the exploitation of hydrosystem resources. These resources are heavily coveted and guarded by different lobbies each having strategic views on future project development. Stakeholders may want to promote hydro-electricity, ecologists a natural reserve, communes may wish to have an increased flood protection and leisure promoters a nautical center. As a result, the proposition of a river development project is certain to face opposition of one party or the other. The motivations of this dissertation are anchored in this context, where various and sometimes conflicting potentials for hydrosystem exploitation remain. This works aims at contributing scientifically to an innovative approach at the conception phase of a multi-purpose river development project by developing the ecological module to be implemented in the general project's optimizer. The SYNERGIE project hypothesis is that it should be possible to identify a synergetic pattern joining the interests of ecological integrity, flood safety, energy production and leisure development. Such a multi-objective river development project would stand more chance of acceptance. This dissertation focuses on the ecological aspects of such a river development project and an application on the regulated Swiss Upper Rhone River. Is expected an ecological answer to a river development project design / management which has to be compatible with Heller's Heller (2007

  18. Optimal conservation resource allocation under variable economic and ecological time discounting rates in boreal forest.

    Science.gov (United States)

    Mazziotta, Adriano; Pouzols, Federico Montesino; Mönkkönen, Mikko; Kotiaho, Janne S; Strandman, Harri; Moilanen, Atte

    2016-09-15

    Resource allocation to multiple alternative conservation actions is a complex task. A common trade-off occurs between protection of smaller, expensive, high-quality areas versus larger, cheaper, partially degraded areas. We investigate optimal allocation into three actions in boreal forest: current standard forest management rules, setting aside of mature stands, or setting aside of clear-cuts. We first estimated how habitat availability for focal indicator species and economic returns from timber harvesting develop through time as a function of forest type and action chosen. We then developed an optimal resource allocation by accounting for budget size and habitat availability of indicator species in different forest types. We also accounted for the perspective adopted towards sustainability, modeled via temporal preference and economic and ecological time discounting. Controversially, we found that in boreal forest set-aside followed by protection of clear-cuts can become a winning cost-effective strategy when accounting for habitat requirements of multiple species, long planning horizon, and limited budget. It is particularly effective when adopting a long-term sustainability perspective, and accounting for present revenues from timber harvesting. The present analysis assesses the cost-effective conditions to allocate resources into an inexpensive conservation strategy that nevertheless has potential to produce high ecological values in the future. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Multi-fidelity optimization of horizontal axis wind turbines

    DEFF Research Database (Denmark)

    McWilliam, Michael; Zahle, Frederik; Pavese, Christian

    2017-01-01

    This paper is concerned with the numerical design optimization of wind turbines. Many examples of wind turbine design optimization in literature rely on simplified analysis in some form. This may lead to sub-optimal design, because the optimizer does not see the full fidelity of the problem....... Finally, AMMF was used in full aero-elastic wind turbine rotor design optimization problem based on the DTU 10 MW reference wind turbine design. Mixed results were achieved for the final study and further work is needed to find the best configuration for AMMF....

  20. A stochastic multi-agent optimization model for energy infrastructure planning under uncertainty and competition.

    Science.gov (United States)

    2017-07-04

    This paper presents a stochastic multi-agent optimization model that supports energy infrastruc- : ture planning under uncertainty. The interdependence between dierent decision entities in the : system is captured in an energy supply chain network, w...

  1. 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.

  2. 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.

  3. METHOD FOR OPTIMAL RESOLUTION OF MULTI-AIRCRAFT CONFLICTS IN THREE-DIMENSIONAL SPACE

    Directory of Open Access Journals (Sweden)

    Denys Vasyliev

    2017-03-01

    Full Text Available Purpose: The risk of critical proximities of several aircraft and appearance of multi-aircraft conflicts increases under current conditions of high dynamics and density of air traffic. The actual problem is a development of methods for optimal multi-aircraft conflicts resolution that should provide the synthesis of conflict-free trajectories in three-dimensional space. Methods: The method for optimal resolution of multi-aircraft conflicts using heading, speed and altitude change maneuvers has been developed. Optimality criteria are flight regularity, flight economy and the complexity of maneuvering. Method provides the sequential synthesis of the Pareto-optimal set of combinations of conflict-free flight trajectories using multi-objective dynamic programming and selection of optimal combination using the convolution of optimality criteria. Within described method the following are defined: the procedure for determination of combinations of aircraft conflict-free states that define the combinations of Pareto-optimal trajectories; the limitations on discretization of conflict resolution process for ensuring the absence of unobservable separation violations. Results: The analysis of the proposed method is performed using computer simulation which results show that synthesized combination of conflict-free trajectories ensures the multi-aircraft conflict avoidance and complies with defined optimality criteria. Discussion: Proposed method can be used for development of new automated air traffic control systems, airborne collision avoidance systems, intelligent air traffic control simulators and for research activities.

  4. 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...

  5. Optimization of orthotropic distributed-mode loudspeaker using attached masses and multi-exciters.

    Science.gov (United States)

    Lu, Guochao; Shen, Yong; Liu, Ziyun

    2012-02-01

    Based on the orthotropic model of the plate, the method to optimize the sound response of the distributed-mode loudspeaker (DML) using the attached masses and the multi-exciters has been investigated. The attached masses method will rebuild the modes distribution of the plate, based on which multi-exciter method will smooth the sound response. The results indicate that the method can be used to optimize the sound response of the DML. © 2012 Acoustical Society of America

  6. An Evolutionary Approach for Optimizing Hierarchical Multi-Agent System Organization

    OpenAIRE

    Shen, Zhiqi; Yu, Ling; Yu, Han

    2014-01-01

    It has been widely recognized that the performance of a multi-agent system is highly affected by its organization. A large scale system may have billions of possible ways of organization, which makes it impractical to find an optimal choice of organization using exhaustive search methods. In this paper, we propose a genetic algorithm aided optimization scheme for designing hierarchical structures of multi-agent systems. We introduce a novel algorithm, called the hierarchical genetic algorithm...

  7. 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.

  8. An optimal multi-channel memory controller for real-time systems

    NARCIS (Netherlands)

    Gomony, M.D.; Akesson, K.B.; Goossens, K.G.W.

    2013-01-01

    Optimal utilization of a multi-channel memory, such as Wide IO DRAM, as shared memory in multi-processor platforms depends on the mapping of memory clients to the memory channels, the granularity at which the memory requests are interleaved in each channel, and the bandwidth and memory capacity

  9. Design of shared unit-dose drug distribution network using multi-level particle swarm optimization.

    Science.gov (United States)

    Chen, Linjie; Monteiro, Thibaud; Wang, Tao; Marcon, Eric

    2018-03-01

    Unit-dose drug distribution systems provide optimal choices in terms of medication security and efficiency for organizing the drug-use process in large hospitals. As small hospitals have to share such automatic systems for economic reasons, the structure of their logistic organization becomes a very sensitive issue. In the research reported here, we develop a generalized multi-level optimization method - multi-level particle swarm optimization (MLPSO) - to design a shared unit-dose drug distribution network. Structurally, the problem studied can be considered as a type of capacitated location-routing problem (CLRP) with new constraints related to specific production planning. This kind of problem implies that a multi-level optimization should be performed in order to minimize logistic operating costs. Our results show that with the proposed algorithm, a more suitable modeling framework, as well as computational time savings and better optimization performance are obtained than that reported in the literature on this subject.

  10. Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations

    Directory of Open Access Journals (Sweden)

    Nah-Oak Song

    2015-08-01

    Full Text Available We propose an optimal electric energy management of a cooperative multi-microgrid community with sequentially coordinated operations. The sequentially coordinated operations are suggested to distribute computational burden and yet to make the optimal 24 energy management of multi-microgrids possible. The sequential operations are mathematically modeled to find the optimal operation conditions and illustrated with physical interpretation of how to achieve optimal energy management in the cooperative multi-microgrid community. This global electric energy optimization of the cooperative community is realized by the ancillary internal trading between the microgrids in the cooperative community which reduces the extra cost from unnecessary external trading by adjusting the electric energy production amounts of combined heat and power (CHP generators and amounts of both internal and external electric energy trading of the cooperative community. A simulation study is also conducted to validate the proposed mathematical energy management models.

  11. The Ecology of Human Fear: Survival Optimization and the Nervous System

    Directory of Open Access Journals (Sweden)

    Dean eMobbs

    2015-03-01

    Full Text Available We propose a Survival Optimization System (SOS to account for the strategies that humans and other animals use to defend against recurring and novel threats. The SOS attempts to merge ecological models that define a repertoire of contextually relevant threat induced survival behaviors with contemporary approaches to human affective science. We first propose that the goal of the nervous system is to reduce surprise and optimize actions by (i predicting the sensory landscape, through simulation of possible encounters with threat, selecting appropriate action by pre-encounter avoidance and (ii prevention strategies in which the organism manufactures safe environments. When a potential threat is encountered the (iii threat orienting system is engaged to determine whether the organism ignores the stimulus or switches into a process of (iv assessment, where the organism monitors the stimulus, weighs the threat value, predicts the actions of the threat, searches for safety, and guides behavioral actions crucial to directed escape. When under imminent attack, (v defensive systems evoke fast reflexive indirect escape behaviors (i.e. fight or flight. This cascade of responses to threat of increasing magnitude are underwritten by an interconnected neural architecture that extends from cortical and hippocampal circuits, to attention, action and threat systems including the amygdala, striatum, and hard-wired defensive systems in the midbrain. The SOS also includes a modulatory feature consisting of cognitive appraisal systems that flexibly guide perception, risk and action. Moreover, personal and vicarious threat encounters fine-tune avoidance behaviors via model-based learning, with higher organisms bridging data to reduce face-to-face encounters with predators. Our theory unifies the divergent field of human affective science, proposing the highly integrated, interconnected nervous systems are optimized to avoid ecological dangers.

  12. 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.

  13. 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.

  14. Multi-Material Design Optimization of Composite Structures

    DEFF Research Database (Denmark)

    Hvejsel, Christian Frier

    properties. The modeling encompasses discrete orientationing of orthotropic materials, selection between different distinct materials as well as removal of material representing holes in the structure within a unified parametrization. The direct generalization of two-phase topology optimization to any number...... of a relaxation-based search heuristic that accelerates a Generalized Benders' Decomposition technique for global optimization and enables the solution of medium-scale problems to global optimality. Improvements in the ability to solve larger problems to global optimality are found and potentially further...... improvements may be obtained with this technique in combination with cheaper heuristics....

  15. 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.

  16. Optimal design of a multi-product biorefinery system

    DEFF Research Database (Denmark)

    Zondervan, E.; Nawaz, Mehboob; de Haan, André B.

    2011-01-01

    In this paper we propose a biorefinery optimization model that can be used to find the optimal processing route for the production of ethanol, butanol, succinic acid and blends of these chemicals with fossil fuel based gasoline. The approach unites transshipment models with a superstructure...

  17. 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...

  18. 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

  19. Multi-period mean–variance portfolio optimization based on Monte-Carlo simulation

    NARCIS (Netherlands)

    F. Cong (Fei); C.W. Oosterlee (Kees)

    2016-01-01

    htmlabstractWe propose a simulation-based approach for solving the constrained dynamic mean– variance portfolio managemen tproblem. For this dynamic optimization problem, we first consider a sub-optimal strategy, called the multi-stage strategy, which can be utilized in a forward fashion. Then,

  20. 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.

  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. Nonlinear Dynamic in an Ecological System with Impulsive Effect and Optimal Foraging

    Directory of Open Access Journals (Sweden)

    Min Zhao

    2014-01-01

    Full Text Available The population dynamics of a three-species ecological system with impulsive effect are investigated. Using the theories of impulsive equations and small-amplitude perturbation scales, the conditions for the system to be permanent when the number of predators released is less than some critical value can be obtained. Furthermore, because the predator in the system follows the predictions of optimal foraging theory, it follows that optimal foraging promotes species coexistence. In particular, the less beneficial prey can support the predator alone when the more beneficial prey goes extinct. Moreover, the influences of the impulsive effect and optimal foraging on inherent oscillations are studied using simulation, which reveals rich dynamic behaviors such as period-halving bifurcations, a chaotic band, a periodic window, and chaotic crises. In addition, the largest Lyapunov exponent and the power spectra of the strange attractor, which can help analyze the chaotic dynamic behavior of the model, are investigated. This information will be useful for studying the dynamic complexity of ecosystems.

  3. Optimism and well-being: a prospective multi-method and multi-dimensional examination of optimism as a resilience factor following the occurrence of stressful life events.

    Science.gov (United States)

    Kleiman, Evan M; Chiara, Alexandra M; Liu, Richard T; Jager-Hyman, Shari G; Choi, Jimmy Y; Alloy, Lauren B

    2017-02-01

    Optimism has been conceptualised variously as positive expectations (PE) for the future , optimistic attributions , illusion of control , and self-enhancing biases. Relatively little research has examined these multiple dimensions of optimism in relation to psychological and physical health. The current study assessed the multi-dimensional nature of optimism within a prospective vulnerability-stress framework. Initial principal component analyses revealed the following dimensions: PEs, Inferential Style (IS), Sense of Invulnerability (SI), and Overconfidence (O). Prospective follow-up analyses demonstrated that PE was associated with fewer depressive episodes and moderated the effect of stressful life events on depressive symptoms. SI also moderated the effect of life stress on anxiety symptoms. Generally, our findings indicated that optimism is a multifaceted construct and not all forms of optimism have the same effects on well-being. Specifically, our findings indicted that PE may be the most relevant to depression, whereas SI may be the most relevant to anxiety.

  4. Heat and mass transfer intensification and shape optimization a multi-scale approach

    CERN Document Server

    2013-01-01

    Is the heat and mass transfer intensification defined as a new paradigm of process engineering, or is it just a common and old idea, renamed and given the current taste? Where might intensification occur? How to achieve intensification? How the shape optimization of thermal and fluidic devices leads to intensified heat and mass transfers? To answer these questions, Heat & Mass Transfer Intensification and Shape Optimization: A Multi-scale Approach clarifies  the definition of the intensification by highlighting the potential role of the multi-scale structures, the specific interfacial area, the distribution of driving force, the modes of energy supply and the temporal aspects of processes.   A reflection on the methods of process intensification or heat and mass transfer enhancement in multi-scale structures is provided, including porous media, heat exchangers, fluid distributors, mixers and reactors. A multi-scale approach to achieve intensification and shape optimization is developed and clearly expla...

  5. Success probability orientated optimization model for resource allocation of the technological innovation multi-project system

    Institute of Scientific and Technical Information of China (English)

    Weixu Dai; Weiwei Wu; Bo Yu; Yunhao Zhu

    2016-01-01

    A success probability orientated optimization model for resource al ocation of the technological innovation multi-project system is studied. Based on the definition of the technological in-novation multi-project system, the leveling optimization of cost and success probability is set as the objective of resource al ocation. The cost function and the probability function of the optimization model are constructed. Then the objective function of the model is constructed and the solving process is explained. The model is applied to the resource al ocation of an enterprise’s technological innovation multi-project system. The results show that the pro-posed model is more effective in rational resource al ocation, and is more applicable in maximizing the utility of the technological innovation multi-project system.

  6. 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...

  7. Optimized multi area AGC simulation in restructured power systems

    International Nuclear Information System (INIS)

    Bhatt, Praghnesh; Roy, Ranjit; Ghoshal, S.P.

    2010-01-01

    In this paper, the traditional automatic generation control loop with modifications is incorporated for simulating automatic generation control (AGC) in restructured power system. Federal energy regulatory commission (FERC) encourages an open market system for price based operation. FERC has issued a notice for proposed rulemaking of various ancillary services. One of these ancillary services is load following with frequency control which comes broadly under Automatic Generation Control in deregulated regime. The concept of DISCO participation matrix is used to simulate the bilateral contracts in the three areas and four area diagrams. Hybrid particle swarm optimization is used to obtain optimal gain parameters for optimal transient performance. (author)

  8. 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.

  9. Toward an Ecologically Optimized N:P Recovery from Wastewater by Microalgae

    Science.gov (United States)

    Fernandes, Tânia V.; Suárez-Muñoz, María; Trebuch, Lukas M.; Verbraak, Paul J.; Van de Waal, Dedmer B.

    2017-01-01

    Global stores of important resources such as phosphorus (P) are being rapidly depleted, while the excessive use of nutrients has led to the enrichment of surface waters worldwide. Ideally, nutrients would be recovered from wastewater, which will not only prevent eutrophication but also provide access to alternative nutrient stores. Current state-of-the-art wastewater treatment technologies are effective in removing these nutrients from wastewater, yet they can only recover P and often in an insufficient way. Microalgae, however, can effectively assimilate P and nitrogen (N), as well as other macro- and micronutrients, allowing these nutrients to be recovered into valuable products that can be used to close nutrient cycles (e.g., fertilizer, bioplastics, color dyes, and bulk chemicals). Here, we show that the green alga Chlorella sorokiniana is able to remove all inorganic N and P present in concentrated toilet wastewater (i.e., black water) with N:P ratios ranging between 15 and 26. However, the N and P uptake by the algae is imbalanced relative to the wastewater N:P stoichiometry, resulting in a rapid removal of P but relatively slower removal of N. Here, we discuss how ecological principles such as ecological stoichiometry and resource-ratio theory may help optimize N:P removal and allow for more effective recovery of N and P from black water. PMID:28955317

  10. Toward an Ecologically Optimized N:P Recovery from Wastewater by Microalgae

    Directory of Open Access Journals (Sweden)

    Tânia V. Fernandes

    2017-09-01

    Full Text Available Global stores of important resources such as phosphorus (P are being rapidly depleted, while the excessive use of nutrients has led to the enrichment of surface waters worldwide. Ideally, nutrients would be recovered from wastewater, which will not only prevent eutrophication but also provide access to alternative nutrient stores. Current state-of-the-art wastewater treatment technologies are effective in removing these nutrients from wastewater, yet they can only recover P and often in an insufficient way. Microalgae, however, can effectively assimilate P and nitrogen (N, as well as other macro- and micronutrients, allowing these nutrients to be recovered into valuable products that can be used to close nutrient cycles (e.g., fertilizer, bioplastics, color dyes, and bulk chemicals. Here, we show that the green alga Chlorella sorokiniana is able to remove all inorganic N and P present in concentrated toilet wastewater (i.e., black water with N:P ratios ranging between 15 and 26. However, the N and P uptake by the algae is imbalanced relative to the wastewater N:P stoichiometry, resulting in a rapid removal of P but relatively slower removal of N. Here, we discuss how ecological principles such as ecological stoichiometry and resource-ratio theory may help optimize N:P removal and allow for more effective recovery of N and P from black water.

  11. The optimal ecological factors and the denitrification populationof a denitrifying process for sulfate reducing bacteriainhibition

    Science.gov (United States)

    Li, Chunying

    2018-02-01

    SRB have great negative impacts on the oil production in Daqing Oil field. A continuous-flow anaerobic baffled reactors (ABR) are applied to investigate the feasibility and optimal ecological factors for the inhibition of SRB by denitrifying bacteria (DNB). The results showed that the SO42- to NO3- concentration ratio (SO42-/NO3-) are the most important ecological factor. The input of NO3- and lower COD can enhance the inhibition of S2-production effectively. The effective time of sulfate reduction is 6 h. Complete inhibition of SRB is obtained when the influent COD concentration is 600 mg/L, the SO42-/NO3- is 1/1 (600 mg/L for each), N is added simultaneously in the 2# and the 5# ABR chambers. By extracting the total DNA of wastewater from the effective chamber, 16SrDNA clones of a bacterium had been constructed. It is showed that the Proteobacteria accounted for eighty- four percent of the total clones. The dominant species was the Neisseria. Sixteen percent of the total clones were the Bacilli of Frimicutes. It indicated that DNB was effective and feasible for SRB inhibition.

  12. 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)

  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. 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.

  15. Mortgage Loan Portfolio Optimization Using Multi-Stage Stochastic Programming

    DEFF Research Database (Denmark)

    Rasmussen, Kourosh Marjani; Clausen, Jens

    2007-01-01

    We consider the dynamics of the Danish mortgage loan system and propose several models to reflect the choices of a mortgagor as well as his attitude towards risk. The models are formulated as multi stage stochastic integer programs, which are difficult to solve for more than 10 stages. Scenario...

  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 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.)

  18. Optimization Design of Multi-Parameters in Rail Launcher System

    OpenAIRE

    Yujiao Zhang; Weinan Qin; Junpeng Liao; Jiangjun Ruan

    2014-01-01

    Today the energy storage systems are still encumbering, therefore it is useful to think about the optimization of a railgun system in order to achieve the best performance with the lowest energy input. In this paper, an optimal design method considering 5 parameters is proposed to improve the energy conversion efficiency of a simple railgun. In order to avoid costly trials, the field- circuit method is employed to analyze the operations of different structural railguns with different paramete...

  19. Multi-objective and multi-criteria optimization for power generation expansion planning with CO2 mitigation in Thailand

    Directory of Open Access Journals (Sweden)

    Kamphol Promjiraprawat

    2013-06-01

    Full Text Available In power generation expansion planning, electric utilities have encountered the major challenge of environmental awareness whilst being concerned with budgetary burdens. The approach for selecting generating technologies should depend on economic and environmental constraint as well as externalities. Thus, the multi-objective optimization becomes a more attractive approach. This paper presents a hybrid framework of multi-objective optimization and multi-criteria decision making to solve power generation expansion planning problems in Thailand. In this paper, CO2 emissions and external cost are modeled as a multi-objective optimization problem. Then the analytic hierarchy process is utilized to determine thecompromised solution. For carbon capture and storage technology, CO2 emissions can be mitigated by 74.7% from the least cost plan and leads to the reduction of the external cost of around 500 billion US dollars over the planning horizon. Results indicate that the proposed approach provides optimum cost-related CO2 mitigation plan as well as external cost.

  20. Ecological tax reform - an optimal solution?. Critical remarks on the DIW study ''Economic effects of an ecological tax reform''

    International Nuclear Information System (INIS)

    Boehringer, C.; Fahl, H.; Voss, A.

    1994-01-01

    Through the latest expertise of the German institute for economic research (DIW) regarding an ecological tax reform, the discussion about a tax system considering the shortage of the ressource environment and a deficiency of demand regarding the ressource work is newly provoked. The focus of this article is a critical dealing with the methodical procedure of the German institute for economic reserarch when analyzing the national economic effect of a concretely formulated ecological tax law scenario. When assessing the overall economic consequences of a tax reform, it is recommended to use an analysis instrument, which is farly more consistent compared to the instruments by DIW, and which is more problem adequate through the increased resort to financial knowledge. Based on the obvious weakness of the DIW study, a more extensive comprehension for an ecological tax reform is pleaded for, standing out for an application of taxes based on division of labour and oriented to the objective. (orig./UA) [de

  1. 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.

  2. Transceiver Optimization for Multi-Antenna Deviceto-Device Communications

    Institute of Scientific and Technical Information of China (English)

    Daohua Zhu; Yajuan Guo; Lei Wei; Chaoyang Zhu; Biyao Huang; Wei Xu; Chunming Zhao

    2016-01-01

    It has been shown that the deployment of device-to-device (D2D) communication in cellular systems can provide better support for local services.However,improper design of the hybrid system may cause severe interference between cellular and D2D links.In this paper,we consider transceiver design for the system employing multiple antennas to mitigate the interference.The precoder and decoder matrices are optimized in terms of sum mean squared error (MSE) and capacity,respectively.For the MSE minimization problem,we present an alternative transceiver optimization algorithm.While for the non-convex capacity maximization problem,we decompose the primal problem into a sequence of standard convex quadratic programs for efficient optimization.The evaluation of our proposed algorithms for performance enhancement of the entire D2D integrated cellular system is carried out through simulations.

  3. Genetic optimization of steam multi-turbines system

    International Nuclear Information System (INIS)

    Olszewski, Pawel

    2014-01-01

    Optimization analysis of partially loaded cogeneration, multiple-stages steam turbines system was numerically investigated by using own-developed code (C++). The system can be controlled by following variables: fresh steam temperature, pressure, and flow rates through all stages in steam turbines. Five various strategies, four thermodynamics and one economical, which quantify system operation, were defined and discussed as an optimization functions. Mathematical model of steam turbines calculates steam properties according to the formulation proposed by the International Association for the Properties of Water and Steam. Genetic algorithm GENOCOP was implemented as a solving engine for non–linear problem with handling constrains. Using formulated methodology, example solution for partially loaded system, composed of five steam turbines (30 input variables) with different characteristics, was obtained for five strategies. The genetic algorithm found multiple solutions (various input parameters sets) giving similar overall results. In real application it allows for appropriate scheduling of machine operation that would affect equable time load of every system compounds. Also based on these results three strategies where chosen as the most complex: the first thermodynamic law energy and exergy efficiency maximization and total equivalent energy minimization. These strategies can be successfully used in optimization of real cogeneration applications. - Highlights: • Genetic optimization model for a set of five various steam turbines was presented. • Four various thermodynamic optimization strategies were proposed and discussed. • Operational parameters (steam pressure, temperature, flow) influence was examined. • Genetic algorithm generated optimal solutions giving the best estimators values. • It has been found that similar energy effect can be obtained for various inputs

  4. Optimal planning of integrated multi-energy systems

    DEFF Research Database (Denmark)

    van Beuzekom, I.; Gibescu, M.; Pinson, Pierre

    2017-01-01

    In this paper, a mathematical approach for the optimal planning of integrated energy systems is proposed. In order to address the challenges of future, RES-dominated energy systems, the model deliberates between the expansion of traditional energy infrastructures, the integration...... and sustainability goals for 2030 and 2045. Optimal green- and brownfield designs for a district's future integrated energy system are compared using a one-step, as well as a two-step planning approach. As expected, the greenfield designs are more cost efficient, as their results are not constrained by the existing...

  5. 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

  6. Game-theoretic learning and distributed optimization in memoryless multi-agent systems

    CERN Document Server

    Tatarenko, Tatiana

    2017-01-01

    This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during scommunication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space. .

  7. 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)

  8. 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.

  9. 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

  10. 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

  11. 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.

  12. An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

    Science.gov (United States)

    Utama, D. N.; Ani, N.; Iqbal, M. M.

    2018-03-01

    Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

  13. 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

  14. 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) ...

  15. Scenario tree generation and multi-asset financial optimization problems

    DEFF Research Database (Denmark)

    Geyer, Alois; Hanke, Michael; Weissensteiner, Alex

    2013-01-01

    We compare two popular scenario tree generation methods in the context of financial optimization: moment matching and scenario reduction. Using a simple problem with a known analytic solution, moment matching-when ensuring absence of arbitrage-replicates this solution precisely. On the other hand...

  16. Optimization of multi-response dynamic systems integrating multiple ...

    African Journals Online (AJOL)

    It also results in better optimization performance than back-propagation neural network-based approach and data mining-based approach reported by the past researchers. Keywords: multiple responses, multiple regression, weighted dynamic signal-to-noise ratio, performance measure modelling, response function ...

  17. 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.

  18. Preliminary study for a nuclear multi-cycle reload optimization system

    International Nuclear Information System (INIS)

    Baptista, Rafael Pereira; Lima, Alan Miranda M. de; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto

    2007-01-01

    Fuel assemblies in a reactor are discharged normally after several fuel cycles. This happens because of the concentration of fissile material existing in the fuel assemblies in the core decreases to values such that it is not more possible to keep the reactor operating producing energy at normal rated power. Therefore, the refueling optimization for a nuclear power plant is in fact a multi-cycle problem. A typical multi-cycle reload optimization depends on several kinds of relationships: one is the relationship between the locations where the fuel assemblies are placed for a specified fuel cycle; another is the relationship between fuel loading patterns for the subsequent fuel cycles. This makes the problem very complex and difficult to solve. Until the moment, all the presented proposals for solution are far from solving the multi-cycle optimization problems in reactor fuel management. In this work, we will show preliminary studies of possible solutions for a typical multi-cycle reload optimization problem trying to consider most important restrictions of a real model. In the initial comparisons, the optimization results will be compared with those obtained by the successive single cycle optimizations. (author)

  19. Extended great deluge algorithm for the imperfect preventive maintenance optimization of multi-state systems

    International Nuclear Information System (INIS)

    Nahas, Nabil; Khatab, Abdelhakim; Ait-Kadi, Daoud; Nourelfath, Mustapha

    2008-01-01

    This paper deals with preventive maintenance optimization problem for multi-state systems (MSS). This problem was initially addressed and solved by Levitin and Lisnianski [Optimization of imperfect preventive maintenance for multi-state systems. Reliab Eng Syst Saf 2000;67:193-203]. It consists on finding an optimal sequence of maintenance actions which minimizes maintenance cost while providing the desired system reliability level. This paper proposes an approach which improves the results obtained by genetic algorithm (GENITOR) in Levitin and Lisnianski [Optimization of imperfect preventive maintenance for multi-state systems. Reliab Eng Syst Saf 2000;67:193-203]. The considered MSS have a range of performance levels and their reliability is defined to be the ability to meet a given demand. This reliability is evaluated by using the universal generating function technique. An optimization method based on the extended great deluge algorithm is proposed. This method has the advantage over other methods to be simple and requires less effort for its implementation. The developed algorithm is compared to than in Levitin and Lisnianski [Optimization of imperfect preventive maintenance for multi-state systems. Reliab Eng Syst Saf 2000;67:193-203] by using a reference example and two newly generated examples. This comparison shows that the extended great deluge gives the best solutions (i.e. those with minimal costs) for 8 instances among 10

  20. Optimization Design of Multi-Parameters in Rail Launcher System

    Directory of Open Access Journals (Sweden)

    Yujiao Zhang

    2014-05-01

    Full Text Available Today the energy storage systems are still encumbering, therefore it is useful to think about the optimization of a railgun system in order to achieve the best performance with the lowest energy input. In this paper, an optimal design method considering 5 parameters is proposed to improve the energy conversion efficiency of a simple railgun. In order to avoid costly trials, the field- circuit method is employed to analyze the operations of different structural railguns with different parameters respectively. And the orthogonal test approach is used to guide the simulation for choosing the better parameter combinations, as well reduce the calculation cost. The research shows that the proposed method gives a better result in the energy efficiency of the system. To improve the energy conversion efficiency of electromagnetic rail launchers, the selection of more parameters must be considered in the design stage, such as the width, height and length of rail, the distance between rail pair, and pulse forming inductance. However, the relationship between these parameters and energy conversion efficiency cannot be directly described by one mathematical expression. So optimization methods must be applied to conduct design. In this paper, a rail launcher with five parameters was optimized by using orthogonal test method. According to the arrangement of orthogonal table, the better parameters’ combination can be obtained through less calculation. Under the condition of different parameters’ value, field and circuit simulation analysis were made. The results show that the energy conversion efficiency of the system is increased by 71.9 % after parameters optimization.

  1. 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.

  2. Optimization of the scheme for natural ecology planning of urban rivers based on ANP (analytic network process) model.

    Science.gov (United States)

    Zhang, Yichuan; Wang, Jiangping

    2015-07-01

    Rivers serve as a highly valued component in ecosystem and urban infrastructures. River planning should follow basic principles of maintaining or reconstructing the natural landscape and ecological functions of rivers. Optimization of planning scheme is a prerequisite for successful construction of urban rivers. Therefore, relevant studies on optimization of scheme for natural ecology planning of rivers is crucial. In the present study, four planning schemes for Zhaodingpal River in Xinxiang City, Henan Province were included as the objects for optimization. Fourteen factors that influenced the natural ecology planning of urban rivers were selected from five aspects so as to establish the ANP model. The data processing was done using Super Decisions software. The results showed that important degree of scheme 3 was highest. A scientific, reasonable and accurate evaluation of schemes could be made by ANP method on natural ecology planning of urban rivers. This method could be used to provide references for sustainable development and construction of urban rivers. ANP method is also suitable for optimization of schemes for urban green space planning and design.

  3. 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.

  4. 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.

  5. Online optimization of a multi-conversion-level DC home microgrid for system efficiency enhancement

    DEFF Research Database (Denmark)

    Boscaino, V.; Guerrero, J. M.; Ciornei, I.

    2017-01-01

    stages, three paralleled DC/DC converters are implemented. A Genetic Algorithm performs the on-line optimization of the DC network’s global efficiency, generating the optimal current sharing ratios of the concurrent power converters. The overall DC/DC conversion system including the optimization section......In this paper, an on-line management system for the optimal efficiency operation of a multi-bus DC home distribution system is proposed. The operation of the system is discussed with reference to a distribution system with two conversion stages and three voltage levels. In each of the conversion...

  6. 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...

  7. 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.

  8. Derivation of optimal joint operating rules for multi-purpose multi-reservoir water-supply system

    Science.gov (United States)

    Tan, Qiao-feng; Wang, Xu; Wang, Hao; Wang, Chao; Lei, Xiao-hui; Xiong, Yi-song; Zhang, Wei

    2017-08-01

    The derivation of joint operating policy is a challenging task for a multi-purpose multi-reservoir system. This study proposed an aggregation-decomposition model to guide the joint operation of multi-purpose multi-reservoir system, including: (1) an aggregated model based on the improved hedging rule to ensure the long-term water-supply operating benefit; (2) a decomposed model to allocate the limited release to individual reservoirs for the purpose of maximizing the total profit of the facing period; and (3) a double-layer simulation-based optimization model to obtain the optimal time-varying hedging rules using the non-dominated sorting genetic algorithm II, whose objectives were to minimize maximum water deficit and maximize water supply reliability. The water-supply system of Li River in Guangxi Province, China, was selected for the case study. The results show that the operating policy proposed in this study is better than conventional operating rules and aggregated standard operating policy for both water supply and hydropower generation due to the use of hedging mechanism and effective coordination among multiple objectives.

  9. 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.

  10. 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.

  11. Multi-physics and multi-objective design of heterogeneous SFR core: development of an optimization method under uncertainty

    International Nuclear Information System (INIS)

    Ammar, Karim

    2014-01-01

    Since Phenix shutting down in 2010, CEA does not have Sodium Fast Reactor (SFR) in operating condition. According to global energetic challenge and fast reactor abilities, CEA launched a program of industrial demonstrator called ASTRID (Advanced Sodium Technological Reactor for Industrial Demonstration), a reactor with electric power capacity equal to 600 MW. Objective of the prototype is, in first to be a response to environmental constraints, in second demonstrates the industrial viability of SFR reactor. The goal is to have a safety level at least equal to 3. generation reactors. ASTRID design integrates Fukushima feedback; Waste reprocessing (with minor actinide transmutation) and it linked industry. Installation safety is the priority. In all cases, no radionuclide should be released into environment. To achieve this objective, it is imperative to predict the impact of uncertainty sources on reactor behaviour. In this context, this thesis aims to develop new optimization methods for SFR cores. The goal is to improve the robustness and reliability of reactors in response to existing uncertainties. We will use ASTRID core as reference to estimate interest of new methods and tools developed. The impact of multi-Physics uncertainties in the calculation of the core performance and the use of optimization methods introduce new problems: How to optimize 'complex' cores (i.e. associated with design spaces of high dimensions with more than 20 variable parameters), taking into account the uncertainties? What is uncertainties behaviour for optimization core compare to reference core? Taking into account uncertainties, optimization core are they still competitive? Optimizations improvements are higher than uncertainty margins? The thesis helps to develop and implement methods necessary to take into account uncertainties in the new generation of simulation tools. Statistical methods to ensure consistency of complex multi-Physics simulation results are also

  12. 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

  13. 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.

  14. The genotypic structure of a multi-host bumblebee parasite suggests a role for ecological niche overlap.

    Directory of Open Access Journals (Sweden)

    Rahel M Salathé

    Full Text Available The genotypic structure of parasite populations is an important determinant of ecological and evolutionary dynamics of host-parasite interactions with consequences for pest management and disease control. Genotypic structure is especially interesting where multiple hosts co-exist and share parasites. We here analyze the natural genotypic distribution of Crithidia bombi, a trypanosomatid parasite of bumblebees (Bombus spp., in two ecologically different habitats over a time period of three years. Using an algorithm to reconstruct genotypes in cases of multiple infections, and combining these with directly identified genotypes from single infections, we find a striking diversity of infection for both data sets, with almost all multi-locus genotypes being unique, and are inferring that around half of the total infections are resulting from multiple strains. Our analyses further suggest a mixture of clonality and sexuality in natural populations of this parasite species. Finally, we ask whether parasite genotypes are associated with host species (the phylogenetic hypothesis or whether ecological factors (niche overlap in flower choice shape the distribution of parasite genotypes (the ecological hypothesis. Redundancy analysis demonstrates that in the region with relatively high parasite prevalence, both host species identity and niche overlap are equally important factors shaping the distribution of parasite strains, whereas in the region with lower parasite prevalence, niche overlap more strongly contributes to the distribution observed. Overall, our study underlines the importance of ecological factors in shaping the natural dynamics of host-parasite systems.

  15. 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.

  16. Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    2004-01-01

    We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

  17. Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    2006-01-01

    We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

  18. 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

  19. 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.

  20. Multi-stage and multi-response process optimization in Taguchi ...

    African Journals Online (AJOL)

    Product quality is all about reducing variations of key performance indicators. However, product manufacturing often, requires multiple processes with multiple indicators, which make reducing variation a complex task. There are tools used to optimize a single stage process independently which ensure local optimization ...

  1. Optimal Scheduling of Biogas-Solar-Wind Renewable Portfolio for Multi-Carrier Energy Supplies

    DEFF Research Database (Denmark)

    Zhou, Bin; Xu, Da; Li, Canbing

    2018-01-01

    the mitigation of renewable intermittency and the efficient utilization of batteries, and a multi-carrier generation scheduling scheme is further presented to dynamically optimize dispatch factors in the coupling matrix for energy-efficient con-version and storage, while different energy demands of end......This paper proposes a multi-source multi-product framework for coupled multi-carrier energy supplies with a biogas-solar-wind hybrid renewable system. In this framework, the biogas-solar-wind complementarities are fully exploited based on digesting thermodynamic effects for the synergetic...... interactions of electricity, gas and heating energy flows, and a coupling matrix is formulated for the modeling of production, conversion, storage, and consumption of different energy carriers. The multi-energy complementarity of biogas-solar-wind renewable portfolio can be utilized to facilitate...

  2. Optimal routing of IP packets to multi-homed servers

    International Nuclear Information System (INIS)

    Swartz, K.L.

    1992-08-01

    Multi-homing, or direct attachment to multiple networks, offers both performance and availability benefits for important servers on busy networks. Exploiting these benefits to their fullest requires a modicum of routing knowledge in the clients. Careful policy control must also be reflected in the routing used within the network to make best use of specialized and often scarce resources. While relatively straightforward in theory, this problem becomes much more difficult to solve in a real network containing often intractable implementations from a variety of vendors. This paper presents an analysis of the problem and proposes a useful solution for a typical campus network. Application of this solution at the Stanford Linear Accelerator Center is studied and the problems and pitfalls encountered are discussed, as are the workarounds used to make the system work in the real world

  3. 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.

  4. 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.

  5. A Gradient Optimization Approach to Adaptive Multi-Robot Control

    Science.gov (United States)

    2009-09-01

    t) - v, (t) = 0 Vi from the first term in the sum, so the network converges to a near-optimal coverage configuration. Furthermore, from Ci(7) Td (t...they are not a function of 7) to get ry&(t)TW[j (TF)ICjIC[ d-l &di(t) = nYdcti( td Since 1V - 0, if limt,, Ai (t) is positive definite (we know the limit...property clearly, examine the magnitude of force exerted by one neighbor (m - 1 = 1) given by IIl f = 0ij - 02i/ llj - P I, and shown in the left of

  6. A Hybrid Algorithm for Optimizing Multi- Modal Functions

    Institute of Scientific and Technical Information of China (English)

    Li Qinghua; Yang Shida; Ruan Youlin

    2006-01-01

    A new genetic algorithm is presented based on the musical performance. The novelty of this algorithm is that a new genetic algorithm, mimicking the musical process of searching for a perfect state of harmony, which increases the robustness of it greatly and gives a new meaning of it in the meantime, has been developed. Combining the advantages of the new genetic algorithm, simplex algorithm and tabu search, a hybrid algorithm is proposed. In order to verify the effectiveness of the hybrid algorithm, it is applied to solving some typical numerical function optimization problems which are poorly solved by traditional genetic algorithms. The experimental results show that the hybrid algorithm is fast and reliable.

  7. Optimization of machining parameters of turning operations based on multi performance criteria

    Directory of Open Access Journals (Sweden)

    N.K.Mandal

    2013-01-01

    Full Text Available The selection of optimum machining parameters plays a significant role to ensure quality of product, to reduce the manufacturing cost and to increase productivity in computer controlled manufacturing process. For many years, multi-objective optimization of turning based on inherent complexity of process is a competitive engineering issue. This study investigates multi-response optimization of turning process for an optimal parametric combination to yield the minimum power consumption, surface roughness and frequency of tool vibration using a combination of a Grey relational analysis (GRA. Confirmation test is conducted for the optimal machining parameters to validate the test result. Various turning parameters, such as spindle speed, feed and depth of cut are considered. Experiments are designed and conducted based on full factorial design of experiment.

  8. 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.

  9. Distributed optimization-based control of multi-agent networks in complex environments

    CERN Document Server

    Zhu, Minghui

    2015-01-01

    This book offers a concise and in-depth exposition of specific algorithmic solutions for distributed optimization based control of multi-agent networks and their performance analysis. It synthesizes and analyzes distributed strategies for three collaborative tasks: distributed cooperative optimization, mobile sensor deployment and multi-vehicle formation control. The book integrates miscellaneous ideas and tools from dynamic systems, control theory, graph theory, optimization, game theory and Markov chains to address the particular challenges introduced by such complexities in the environment as topological dynamics, environmental uncertainties, and potential cyber-attack by human adversaries. The book is written for first- or second-year graduate students in a variety of engineering disciplines, including control, robotics, decision-making, optimization and algorithms and with backgrounds in aerospace engineering, computer science, electrical engineering, mechanical engineering and operations research. Resea...

  10. 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....

  11. Application of multi response optimization with grey relational analysis and fuzzy logic method

    Science.gov (United States)

    Winarni, Sri; Wahyu Indratno, Sapto

    2018-01-01

    Multi-response optimization is an optimization process by considering multiple responses simultaneously. The purpose of this research is to get the optimum point on multi-response optimization process using grey relational analysis and fuzzy logic method. The optimum point is determined from the Fuzzy-GRG (Grey Relational Grade) variable which is the conversion of the Signal to Noise Ratio of the responses involved. The case study used in this research are case optimization of electrical process parameters in electrical disharge machining. It was found that the combination of treatments resulting to optimum MRR and SR was a 70 V gap voltage factor, peak current 9 A and duty factor 0.8.

  12. 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 ...

  13. 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 ...

  14. Optimal Sequential Resource Sharing and Exchange in Multi-Agent Systems

    OpenAIRE

    Xiao, Yuanzhang

    2014-01-01

    Central to the design of many engineering systems and social networks is to solve the underlying resource sharing and exchange problems, in which multiple decentralized agents make sequential decisions over time to optimize some long-term performance metrics. It is challenging for the decentralized agents to make optimal sequential decisions because of the complicated coupling among the agents and across time. In this dissertation, we mainly focus on three important classes of multi-agent seq...

  15. 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...

  16. Energy optimization methodology of multi-chiller plant in commercial buildings

    International Nuclear Information System (INIS)

    Thangavelu, Sundar Raj; Myat, Aung; Khambadkone, Ashwin

    2017-01-01

    This study investigates the potential energy savings in commercial buildings through optimized operation of a multi-chiller plant. The cooling load contributes 45–60% of total power consumption in commercial and office buildings, especially at tropics. The chiller plant operation is not optimal in most of the existing buildings because the chiller plant is either operated at design condition irrespective of the cooling load or optimized locally due to lack of overall chiller plant behavior. In this study, an overall energy model of chiller plant is developed to capture the thermal behavior of all systems and their interactions including the power consumption. An energy optimization methodology is proposed to derive optimized operation decisions for chiller plant at regular intervals based on building thermal load and weather condition. The benefits of proposed energy optimization methodology are examined using case study problems covering different chiller plant configurations. The case studies result confirmed the energy savings achieved through optimized operations is up to 40% for moderate size chiller plant and around 20% for small chiller plant which consequently reduces the energy cost and greenhouse gas emissions. - Highlights: • Energy optimization methodology improves the performance of multi-chiller plant. • Overall energy model of chiller plant accounts all equipment and the interactions. • Operation decisions are derived at regular interval based on time-varying factors. • Three case studies confirmed 20 to 40% of energy savings than conventional method.

  17. multi-stage and multi-response process optimization in taguchi method

    African Journals Online (AJOL)

    Shewit`

    product manufacturing often, requires multiple ... new model with its optimization procedure is proven to ensure ... related with inspection strategies. The third ... determine optimum levels of input parameters in ... under the least possible cost.

  18. Non-Linear Multi-Physics Analysis and Multi-Objective Optimization in Electroheating Applications

    Czech Academy of Sciences Publication Activity Database

    di Barba, P.; Doležel, Ivo; Mognaschi, M. E.; Savini, A.; Karban, P.

    2014-01-01

    Roč. 50, č. 2 (2014), s. 7016604-7016604 ISSN 0018-9464 Institutional support: RVO:61388998 Keywords : coupled multi-physics problems * finite element method * non-linear equations Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.386, year: 2014

  19. Multi-Dimensional Optimization for Cloud Based Multi-Tier Applications

    Science.gov (United States)

    Jung, Gueyoung

    2010-01-01

    Emerging trends toward cloud computing and virtualization have been opening new avenues to meet enormous demands of space, resource utilization, and energy efficiency in modern data centers. By being allowed to host many multi-tier applications in consolidated environments, cloud infrastructure providers enable resources to be shared among these…

  20. A multi-cycle optimization approach for low leakage in-core fuel management

    International Nuclear Information System (INIS)

    Cheng Pingdong; Shen Wei

    1999-01-01

    A new approach was developed to optimize pressurized waster reactor (PWR) low-leakage multi-cycle reload core design. The multi-cycle optimization process is carried out by the following three steps: The first step is a linear programming in search for an optimum power sharing distribution and optimum cycle length distribution for the successive several cycles to yield maximum multi-cycle total cycle length. In the second step, the fuel arrangement and burnable poison (BP) assignment are decoupled by using Haling power distribution and the optimum fuel arrangement is determined at the EOL in the absence of all BPs by employing a linear programming method or direct search method with objective function to force the calculated cycle length to be as close as possible to the optimum single cycle length obtained in the first step and with optimum power sharing distribution as additional constraints during optimization. In the third step, the BP assignment is optimized by the Flexible Tolerance Method (FTM) or linear programming method using the number of BP rods as control variable. The technology employed in the second and third steps was the usual decoupling method used in low-leakage core design. The first step was developed specially for multi-cycle optimization design and discussed in detail. Based on the proposed method a computer code MCYCO was encoded and tested for Qinshan Nuclear Power Plant (QNPP) low leakage reload core design. The multi-cycle optimization method developed, together with the program MCYCO, provides an applicable tool for solving the PWR low leakage reload core design problem

  1. Multi-dimensional optimization of small wind turbine blades

    DEFF Research Database (Denmark)

    Sessarego, Matias; Wood, David

    2015-01-01

    used to reduce the rotor inertia to help minimize starting time. Two airfoils are considered: the 10% thick SG6043 which has excellent lift:drag performance at low Reynolds number and the SD7062 whose extra thickness (14%) has some structural advantages, particularly for the weaker material (c). All......This paper describes a computer method to allow the design of small wind turbine blades for the multiple objectives of rapid starting, efficient power extraction, low noise, and minimal mass. For the sake of brevity, only the first two and the last objectives are considered in this paper....... The optimization aimed to study a range of blade materials, from traditional fibreglass through sustainable alternatives to rapid prototyping plastic. Because starting performance depends on blade inertia, there is a complex interaction between the material properties and the aerodynamics. Example blades of 1.1 m...

  2. Shock Absorbers Multi-Modeling and Suspension Optimization

    Directory of Open Access Journals (Sweden)

    LUPU Ciprian

    2013-05-01

    Full Text Available The standard dampers used by more 90% of vehicles have damping coefficients constant along stroke, so they can’t solve simultaneous all of them, situation solving practically using a relative dampingcoefficient able to made compromise between them. This paper design and simulation testing multi-models of two types of Damp (DSA and VZN. To compare the two types of suspension they are simulated in various road and load conditions. Analysis of simulation results is presente a new VZN shock absorber. This is an invention of the Institute of Mechanics of the Romanian Academy, and patented at European and U.S. [1], [2]. This is Called VZN shock absorber, iscoming from Variable Zeta Necessary acronym, for well moving in all road and load Conditions, Where zeta Represents the relative damping, Which is Adjusted automatically, stepwise, According to the piston positions [3,4,5]. Suspension systems are used in all air and ground transportation to protect that building transportation and cargo transported around against shocks and vibrations induced in the systemfrom the road Modifying damping coefficients (Zeta function piston position, being correlated with vehicle load and road unevenness.

  3. 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

  4. Multi-Attribute Modelling of Economic and Ecological Impacts of Cropping Systems

    NARCIS (Netherlands)

    Bohanec, M.; Dzeroski, S.; Znidarsic, M.; Messéan, A.; Scatasta, S.; Wesseler, J.H.H.

    2004-01-01

    Modelling of economic and ecological impacts of genetically modified crops is a demanding task. We present some preliminary attempts made for the purpose of the ECOGEN project "Soil ecological and economic evaluation of genetically modified crops". One of the goals of the project is to develop a

  5. Ecological restoration of central European mining sites: a summary of a multi-site analysis

    Czech Academy of Sciences Publication Activity Database

    Prach, Karel; Řehounková, Klára; Řehounek, J.; Konvalinková, Petra

    2011-01-01

    Roč. 36, č. 2 (2011), 263-268 ISSN 0142-6397 R&D Projects: GA AV ČR IAA600050702 Institutional research plan: CEZ:AV0Z60050516 Keywords : ecological restoration * mining * succession Subject RIV: EH - Ecology, Behaviour Impact factor: 0.677, year: 2011

  6. 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

  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. 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.

  9. 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.

  10. Trading river services: optimizing dam decisions at the basin scale to improve socio-ecological resilience

    Science.gov (United States)

    Roy, S. G.; Gold, A.; Uchida, E.; McGreavy, B.; Smith, S. M.; Wilson, K.; Blachly, B.; Newcomb, A.; Hart, D.; Gardner, K.

    2017-12-01

    Dam removal has become a cornerstone of environmental restoration practice in the United States. One outcome of dam removal that has received positive attention is restored access to historic habitat for sea-run fisheries, providing a crucial gain in ecosystem resilience. But dams also provide stakeholders with valuable services, and uncertain socio-ecological outcomes can arise if there is not careful consideration of the basin scale trade offs caused by dam removal. In addition to fisheries, dam removals can significantly affect landscape nutrient flux, municipal water storage, recreational use of lakes and rivers, property values, hydroelectricity generation, the cultural meaning of dams, and many other river-based ecosystem services. We use a production possibility frontiers approach to explore dam decision scenarios and opportunities for trading between ecosystem services that are positively or negatively affected by dam removal in New England. Scenarios that provide efficient trade off potentials are identified using a multiobjective genetic algorithm. Our results suggest that for many river systems, there is a significant potential to increase the value of fisheries and other ecosystem services with minimal dam removals, and further increases are possible by including decisions related to dam operations and physical modifications. Run-of-river dams located near the head of tide are often found to be optimal for removal due to low hydroelectric capacity and high impact on fisheries. Conversely, dams with large impoundments near a river's headwaters can be less optimal for dam removal because their value as nitrogen sinks often outweighs the potential value for fisheries. Hydropower capacity is negatively impacted by dam removal but there are opportunities to meet or exceed lost capacity by upgrading preserved hydropower dams. Improving fish passage facilities for dams that are critical for safety or water storage can also reduce impacts on fisheries. Our

  11. 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

  12. Multi-scale Modeling Approach for Design and Optimization of Oleochemical Processes

    DEFF Research Database (Denmark)

    Jones, Mark Nicholas; Forero-Hernandez, Hector Alexander; Sarup, Bent

    2017-01-01

    The primary goal of this work is to present a systematic methodology and software frameworkfor a multi-level approach ranging from process synthesis and modeling throughproperty prediction, to sensitivity analysis, property parameter tuning and optimization.This framework is applied to the follow...

  13. 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...

  14. 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.

  15. 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 ...

  16. Optimal One Bit Time Reversal For UWB Impulse Radio In Multi-User Wireless Communications

    DEFF Research Database (Denmark)

    Nguyen, Hung Tuan

    2008-01-01

    In this paper, with the purpose of further reducing the complexity of the system, while keeping its temporal and spatial focusing performance, we investigate the possibility of using optimal one bit time reversal (TR) system for impulse radio ultra wideband multi-user wireless communications...

  17. Optimal resource allocation and load scheduling for a multi-commodity smart energy system

    NARCIS (Netherlands)

    Blaauwbroek, N.; Nguyen, H.P.; Shi, H.; Kamphuis, I.G.; Kling, W.L.; Konsman, M.J.

    2015-01-01

    The increasing introduction of district heating systems together with hybrid energy appliances as heat pumps and micro-combined heat and power installations, results in new opportunities for optimizing the available resources in multi-commodity smart energy systems, including electricity, heat and

  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. Tomographic Reconstruction from a Few Views: A Multi-Marginal Optimal Transport Approach

    Energy Technology Data Exchange (ETDEWEB)

    Abraham, I., E-mail: isabelle.abraham@cea.fr [CEA Ile de France (France); Abraham, R., E-mail: romain.abraham@univ-orleans.fr; Bergounioux, M., E-mail: maitine.bergounioux@univ-orleans.fr [Université d’Orléans, UFR Sciences, MAPMO, UMR 7349 (France); Carlier, G., E-mail: carlier@ceremade.dauphine.fr [CEREMADE, UMR CNRS 7534, Université Paris IX Dauphine, Pl. de Lattre de Tassigny (France)

    2017-02-15

    In this article, we focus on tomographic reconstruction. The problem is to determine the shape of the interior interface using a tomographic approach while very few X-ray radiographs are performed. We use a multi-marginal optimal transport approach. Preliminary numerical results are presented.

  20. Multi-stage Optimization of Matchings in Trees with Application to Kidney Exchange

    KAUST Repository

    Mankowski, Michal; Moshkov, Mikhail

    2017-01-01

    In this paper, we propose a method for multi-stage optimization of matchings in trees relative to different weight functions that assign positive weights to the edges of the trees. This method can be useful in transplantology where nodes of the tree

  1. Optimization of sound absorbing performance for gradient multi-layer-assembled sintered fibrous absorbers

    Science.gov (United States)

    Zhang, Bo; Zhang, Weiyong; Zhu, Jian

    2012-04-01

    The transfer matrix method, based on plane wave theory, of multi-layer equivalent fluid is employed to evaluate the sound absorbing properties of two-layer-assembled and three-layer-assembled sintered fibrous sheets (generally regarded as a kind of compound absorber or structures). Two objective functions which are more suitable for the optimization of sound absorption properties of multi-layer absorbers within the wider frequency ranges are developed and the optimized results of using two objective functions are also compared with each other. It is found that using the two objective functions, especially the second one, may be more helpful to exert the sound absorbing properties of absorbers at lower frequencies to the best of their abilities. Then the calculation and optimization of sound absorption properties of multi-layer-assembled structures are performed by developing a simulated annealing genetic arithmetic program and using above-mentioned objective functions. Finally, based on the optimization in this work the thoughts of the gradient design over the acoustic parameters- the porosity, the tortuosity, the viscous and thermal characteristic lengths and the thickness of each samples- of porous metals are put forth and thereby some useful design criteria upon the acoustic parameters of each layer of porous fibrous metals are given while applying the multi-layer-assembled compound absorbers in noise control engineering.

  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 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.

  5. Towards Optimal Multi-Dimensional Query Processing with BitmapIndices

    Energy Technology Data Exchange (ETDEWEB)

    Rotem, Doron; Stockinger, Kurt; Wu, Kesheng

    2005-09-30

    Bitmap indices have been widely used in scientific applications and commercial systems for processing complex, multi-dimensional queries where traditional tree-based indices would not work efficiently. This paper studies strategies for minimizing the access costs for processing multi-dimensional queries using bitmap indices with binning. Innovative features of our algorithm include (a) optimally placing the bin boundaries and (b) dynamically reordering the evaluation of the query terms. In addition, we derive several analytical results concerning optimal bin allocation for a probabilistic query model. Our experimental evaluation with real life data shows an average I/O cost improvement of at least a factor of 10 for multi-dimensional queries on datasets from two different applications. Our experiments also indicate that the speedup increases with the number of query dimensions.

  6. Frequency locking of a field-widened Michelson interferometer based on optimal multi-harmonics heterodyning.

    Science.gov (United States)

    Cheng, Zhongtao; Liu, Dong; Zhou, Yudi; Yang, Yongying; Luo, Jing; Zhang, Yupeng; Shen, Yibing; Liu, Chong; Bai, Jian; Wang, Kaiwei; Su, Lin; Yang, Liming

    2016-09-01

    A general resonant frequency locking scheme for a field-widened Michelson interferometer (FWMI), which is intended as a spectral discriminator in a high-spectral-resolution lidar, is proposed based on optimal multi-harmonics heterodyning. By transferring the energy of a reference laser to multi-harmonics of different orders generated by optimal electro-optic phase modulation, the heterodyne signal of these multi-harmonics through the FWMI can reveal the resonant frequency drift of the interferometer very sensitively within a large frequency range. This approach can overcome the locking difficulty induced by the low finesse of the FWMI, thus contributing to excellent locking accuracy and lock acquisition range without any constraint on the interferometer itself. The theoretical and experimental results are presented to verify the performance of this scheme.

  7. METAHEURISTICS FOR OPTIMIZING SAFETY STOCK IN MULTI STAGE INVENTORY SYSTEM

    Directory of Open Access Journals (Sweden)

    Gordan Badurina

    2013-02-01

    Full Text Available Managing the right level of inventory is critical in order to achieve the targeted level of customer service, but it also carries significant cost in supply chain. In majority of cases companies define safety stock on the most downstream level, i.e. the finished product level, using different analytical methods. Safety stock on upstream level, however, usually covers only those problems which companies face on that particular level (uncertainty of delivery, issues in production, etc.. This paper looks into optimizing safety stock in a pharmaceutical supply considering the three stages inventory system. The problem is defined as a single criterion mixed integer programming problem. The objective is to minimize the inventory cost while the service level is predetermined. In order to coordinate inventories at all echelons, the variable representing the so-called service time is introduced. Because of the problem dimensions, metaheuristics based on genetic algorithm and simulated annealing are constructed and compared, using real data from a Croatian pharmaceutical company. The computational results are presented evidencing improvements in minimizing inventory costs.

  8. [Optimal use of peritoneal dialysis with multi-disciplinary management].

    Science.gov (United States)

    Elios Russo, Gaspare; Martinez, A; Mazzaferro, S; Nunzi, A; Testorio, M; Rocca, A R; Lai, S; Morgia, A; Borzacca, B; Gnerre Musto, T

    2013-01-01

    Considering the increasing incidence of chronic kidney disease and the increased use of peritoneal dialysis, we wanted to assess whether the multidisciplinary management of patients in peritoneal dialysis might produce improvement in the quality of patients' lives when compared to management by a routine team of operators. Our study observed 40 patients on peritoneal dialysis in our Department between 2010 and 2012. They were randomly assigned to either group A, the routine team which consisted of a nephrologist and a nurse, or group B, a multidisciplinary team comprising several medical specialists, a nurse, a psychologist and a social worker. Two tests, KDQOL-SF and MMPI-2, were administered to both groups. In group B, the number of days of hospitalization and day hospital were more than 88% lower when compared to group A. The multidisciplinary team achieved better results with the KDQOL-SF test with regards to both emotional and objective dimensions. The Pearson coefficient between the results of the two questionnaires shows how multidisciplinary management can positively influence the perceived well-being of the patient and his or her adherence to treatment. In a multidisciplinary team, each operator, in addition to his or her specific role, also contributes to the achievement of the overall objective, namely of ensuring an optimal quality of life to the patient on peritoneal dialysis thereby allowing these patients to continue their professional and social lives.

  9. A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

    Science.gov (United States)

    Moghaddam, Kamran S.; Usher, John S.

    2011-07-01

    In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

  10. Optimized dose conformation of multi-leaf collimator fields

    International Nuclear Information System (INIS)

    Serago, Christopher F.; Buskirk, Steven J.; Foo, May L.; McLaughlin, Mark P.

    1996-01-01

    Purpose/Objective: Current commercially available multi-leaf collimators (MLC) have leaf widths of about 1 cm. These leaf widths may produce stepped dose gradients at the fields edges at the 50% dose level. Small local perturbations of the dose distribution from the prescribed/expected dose distribution may not be acceptable for some clinical applications. Improvements to the conformation of the MLC dose distribution may be achieved using multiple exposures per MLC field, with either shifting the table/patient position, or rotating the orientation of the MLC jaws between exposures. Material and Methods: Dose distributions for MLC, primary jaws only, and lead alloy block fields were measured with film dosimetry for 6 and 20 MV photon beams in a solid water phantom. Square, circular, and typical clinical prostate, brain, lung, esophagus, and head and neck fields were measured. MLC field shapes were produced using a commercial MLC with a leaf width of 1 cm at the treatment isocenter. The dose per MLC field was delivered in either single (conventional) or multiple exposures. The table(patient) position or the collimator rotation was shifted between exposures when multiple exposure MLC fields were used. Differences in the dose distribution were evaluated at the 90% and 50% isodose level. Displacements of the measured 50% isodose from the prescribed/expected 50% isodose were measured at 5 degree intervals. Results: Measurements of the penumbra at a 10 cm depth for square fields show that using double exposure MLC fields with .5 cm table index decreases the effective penumbra by 1 mm. For clinical shaped fields, displacements between the prescribed/expected 50% isodose and the measured 50% isodose for conventional single exposure MLC fields are measured to be as great as 9 mm, and discrepancies on the order of 5 to 6 mm are common. In contrast, the maximum displacement errors measured with multiple exposure MLC fields are less than 5 mm and rarely more than 4 mm. In some

  11. Establishing ecological and social continuities: new challenges to optimize urban watershed management

    Science.gov (United States)

    Mitroi, V.; de Coninck, A.; Vinçon-Leite, B.; Deroubaix, J.-F.

    2014-09-01

    The (re)construction of the ecological continuity is stated as one of the main objectives of the European Water Framework Directive for watershed management in Europe. Analysing the social, political, technical and scientific processes characterising the implementation of different projects of ecological continuity in two adjacent peri-urban territories in Ile-de-France, we observed science-driven approaches disregarding the social contexts. We show that, in urbanized areas, ecological continuity requires not only important technical and ecological expertise, but also social and political participation to the definition of a common vision and action plan. Being a challenge for both, technical water management institutions and "classical" ecological policies, we propose some social science contributions to deal with ecological unpredictability and reconsider stakeholder resistance to this kind of project.

  12. Establishing ecological and social continuities: new challenges to optimize urban watershed management

    Directory of Open Access Journals (Sweden)

    V. Mitroi

    2014-09-01

    Full Text Available The (reconstruction of the ecological continuity is stated as one of the main objectives of the European Water Framework Directive for watershed management in Europe. Analysing the social, political, technical and scientific processes characterising the implementation of different projects of ecological continuity in two adjacent peri-urban territories in Ile-de-France, we observed science-driven approaches disregarding the social contexts. We show that, in urbanized areas, ecological continuity requires not only important technical and ecological expertise, but also social and political participation to the definition of a common vision and action plan. Being a challenge for both, technical water management institutions and “classical” ecological policies, we propose some social science contributions to deal with ecological unpredictability and reconsider stakeholder resistance to this kind of project.

  13. 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.

  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. Evaluating the Efficiency of a Multi-core Aware Multi-objective Optimization Tool for Calibrating the SWAT Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, X. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Izaurralde, R. C. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zong, Z. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zhao, K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Thomson, A. M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2012-08-20

    The efficiency of calibrating physically-based complex hydrologic models is a major concern in the application of those models to understand and manage natural and human activities that affect watershed systems. In this study, we developed a multi-core aware multi-objective evolutionary optimization algorithm (MAMEOA) to improve the efficiency of calibrating a worldwide used watershed model (Soil and Water Assessment Tool (SWAT)). The test results show that MAMEOA can save about 1-9%, 26-51%, and 39-56% time consumed by calibrating SWAT as compared with sequential method by using dual-core, quad-core, and eight-core machines, respectively. Potential and limitations of MAMEOA for calibrating SWAT are discussed. MAMEOA is open source software.

  16. Multi-machine power system stabilizers design using chaotic optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Shayeghi, H., E-mail: hshayeghi@gmail.co [Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil (Iran, Islamic Republic of); Shayanfar, H.A. [Center of Excellence for Power System Automation and Operation, Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Jalilzadeh, S.; Safari, A. [Technical Engineering Department, Zanjan University, Zanjan (Iran, Islamic Republic of)

    2010-07-15

    In this paper, a multiobjective design of the multi-machine power system stabilizers (PSSs) using chaotic optimization algorithm (COA) is proposed. Chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from the local optimum, is a promising tool for the engineering applications. The PSSs parameters tuning problem is converted to an optimization problem which is solved by a chaotic optimization algorithm based on Lozi map. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed chaotic optimization problem introduces chaos mapping using Lozi map chaotic sequences which increases its convergence rate and resulting precision. Two different objective functions are proposed in this study for the PSSs design problem. The first objective function is the eigenvalues based comprising the damping factor, and the damping ratio of the lightly damped electro-mechanical modes, while the second is the time domain-based multi-objective function. The robustness of the proposed COA-based PSSs (COAPSS) is verified on a multi-machine power system under different operating conditions and disturbances. The results of the proposed COAPSS are demonstrated through eigenvalue analysis, nonlinear time-domain simulation and some performance indices. In addition, the potential and superiority of the proposed method over the classical approach and genetic algorithm is demonstrated.

  17. 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

  18. Bandwidth Optimization of Normal Equation Matrix in Bundle Block Adjustment in Multi-baseline Rotational Photography

    Directory of Open Access Journals (Sweden)

    WANG Xiang

    2016-02-01

    Full Text Available A new bandwidth optimization method of normal equation matrix in bundle block adjustment in multi-baseline rotational close range photography by image index re-sorting is proposed. The equivalent exposure station of each image is calculated by its object space coverage and the relationship with other adjacent images. Then, according to the coordinate relations between equivalent exposure stations, new logical indices of all images are computed, based on which, the optimized bandwidth value can be obtained. Experimental results show that the bandwidth determined by our proposed method is significantly better than its original value, thus the operational efficiency, as well as the memory consumption of multi-baseline rotational close range photography in real-data applications, is optimized to a certain extent.

  19. 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.

  20. 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.

  1. Multi Dimensional Honey Bee Foraging Algorithm Based on Optimal Energy Consumption

    Science.gov (United States)

    Saritha, R.; Vinod Chandra, S. S.

    2017-10-01

    In this paper a new nature inspired algorithm is proposed based on natural foraging behavior of multi-dimensional honey bee colonies. This method handles issues that arise when food is shared from multiple sources by multiple swarms at multiple destinations. The self organizing nature of natural honey bee swarms in multiple colonies is based on the principle of energy consumption. Swarms of multiple colonies select a food source to optimally fulfill the requirements of its colonies. This is based on the energy requirement for transporting food between a source and destination. Minimum use of energy leads to maximizing profit in each colony. The mathematical model proposed here is based on this principle. This has been successfully evaluated by applying it on multi-objective transportation problem for optimizing cost and time. The algorithm optimizes the needs at each destination in linear time.

  2. Optimization of inverse model identification for multi-axial test rig control

    Directory of Open Access Journals (Sweden)

    Müller Tino

    2016-01-01

    Full Text Available Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization.

  3. Optimizing Energy and Modulation Selection in Multi-Resolution Modulation For Wireless Video Broadcast/Multicast

    KAUST Repository

    She, James

    2009-11-01

    Emerging technologies in Broadband Wireless Access (BWA) networks and video coding have enabled high-quality wireless video broadcast/multicast services in metropolitan areas. Joint source-channel coded wireless transmission, especially using hierarchical/superposition coded modulation at the channel, is recognized as an effective and scalable approach to increase the system scalability while tackling the multi-user channel diversity problem. The power allocation and modulation selection problem, however, is subject to a high computational complexity due to the nonlinear formulation and huge solution space. This paper introduces a dynamic programming framework with conditioned parsing, which significantly reduces the search space. The optimized result is further verified with experiments using real video content. The proposed approach effectively serves as a generalized and practical optimization framework that can gauge and optimize a scalable wireless video broadcast/multicast based on multi-resolution modulation in any BWA network.

  4. System, methods and apparatus for program optimization for multi-threaded processor architectures

    Science.gov (United States)

    Bastoul, Cedric; Lethin, Richard A; Leung, Allen K; Meister, Benoit J; Szilagyi, Peter; Vasilache, Nicolas T; Wohlford, David E

    2015-01-06

    Methods, apparatus and computer software product for source code optimization are provided. In an exemplary embodiment, a first custom computing apparatus is used to optimize the execution of source code on a second computing apparatus. In this embodiment, the first custom computing apparatus contains a memory, a storage medium and at least one processor with at least one multi-stage execution unit. The second computing apparatus contains at least two multi-stage execution units that allow for parallel execution of tasks. The first custom computing apparatus optimizes the code for parallelism, locality of operations and contiguity of memory accesses on the second computing apparatus. This Abstract is provided for the sole purpose of complying with the Abstract requirement rules. This Abstract is submitted with the explicit understanding that it will not be used to interpret or to limit the scope or the meaning of the claims.

  5. 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.

  6. Optimizing Energy and Modulation Selection in Multi-Resolution Modulation For Wireless Video Broadcast/Multicast

    KAUST Repository

    She, James; Ho, Pin-Han; Shihada, Basem

    2009-01-01

    Emerging technologies in Broadband Wireless Access (BWA) networks and video coding have enabled high-quality wireless video broadcast/multicast services in metropolitan areas. Joint source-channel coded wireless transmission, especially using hierarchical/superposition coded modulation at the channel, is recognized as an effective and scalable approach to increase the system scalability while tackling the multi-user channel diversity problem. The power allocation and modulation selection problem, however, is subject to a high computational complexity due to the nonlinear formulation and huge solution space. This paper introduces a dynamic programming framework with conditioned parsing, which significantly reduces the search space. The optimized result is further verified with experiments using real video content. The proposed approach effectively serves as a generalized and practical optimization framework that can gauge and optimize a scalable wireless video broadcast/multicast based on multi-resolution modulation in any BWA network.

  7. 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.

  8. Cooperative control of multi-agent systems optimal and adaptive design approaches

    CERN Document Server

    Lewis, Frank L; Hengster-Movric, Kristian; Das, Abhijit

    2014-01-01

    Task complexity, communication constraints, flexibility and energy-saving concerns are all factors that may require a group of autonomous agents to work together in a cooperative manner. Applications involving such complications include mobile robots, wireless sensor networks, unmanned aerial vehicles (UAVs), spacecraft, and so on. In such networked multi-agent scenarios, the restrictions imposed by the communication graph topology can pose severe problems in the design of cooperative feedback control systems.  Cooperative control of multi-agent systems is a challenging topic for both control theorists and practitioners and has been the subject of significant recent research. Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs.  It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design.  B...

  9. Optimized production planning model for a multi-plant cultivation system under uncertainty

    Science.gov (United States)

    Ke, Shunkui; Guo, Doudou; Niu, Qingliang; Huang, Danfeng

    2015-02-01

    An inexact multi-constraint programming model under uncertainty was developed by incorporating a production plan algorithm into the crop production optimization framework under the multi-plant collaborative cultivation system. In the production plan, orders from the customers are assigned to a suitable plant under the constraints of plant capabilities and uncertainty parameters to maximize profit and achieve customer satisfaction. The developed model and solution method were applied to a case study of a multi-plant collaborative cultivation system to verify its applicability. As determined in the case analysis involving different orders from customers, the period of plant production planning and the interval between orders can significantly affect system benefits. Through the analysis of uncertain parameters, reliable and practical decisions can be generated using the suggested model of a multi-plant collaborative cultivation system.

  10. On the optimity of separation cascade for a binary and a multi-component case

    International Nuclear Information System (INIS)

    Song, T.M.; Zeng, S.

    2006-01-01

    The optimity discussed in this article means minimum total interstage flow which is studied for two cases, a binary and a multi-component case, using direct numerical optimizations for countercurrent symmetric cascades with the concentrations of the target component specified in the .feed flow, the product and waste withdrawals In binary separation, the ideal cascade in which there are no mixing losses and whose stages are working under symmetric separation is the optimum cascade that has the minimum total flow However when the separation factor is large, there may not exist an ideal cascade for certain prescribed external parameters. Cascades are optimized numerically to minimize mixing losses and total flows, respectively The results are compared for the minimum mixing losses and the minimum total flow, and analyzed with theoretically derived formulas. For the multi-component case, satisfying the non-mixing condition is impossible. There is a counterpart of the binary ideal cascade named MARC which matches the abundance ratio at mixing points. An optimization example for a four-cornponent mixture separation cascade is analyzed with the first and the last components as the targets, respectively. The results show that MARC is not the optimum cascade for the separation of one certain isotope. The separation power of each stage in the optimized cascades is calculated using several different definitions, and the rationality of these definitions is discussed. The Q-iteration method is used to calculate the concentration distribution in both the binary and the multi-component cases. Ns-2 stage cuts out of the Ns stages of the cascade are the optimization variables in the optimization process and a combination of the simulated annealing and the Hooke-Jeeves method is applied as the optimization technique to find the minimum. (authors)

  11. Framework to evaluate the worth of hydraulic conductivity data for optimal groundwater resources management in ecologically sensitive areas

    Science.gov (United States)

    Feyen, Luc; Gorelick, Steven M.

    2005-03-01

    We propose a framework that combines simulation optimization with Bayesian decision analysis to evaluate the worth of hydraulic conductivity data for optimal groundwater resources management in ecologically sensitive areas. A stochastic simulation optimization management model is employed to plan regionally distributed groundwater pumping while preserving the hydroecological balance in wetland areas. Because predictions made by an aquifer model are uncertain, groundwater supply systems operate below maximum yield. Collecting data from the groundwater system can potentially reduce predictive uncertainty and increase safe water production. The price paid for improvement in water management is the cost of collecting the additional data. Efficient data collection using Bayesian decision analysis proceeds in three stages: (1) The prior analysis determines the optimal pumping scheme and profit from water sales on the basis of known information. (2) The preposterior analysis estimates the optimal measurement locations and evaluates whether each sequential measurement will be cost-effective before it is taken. (3) The posterior analysis then revises the prior optimal pumping scheme and consequent profit, given the new information. Stochastic simulation optimization employing a multiple-realization approach is used to determine the optimal pumping scheme in each of the three stages. The cost of new data must not exceed the expected increase in benefit obtained in optimal groundwater exploitation. An example based on groundwater management practices in Florida aimed at wetland protection showed that the cost of data collection more than paid for itself by enabling a safe and reliable increase in production.

  12. 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.

  13. Optimal Scheduling of a Multi-Carrier Energy Hub Supplemented By Battery Energy Storage Systems

    DEFF Research Database (Denmark)

    Javadi, Mohammad Sadegh; Anvari-Moghaddam, Amjad; Guerrero, Josep M.

    2017-01-01

    This paper introduces a management model for optimal scheduling of a multi-carrier energy hub. In the proposed hub, three types of assets are considered: dispersed generating systems (DGs) such as micro-combined heat and power (mCHP) units, storage devices such as battery-based electrical storage...... systems (ESSs), and heating/cooling devices such as electrical heater, heat-pumps and absorption chillers. The optimal scheduling and management of the examined energy hub assets in line with electrical transactions with distribution network is modeled as a mixed-integer non-linear optimization problem....... In this regard, optimal operating points of DG units as well as ESSs are calculated based on a cost-effective strategy. Degradation cost of ESSs is also taken into consideration for short-term scheduling. Simulation results demonstrate that including well-planned energy storage options together with optimal...

  14. 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.

  15. Optimal control of multi-level quantum systems

    Energy Technology Data Exchange (ETDEWEB)

    Fisher, Robert M.

    2010-12-02

    This thesis is concerned with the control of quantum systems. Given a Hamiltonian model of a quantum system, we are interested in finding controls - typically shaped electromagnetic pulses - that steer the evolution of the system toward a desired target operation. For this we employ a numerical optimisation method known as the GRAPE algorithm. For particular experimental systems, we design control schemes that respect constraints of robustness and addressability, and are within the reach of the experimental hardware. A general procedure is given for specifying a Hamiltonian model of a driven N-level system and converting it to an appropriate rotating frame. This is then applied together with the numerical algorithm to design improved schemes for two different systems, where laser fields manipulate orbital and hyperfine states of Pr{sup 3+} and Rb. The generation of cluster states in Ising-coupled systems is also studied. We find that, in the ideal case, the solution of evolving only under the coupling Hamiltonian is not time-optimal. This surprising result is in contrast to the known cases for unitary gates. For a symmetrised three-qubit example, we provide a geometrical interpretation of this. Numerically optimised control schemes are then developed for a nonideal coupling topology, modelling an experimental configuration of trapped ions. Controls for the implementation of the two-qubit Deutsch and Grover algorithms are designed for a pair of {sup 13}C nuclear spins at a nitrogen vacancy center in diamond. These implementations are robust to experimental errors, and found to be reproduced with high accuracy on a VFG-150 pulse generator. We also consider two-qubit gate synthesis in a system of superconducting qubits coupled by microwave resonators known as the cavity grid. We find that the optimised schemes allow two-qubit operations to be performed between an arbitrary qubit pair on the grid with only a small time overhead, with speedups of 2-4 over the existing

  16. Optimal control of multi-level quantum systems

    International Nuclear Information System (INIS)

    Fisher, Robert M.

    2010-01-01

    This thesis is concerned with the control of quantum systems. Given a Hamiltonian model of a quantum system, we are interested in finding controls - typically shaped electromagnetic pulses - that steer the evolution of the system toward a desired target operation. For this we employ a numerical optimisation method known as the GRAPE algorithm. For particular experimental systems, we design control schemes that respect constraints of robustness and addressability, and are within the reach of the experimental hardware. A general procedure is given for specifying a Hamiltonian model of a driven N-level system and converting it to an appropriate rotating frame. This is then applied together with the numerical algorithm to design improved schemes for two different systems, where laser fields manipulate orbital and hyperfine states of Pr 3+ and Rb. The generation of cluster states in Ising-coupled systems is also studied. We find that, in the ideal case, the solution of evolving only under the coupling Hamiltonian is not time-optimal. This surprising result is in contrast to the known cases for unitary gates. For a symmetrised three-qubit example, we provide a geometrical interpretation of this. Numerically optimised control schemes are then developed for a nonideal coupling topology, modelling an experimental configuration of trapped ions. Controls for the implementation of the two-qubit Deutsch and Grover algorithms are designed for a pair of 13 C nuclear spins at a nitrogen vacancy center in diamond. These implementations are robust to experimental errors, and found to be reproduced with high accuracy on a VFG-150 pulse generator. We also consider two-qubit gate synthesis in a system of superconducting qubits coupled by microwave resonators known as the cavity grid. We find that the optimised schemes allow two-qubit operations to be performed between an arbitrary qubit pair on the grid with only a small time overhead, with speedups of 2-4 over the existing schemes

  17. 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.

  18. 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.

  19. Using Urban Landscape Trajectories to Develop a Multi-Temporal Land Cover Database to Support Ecological Modeling

    Directory of Open Access Journals (Sweden)

    Marina Alberti

    2009-12-01

    Full Text Available Urbanization and the resulting changes in land cover have myriad impacts on ecological systems. Monitoring these changes across large spatial extents and long time spans requires synoptic remotely sensed data with an appropriate temporal sequence. We developed a multi-temporal land cover dataset for a six-county area surrounding the Seattle, Washington State, USA, metropolitan region. Land cover maps for 1986, 1991, 1995, 1999, and 2002 were developed from Landsat TM images through a combination of spectral unmixing, image segmentation, multi-season imagery, and supervised classification approaches to differentiate an initial nine land cover classes. We then used ancillary GIS layers and temporal information to define trajectories of land cover change through multiple updating and backdating rules and refined our land cover classification for each date into 14 classes. We compared the accuracy of the initial approach with the landscape trajectory modifications and determined that the use of landscape trajectory rules increased our ability to differentiate several classes including bare soil (separated into cleared for development, agriculture, and clearcut forest and three intensities of urban. Using the temporal dataset, we found that between 1986 and 2002, urban land cover increased from 8 to 18% of our study area, while lowland deciduous and mixed forests decreased from 21 to 14%, and grass and agriculture decreased from 11 to 8%. The intensity of urban land cover increased with 252 km2 in Heavy Urban in 1986 increasing to 629 km2 by 2002. The ecological systems that are present in this region were likely significantly altered by these changes in land cover. Our results suggest that multi-temporal (i.e., multiple years and multiple seasons within years Landsat data are an economical means to quantify land cover and land cover change across large and highly heterogeneous urbanizing landscapes. Our data, and similar temporal land cover change

  20. Multi-criteria analysis for evaluating the radiological and ecological safety measures in radioactive waste management

    International Nuclear Information System (INIS)

    Sazykina, T.G.; Kryshev, I.I.

    2006-01-01

    A methodological approach is presented for multicriterial evaluating the effectiveness of radiation ecological safety measures during radioactive waste management. The approach is based on multicriterial analysis with consideration of radiological, ecological, social, economical consequences of various safety measures. The application of the multicriterial approach is demonstrated taking as an example of decision-making on the most effective actions for rehabilitation of a water subject, contaminated with radionuclides [ru

  1. Optimal design and routing of power lines; ecological, technical and economic perspectives (OPTIPOL)

    Energy Technology Data Exchange (ETDEWEB)

    Bevanger, K.; Bartzke, G.; Broeseth, H.; Gjershaug, J.O.; Hanssen, F.; Jacobsen, K.-O.; Kvaloey, P.; May, R.; Meaas, R.; Nygaard, T.; Refsnaes, S.; Stokke, S.; Vang, R.

    2010-12-15

    The OPTIPOL project - 'Optimal design and routing of power lines; ecological, technical and economic perspectives' - has been active for two years, although the main operational phase was delayed until autumn 2009. The overall OPTIPOL objective is to develop knowledge and tools to improve the decision on environmental friendly power-line routing. To achieve this goal the work is subdivided into 9 focal areas; Develop a 'least-cost path' GIS-based application for an environmental friendly routing of power lines based on ecological, financial and technological criteria; Assess habitat use of power-line Rights-of-Way (ROW) by different wildlife species, consider actions of improving power-line ROW as wildlife habitats, and evaluate possible positive and negative effects on wildlife of power-line ROWs. More specific we will examine how power-line ROW may offer suitable feeding grounds for moose and see if the species habitat selection is influenced by power line ROW; Assess population impact of bird mortality due to power-line collisions, relative to other human related mortality factors (primarily hunting) in gallinaceous birds (with capercaillie and black grouse as model species); Identify ecological high-risk factors for bird collisions, i.e. site-specific factors connected to topographic characteristics, including vegetation structure, season, weather and light conditions; Establish a national infrastructure for management of dead bird data (including birds re-corded as collision and electrocution victims) by developing an online web application enabling the general public to contribute with data on recorded dead birds via Internet; Review available literature to assess 1) the possibilities for increased collision hazard to birds by making power-line structures less visible for humans given the present knowledge on bird vision, and 2) technical properties and constraints of camouflaging techniques on conductors and earth wires; Review available

  2. Incorporating multi-leaf collimator leaf sequencing into iterative IMRT optimization

    International Nuclear Information System (INIS)

    Siebers, Jeffrey V.; Lauterbach, Marc; Keall, Paul J.; Mohan, Radhe

    2002-01-01

    Intensity modulated radiation therapy (IMRT) treatment planning typically considers beam optimization and beam delivery as separate tasks. Following optimization, a multi-leaf collimator (MLC) or other beam delivery device is used to generate fluence patterns for patient treatment delivery. Due to limitations and characteristics of the MLC, the deliverable intensity distributions often differ from those produced by the optimizer, leading to differences between the delivered and the optimized doses. Objective function parameters are then adjusted empirically, and the plan is reoptimized to achieve a desired deliverable dose distribution. The resulting plan, though usually acceptable, may not be the best achievable. A method has been developed to incorporate the MLC restrictions into the optimization process. Our in-house IMRT system has been modified to include the calculation of the deliverable intensity into the optimizer. In this process, prior to dose calculation, the MLC leaf sequencer is used to convert intensities to dynamic MLC sequences, from which the deliverable intensities are then determined. All other optimization steps remain the same. To evaluate the effectiveness of deliverable-based optimization, 17 patient cases have been studied. Compared with standard optimization plus conversion to deliverable beams, deliverable-based optimization results show improved isodose coverage and a reduced dose to critical structures. Deliverable-based optimization results are close to the original nondeliverable optimization results, suggesting that IMRT can overcome the MLC limitations by adjusting individual beamlets. The use of deliverable-based optimization may reduce the need for empirical adjustment of objective function parameters and reoptimization of a plan to achieve desired results

  3. 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

  4. Integrating traditional ecological knowledge with western science for optimal natural resource management

    Science.gov (United States)

    Serra J. Hoagland

    2017-01-01

    Traditional ecological knowledge (TEK) has been recognized within indigenous communities for millennia; however, traditional ecological knowledge has received growing attention within the western science (WS) paradigm over the past twenty-five years. Federal agencies, national organizations, and university programs dedicated to natural resource management are beginning...

  5. Optimization of exploring well laying place from parameter of regional ecological protection point of view

    International Nuclear Information System (INIS)

    Khairov, G.B.

    1997-01-01

    In this article an influence of petroleum industry and especially discovery of hydrocarbon deposits with high content of hydrogen sulfide to ecological safety and environment is shown. New criteria for zoning and geological estimation are presented. For the first time a parameter of territory ecological protection is offered. (author)

  6. A multi-criteria optimization and decision-making approach for improvement of food engineering processes

    Directory of Open Access Journals (Sweden)

    Alik Abakarov

    2013-04-01

    Full Text Available The objective of this study was to propose a multi-criteria optimization and decision-making technique to solve food engineering problems. This technique was demonstrated using experimental data obtained on osmotic dehydration of carrot cubes in a sodium chloride solution. The Aggregating Functions Approach, the Adaptive Random Search Algorithm, and the Penalty Functions Approach were used in this study to compute the initial set of non-dominated or Pareto-optimal solutions. Multiple non-linear regression analysis was performed on a set of experimental data in order to obtain particular multi-objective functions (responses, namely water loss, solute gain, rehydration ratio, three different colour criteria of rehydrated product, and sensory evaluation (organoleptic quality. Two multi-criteria decision-making approaches, the Analytic Hierarchy Process (AHP and the Tabular Method (TM, were used simultaneously to choose the best alternative among the set of non-dominated solutions. The multi-criteria optimization and decision-making technique proposed in this study can facilitate the assessment of criteria weights, giving rise to a fairer, more consistent, and adequate final compromised solution or food process. This technique can be useful to food scientists in research and education, as well as to engineers involved in the improvement of a variety of food engineering processes.

  7. Multi-infill strategy for kriging models used in variable fidelity optimization

    Directory of Open Access Journals (Sweden)

    Chao SONG

    2018-03-01

    Full Text Available In this paper, a computationally efficient optimization method for aerodynamic design has been developed. The low-fidelity model and the multi-infill strategy are utilized in this approach. Low-fidelity data is employed to provide a good global trend for model prediction, and multiple sample points chosen by different infill criteria in each updating cycle are used to enhance the exploitation and exploration ability of the optimization approach. Take the advantages of low-fidelity model and the multi-infill strategy, and no initial sample for the high-fidelity model is needed. This approach is applied to an airfoil design case and a high-dimensional wing design case. It saves a large number of high-fidelity function evaluations for initial model construction. What’s more, faster reduction of an aerodynamic function is achieved, when compared to ordinary kriging using the multi-infill strategy and variable-fidelity model using single infill criterion. The results indicate that the developed approach has a promising application to efficient aerodynamic design when high-fidelity analyses are involved. Keywords: Aerodynamics, Infill criteria, Kriging models, Multi-infill, Optimization

  8. 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

  9. POBE: A Computer Program for Optimal Design of Multi-Subject Blocked fMRI Experiments

    Directory of Open Access Journals (Sweden)

    Bärbel Maus

    2014-01-01

    Full Text Available For functional magnetic resonance imaging (fMRI studies, researchers can use multi-subject blocked designs to identify active brain regions for a certain stimulus type of interest. Before performing such an experiment, careful planning is necessary to obtain efficient stimulus effect estimators within the available financial resources. The optimal number of subjects and the optimal scanning time for a multi-subject blocked design with fixed experimental costs can be determined using optimal design methods. In this paper, the user-friendly computer program POBE 1.2 (program for optimal design of blocked experiments, version 1.2 is presented. POBE provides a graphical user interface for fMRI researchers to easily and efficiently design their experiments. The computer program POBE calculates the optimal number of subjects and the optimal scanning time for user specified experimental factors and model parameters so that the statistical efficiency is maximised for a given study budget. POBE can also be used to determine the minimum budget for a given power. Furthermore, a maximin design can be determined as efficient design for a possible range of values for the unknown model parameters. In this paper, the computer program is described and illustrated with typical experimental factors for a blocked fMRI experiment.

  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. 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.

  12. A multi-objective optimization problem for multi-state series-parallel systems: A two-stage flow-shop manufacturing system

    International Nuclear Information System (INIS)

    Azadeh, A.; Maleki Shoja, B.; Ghanei, S.; Sheikhalishahi, M.

    2015-01-01

    This research investigates a redundancy-scheduling optimization problem for a multi-state series parallel system. The system is a flow shop manufacturing system with multi-state machines. Each manufacturing machine may have different performance rates including perfect performance, decreased performance and complete failure. Moreover, warm standby redundancy is considered for the redundancy allocation problem. Three objectives are considered for the problem: (1) minimizing system purchasing cost, (2) minimizing makespan, and (3) maximizing system reliability. Universal generating function is employed to evaluate system performance and overall reliability of the system. Since the problem is in the NP-hard class of combinatorial problems, genetic algorithm (GA) is used to find optimal/near optimal solutions. Different test problems are generated to evaluate the effectiveness and efficiency of proposed approach and compared to simulated annealing optimization method. The results show the proposed approach is capable of finding optimal/near optimal solution within a very reasonable time. - Highlights: • A redundancy-scheduling optimization problem for a multi-state series parallel system. • A flow shop with multi-state machines and warm standby redundancy. • Objectives are to optimize system purchasing cost, makespan and reliability. • Different test problems are generated and evaluated by a unique genetic algorithm. • It locates optimal/near optimal solution within a very reasonable time

  13. 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.

  14. Assessment of ecological passages along road networks within the Mediterranean forest using GIS-based multi criteria evaluation approach.

    Science.gov (United States)

    Gülci, Sercan; Akay, Abdullah Emin

    2015-12-01

    Major roads cause barrier effect and fragmentation on wildlife habitats that are suitable places for feeding, mating, socializing, and hiding. Due to wildlife collisions (Wc), human-wildlife conflicts result in lost lives and loss of biodiversity. Geographical information system (GIS)-based multi criteria evaluation (MCE) methods have been successfully used in short-term planning of road networks considering wild animals. Recently, wildlife passages have been effectively utilized as road engineering structures provide quick and certain solutions for traffic safety and wildlife conservation problems. GIS-based MCE methods provide decision makers with optimum location for ecological passages based on habitat suitability models (HSMs) that classify the areas based on ecological requirements of target species. In this study, ecological passages along Motorway 52 within forested areas in Mediterranean city of Osmaniye in Turkey were evaluated. Firstly, HSM coupled with nine eco-geographic decision variables were developed based on ecological requirements of roe deer (Capreolus capreolus) that were chosen as target species. Then specified decision variables were evaluated using GIS-based weighted linear combination (WLC) method to estimate movement corridors and mitigation points along the motorway. In the solution process, two linkage nodes were evaluated for eco-passages which were determined based on the least-cost movement corridor intersecting with the motorway. One of the passages was identified as a natural wildlife overpass while the other was suggested as underpass construction. The results indicated that computer-based models provide accurate and quick solutions for positioning ecological passages to reduce environmental effects of road networks on wild animals.

  15. 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.

  16. 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

  17. 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.

  18. Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

    Science.gov (United States)

    Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

    2018-02-06

    This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

  19. 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.

  20. Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure

    Directory of Open Access Journals (Sweden)

    Shan Pang

    2016-01-01

    Full Text Available A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM was proposed. OM-ELM employs quantum-behaved particle swarm optimization to automatically obtain the optimal network structure according to both the root mean square error on training data set and the norm of output weights. The proposed method is applied to handwritten recognition data set and a gas turbine engine diagnostic application and is compared with basic ELM, multi-hidden-layer ELM, and two state-of-the-art deep learning algorithms: deep belief network and the stacked denoising autoencoder. Results show that, with optimized network structure, OM-ELM obtains better test accuracy in both applications and is more robust to sensor noise. Meanwhile it controls the model complexity and needs far less hidden nodes than multi-hidden-layer ELM, thus saving computer memory and making it more efficient to implement. All these advantages make our method an effective and reliable tool for engine component fault diagnosis tool.

  1. 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...

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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

  7. 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.)

  8. Coupling between a multi-physics workflow engine and an optimization framework

    Science.gov (United States)

    Di Gallo, L.; Reux, C.; Imbeaux, F.; Artaud, J.-F.; Owsiak, M.; Saoutic, B.; Aiello, G.; Bernardi, P.; Ciraolo, G.; Bucalossi, J.; Duchateau, J.-L.; Fausser, C.; Galassi, D.; Hertout, P.; Jaboulay, J.-C.; Li-Puma, A.; Zani, L.

    2016-03-01

    A generic coupling method between a multi-physics workflow engine and an optimization framework is presented in this paper. The coupling architecture has been developed in order to preserve the integrity of the two frameworks. The objective is to provide the possibility to replace a framework, a workflow or an optimizer by another one without changing the whole coupling procedure or modifying the main content in each framework. The coupling is achieved by using a socket-based communication library for exchanging data between the two frameworks. Among a number of algorithms provided by optimization frameworks, Genetic Algorithms (GAs) have demonstrated their efficiency on single and multiple criteria optimization. Additionally to their robustness, GAs can handle non-valid data which may appear during the optimization. Consequently GAs work on most general cases. A parallelized framework has been developed to reduce the time spent for optimizations and evaluation of large samples. A test has shown a good scaling efficiency of this parallelized framework. This coupling method has been applied to the case of SYCOMORE (SYstem COde for MOdeling tokamak REactor) which is a system code developed in form of a modular workflow for designing magnetic fusion reactors. The coupling of SYCOMORE with the optimization platform URANIE enables design optimization along various figures of merit and constraints.

  9. 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.

  10. 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.

  11. 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.

  12. Optimal control of cooperative multi-vehicle systems; Optimalsteuerung kooperierender Mehrfahrzeugsysteme

    Energy Technology Data Exchange (ETDEWEB)

    Reinl, Christian; Stryk, Oskar von [Technische Univ. Darmstadt (Germany). FB Informatik; Glocker, Markus [Trimble Terrasat GmbH, Hoehenkirchen (Germany)

    2009-07-01

    Nonlinear hybrid dynamical systems for modeling optimal cooperative control enable a tight and formal coupling of discrete and continuous state dynamics, i.e. of dynamic role and task assignment with switching dynamics of motions. In the resulting mixed-integer multi-phase optimal control problems constraints on the discrete and continuous state and control variables can be considered, e.g., formation or communication requirements. Two numerical methods are investigated: a decomposition approach using branch-and-bound and direct collocation methods as well as an approximation by large-scale, mixed-integer linear problems. Both methods are applied to example problems: the optimal simultaneous waypoint sequencing and trajectory planning of a team of aerial vehicles and the optimization of role distribution and trajectories in robot soccer. (orig.)

  13. 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.

  14. Particle swarm optimization using multi-information characteristics of all personal-best information.

    Science.gov (United States)

    Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

    2016-01-01

    Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition term with three positions of all personal-best information is used in velocity update equation to enhance the search capability. This strategy could make particles fly to a better direction by discovering useful information from all the personal-best positions. The validity of the proposed algorithm is assessed on twenty benchmark problems including unimodal, multimodal, rotated and shifted functions, and the results are compared with that obtained by some published variants of particle swarm optimization in the literature. Computational results demonstrate that the proposed algorithm finds several global optimum and high-quality solutions in most case with a fast convergence speed.

  15. 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.

  16. Optimizing an Actuator Array for the Control of Multi-Frequency Noise in Aircraft Interiors

    Science.gov (United States)

    Palumbo, D. L.; Padula, S. L.

    1997-01-01

    Techniques developed for selecting an optimized actuator array for interior noise reduction at a single frequency are extended to the multi-frequency case. Transfer functions for 64 actuators were obtained at 5 frequencies from ground testing the rear section of a fully trimmed DC-9 fuselage. A single loudspeaker facing the left side of the aircraft was the primary source. A combinatorial search procedure (tabu search) was employed to find optimum actuator subsets of from 2 to 16 actuators. Noise reduction predictions derived from the transfer functions were used as a basis for evaluating actuator subsets during optimization. Results indicate that it is necessary to constrain actuator forces during optimization. Unconstrained optimizations selected actuators which require unrealistically large forces. Two methods of constraint are evaluated. It is shown that a fast, but approximate, method yields results equivalent to an accurate, but computationally expensive, method.

  17. Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration.

    Science.gov (United States)

    Young Kim, Eun; Johnson, Hans J

    2013-01-01

    A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness improvements from incorporation of following four elements: (1) utilize multi-modal and repeated scans, (2) incorporate high-deformable registration, (3) use extended set of tissue definitions, and (4) use of multi-modal aware intensity-context priors. The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (BrainWeb) and by applying to highly-heterogeneous data from a 32 site imaging study with quality assessments through the expert visual inspection. The implementation of this tool is tailored for, but not limited to, large-scale data processing with great data variation with a flexible interface. In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal MR scans collected at multi-sites. The tool was evaluated by using both simulated and simulated and human subject MRI images. With these enhancements, the results showed improved robustness for large-scale heterogeneous MRI processing.

  18. DC microgrid power flow optimization by multi-layer supervision control. Design and experimental validation

    International Nuclear Information System (INIS)

    Sechilariu, Manuela; Wang, Bao Chao; Locment, Fabrice; Jouglet, Antoine

    2014-01-01

    Highlights: • DC microgrid (PV array, storage, power grid connection, DC load) with multi-layer supervision control. • Power balancing following power flow optimization while providing interface for smart grid communication. • Optimization under constraints: storage capability, grid power limitations, grid time-of-use pricing. • Experimental validation of DC microgrid power flow optimization by multi-layer supervision control. • DC microgrid able to perform peak shaving, to avoid undesired injection, and to make full use of locally energy. - Abstract: Urban areas have great potential for photovoltaic (PV) generation, however, direct PV power injection has limitations for high level PV penetration. It induces additional regulations in grid power balancing because of lacking abilities of responding to grid issues such as reducing grid peak consumption or avoiding undesired injections. The smart grid implementation, which is designed to meet these requirements, is facilitated by microgrids development. This paper presents a DC microgrid (PV array, storage, power grid connection, DC load) with multi-layer supervision control which handles instantaneous power balancing following the power flow optimization while providing interface for smart grid communication. The optimization takes into account forecast of PV power production and load power demand, while satisfying constraints such as storage capability, grid power limitations, grid time-of-use pricing and grid peak hour. Optimization, whose efficiency is related to the prediction accuracy, is carried out by mixed integer linear programming. Experimental results show that the proposed microgrid structure is able to control the power flow at near optimum cost and ensures self-correcting capability. It can respond to issues of performing peak shaving, avoiding undesired injection, and making full use of locally produced energy with respect to rigid element constraints

  19. 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.

  20. NLP model and stochastic multi-start optimization approach for heat exchanger networks

    International Nuclear Information System (INIS)

    Núñez-Serna, Rosa I.; Zamora, Juan M.

    2016-01-01

    Highlights: • An NLP model for the optimal design of heat exchanger networks is proposed. • The NLP model is developed from a stage-wise grid diagram representation. • A two-phase stochastic multi-start optimization methodology is utilized. • Improved network designs are obtained with different heat load distributions. • Structural changes and reductions in the number of heat exchangers are produced. - Abstract: Heat exchanger network synthesis methodologies frequently identify good network structures, which nevertheless, might be accompanied by suboptimal values of design variables. The objective of this work is to develop a nonlinear programming (NLP) model and an optimization approach that aim at identifying the best values for intermediate temperatures, sub-stream flow rate fractions, heat loads and areas for a given heat exchanger network topology. The NLP model that minimizes the total annual cost of the network is constructed based on a stage-wise grid diagram representation. To improve the possibilities of obtaining global optimal designs, a two-phase stochastic multi-start optimization algorithm is utilized for the solution of the developed model. The effectiveness of the proposed optimization approach is illustrated with the optimization of two network designs proposed in the literature for two well-known benchmark problems. Results show that from the addressed base network topologies it is possible to achieve improved network designs, with redistributions in exchanger heat loads that lead to reductions in total annual costs. The results also show that the optimization of a given network design sometimes leads to structural simplifications and reductions in the total number of heat exchangers of the network, thereby exposing alternative viable network topologies initially not anticipated.

  1. Evolutionary optimization and game strategies for advanced multi-disciplinary design applications to aeronautics and UAV design

    CERN Document Server

    Periaux, Jacques; Lee, Dong Seop Chris

    2015-01-01

    Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies. This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization and hierarchical topology which allows different models including precise, intermediate and approximate models with each node belonging to the different hierarchical layer handled by a different Evolutionary Algorithm. The efficiency of evolutionary algorithms for both single and multi-objective optimization problems are significantly improved by the coupling of EAs with games and in particular by a new dynamic methodology named “Hybridized Nash-Pareto games”. Multi objective Optimization techniques and robust design problems taking into account uncertainties are introduced and explained in detail. Several applications dealing with c...

  2. Dealing with incomplete and variable detectability in multi-year, multi-site monitoring of ecological populations

    Science.gov (United States)

    Converse, Sarah J.; Royle, J. Andrew; Gitzen, Robert A.; Millspaugh, Joshua J.; Cooper, Andrew B.; Licht, Daniel S.

    2012-01-01

    An ecological monitoring program should be viewed as a component of a larger framework designed to advance science and/or management, rather than as a stand-alone activity. Monitoring targets (the ecological variables of interest; e.g. abundance or occurrence of a species) should be set based on the needs of that framework (Nichols and Williams 2006; e.g. Chapters 2–4). Once such monitoring targets are set, the subsequent step in monitoring design involves consideration of the field and analytical methods that will be used to measure monitoring targets with adequate accuracy and precision. Long-term monitoring programs will involve replication of measurements over time, and possibly over space; that is, one location or each of multiple locations will be monitored multiple times, producing a collection of site visits (replicates). Clearly this replication is important for addressing spatial and temporal variability in the ecological resources of interest (Chapters 7–10), but it is worth considering how this replication can further be exploited to increase the effectiveness of monitoring. In particular, defensible monitoring of the majority of animal, and to a lesser degree plant, populations and communities will generally require investigators to account for imperfect detection (Chapters 4, 18). Raw indices of population state variables, such as abundance or occupancy (sensu MacKenzie et al. 2002), are rarely defensible when detection probabilities are failing to correct for differences in detection, resulting in indices that have an unknown relationship to the parameters of interest (e.g. Nichols 1992, Anderson 2001, MacKenzie et al. 2002, Williams et al. 2002, Anderson 2003, White 2005, Kéry and Schmidt 2008). While others have argued that indices may be preferable in some cases due to the challenges associated with estimating detection probabilities (e.g. McKelvey and Pearson 2001, Johnson 2008), we do not attempt to resolve this debate here. Rather, we are

  3. Dealing with incomplete and variable detectability in multi-year, multi-site monitoring of ecological populations

    Science.gov (United States)

    Converse, Sarah J.; Royle, J. Andrew; Gitzen, Robert A.; Millspaugh, Joshua J.; Cooper, Andrew B.; Licht, Daniel S.

    2012-01-01

    An ecological monitoring program should be viewed as a component of a larger framework designed to advance science and/or management, rather than as a stand-alone activity. Monitoring targets (the ecological variables of interest; e.g. abundance or occurrence of a species) should be set based on the needs of that framework (Nichols and Williams 2006; e.g. Chapters 2–4). Once such monitoring targets are set, the subsequent step in monitoring design involves consideration of the field and analytical methods that will be used to measure monitoring targets with adequate accuracy and precision. Long-term monitoring programs will involve replication of measurements over time, and possibly over space; that is, one location or each of multiple locations will be monitored multiple times, producing a collection of site visits (replicates). Clearly this replication is important for addressing spatial and temporal variability in the ecological resources of interest (Chapters 7–10), but it is worth considering how this replication can further be exploited to increase the effectiveness of monitoring. In particular, defensible monitoring of the majority of animal, and to a lesser degree plant, populations and communities will generally require investigators to account for imperfect detection (Chapters 4, 18). Raw indices of population state variables, such as abundance or occupancy (sensu MacKenzie et al. 2002), are rarely defensible when detection probabilities are challenges associated with estimating detection probabilities (e.g. McKelvey and Pearson 2001, Johnson 2008), we do not attempt to resolve this debate here. Rather, we are more apt to agree with MacKenzie and Kendall (2002) that the burden of proof ought to be on the assertion that detection probabilities are constant. Furthermore, given the wide variety of field methods available for estimating detection probabilities and the inability for an investigator to know, a priori, if detection probabilities will be

  4. Optimal design and routing of power lines; ecological, technical and economic perspectives (OPTIPOL). Progress report 2009

    Energy Technology Data Exchange (ETDEWEB)

    Bevanger, K.; Bartzke, G.; Broeseth, H.; Gjershaug, J.O.; Hanssen, F.; Jacobsen, K.-O.; Kvaloey, P.; May, R.; Nygaard, T.; Pedersen, H.C.; Reitan, O.; Refsnaes, S.; Stokke, S.; Vang, R.

    2009-12-15

    From 2009 inclusive, NINA has received economic support for research on power lines and wildlife from the Norwegian Research Council (NFR) through the RENERGI Programme. The project is named 'Optimal design and routing of power lines; ecological, technical and economic perspectives' (OPTIPOL). It is scheduled for 5 years (2009-1013) and is part of the activities within CEDREN, i.e. the Centre for environmental design of renewable energy (cf. http://www.cedren.no). With a grid close to 200 000 km overhead power-lines, the associated rights-of-way (ROW) affect huge land areas in Norway. The overall goal is to develop predict-ing tools for optimal routing of power lines from an environmental perspective and assess technical and economic solutions to minimize conflicts with wildlife and habitat conservation. Thus, the OPTIPOL rationale is based on the belief that the negative effects of electricity transmission and distribution can be reduced with respect to birds and mammals. OPTIPOL has several ambitious objectives, and is divided into sub-projects and specific tasks. From the first of November a PhD-student became part of the project, a position that will be held for 4 years. The main objective of the PhD-activities will be to assess how and why different wildlife species use deforested areas below power lines, evaluate possible positive and negative effects of power-line ROWs, and assess the possibilities for quality improvement. Another part of the project is dedicated the effects of linear structures on movement patterns and distribution in the landscape in native deer species. Here we will examine how different spatial scales influence the processes that guide movement patterns, and responses to linear structures. Another focus will be small game species, with mountain hare, capercaillie, black grouse and hazel grouse as model species. The main objective will be to assess the impact of transforming ROW habitats into attractive small-game foraging

  5. 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)

  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. 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.

  8. Optimization of series-parallel multi-state systems under maintenance policies

    International Nuclear Information System (INIS)

    Nourelfath, Mustapha; Ait-Kadi, Daoud

    2007-01-01

    In the redundancy optimization problem, the design goal is achieved by discrete choices made from components available in the market. In this paper, the problem is to find, under reliability constraints, the minimal cost configuration of a multi-state series-parallel system, which is subject to a specified maintenance policy. The number of maintenance teams is less than the number of repairable components, and a maintenance policy specifies the priorities between the system components. To take into account the dependencies resulting from the sharing of maintenance teams, the universal generating function approach is coupled with a Markov model. The resulting optimization approach has the advantage of being mainly analytical

  9. An Optimal Beamforming Algorithm for Phased-Array Antennas Used in Multi-Beam Spaceborne Radiometers

    DEFF Research Database (Denmark)

    Iupikov, O. A.; Ivashina, M. V.; Pontoppidan, K.

    2015-01-01

    Strict requirements for future spaceborne ocean missions using multi-beam radiometers call for new antenna technologies, such as digital beamforming phased arrays. In this paper, we present an optimal beamforming algorithm for phased-array antenna systems designed to operate as focal plane arrays...... to a FPA feeding a torus reflector antenna (designed under the contract with the European Space Agency) and tested for multiple beams. The results demonstrate an improved performance in terms of the optimized beam characteristics, yielding much higher spatial and radiometric resolution as well as much...

  10. 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...

  11. 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...

  12. Ecological optimization of quantum spin-1/2 heat engine at the classical limit

    International Nuclear Information System (INIS)

    Chen, T-H

    2006-01-01

    The purpose of this paper is to present a study of finite-time thermodynamics applied to evaluate the ecological performance of a quantum heat engine which operates between two thermal reservoirs using the working substance of spin-1/2 systems. The quantum heat engine cycle is composed of two isothermal processes, an adiabatic process and an isomagnetic field process. A sequence of time evolution was determined from the quantum angular momentum rate based on the semigroup approach and the quantum master equation. The individual time duration is added to account for the total cycle time. The objective ecological function representing a compromise between power output and irreversibility is maximized with respect to cycle temperature ratio. Effects of thermal reservoir temperature ratio and magnetic field ratio on the ecological function have been discussed. A comparison of quantum heat engine performance under maximum ecological function and maximum power conditions is also presented

  13. 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.

  14. Dynamic Programming Optimization of Multi-rate Multicast Video-Streaming Services

    Directory of Open Access Journals (Sweden)

    Nestor Michael Caños Tiglao

    2010-06-01

    Full Text Available In large scale IP Television (IPTV and Mobile TV distributions, the video signal is typically encoded and transmitted using several quality streams, over IP Multicast channels, to several groups of receivers, which are classified in terms of their reception rate. As the number of video streams is usually constrained by both the number of TV channels and the maximum capacity of the content distribution network, it is necessary to find the selection of video stream transmission rates that maximizes the overall user satisfaction. In order to efficiently solve this problem, this paper proposes the Dynamic Programming Multi-rate Optimization (DPMO algorithm. The latter was comparatively evaluated considering several user distributions, featuring different access rate patterns. The experimental results reveal that DPMO is significantly more efficient than exhaustive search, while presenting slightly higher execution times than the non-optimal Multi-rate Step Search (MSS algorithm.

  15. Using a multi-state recurrent neural network to optimize loading patterns in BWRs

    International Nuclear Information System (INIS)

    Ortiz, Juan Jose; Requena, Ignacio

    2004-01-01

    A Multi-State Recurrent Neural Network is used to optimize Loading Patterns (LP) in BWRs. We have proposed an energy function that depends on fuel assembly positions and their nuclear cross sections to carry out optimisation. Multi-State Recurrent Neural Networks creates LPs that satisfy the Radial Power Peaking Factor and maximize the effective multiplication factor at the Beginning of the Cycle, and also satisfy the Minimum Critical Power Ratio and Maximum Linear Heat Generation Rate at the End of the Cycle, thereby maximizing the effective multiplication factor. In order to evaluate the LPs, we have used a trained back-propagation neural network to predict the parameter values, instead of using a reactor core simulator, which saved considerable computation time in the search process. We applied this method to find optimal LPs for five cycles of Laguna Verde Nuclear Power Plant (LVNPP) in Mexico

  16. Optimal replacement policy for safety-related multi-component multi-state systems

    International Nuclear Information System (INIS)

    Xu Ming; Chen Tao; Yang Xianhui

    2012-01-01

    This paper investigates replacement scheduling for non-repairable safety-related systems (SRS) with multiple components and states. The aim is to determine the cost-minimizing time for replacing SRS while meeting the required safety. Traditionally, such scheduling decisions are made without considering the interaction between the SRS and the production system under protection, the interaction being essential to formulate the expected cost to be minimized. In this paper, the SRS is represented by a non-homogeneous continuous time Markov model, and its state distribution is evaluated with the aid of the universal generating function. Moreover, a structure function of SRS with recursive property is developed to evaluate the state distribution efficiently. These methods form the basis to derive an explicit expression of the expected system cost per unit time, and to determine the optimal time to replace the SRS. The proposed methodology is demonstrated through an illustrative example.

  17. Optimizing Multi-Product Multi-Constraint Inventory Control Systems with Stochastic Replenishments

    Science.gov (United States)

    Allah Taleizadeh, Ata; Aryanezhad, Mir-Bahador; Niaki, Seyed Taghi Akhavan

    Multi-periodic inventory control problems are mainly studied employing two assumptions. The first is the continuous review, where depending on the inventory level orders can happen at any time and the other is the periodic review, where orders can only happen at the beginning of each period. In this study, we relax these assumptions and assume that the periodic replenishments are stochastic in nature. Furthermore, we assume that the periods between two replenishments are independent and identically random variables. For the problem at hand, the decision variables are of integer-type and there are two kinds of space and service level constraints for each product. We develop a model of the problem in which a combination of back-order and lost-sales are considered for the shortages. Then, we show that the model is of an integer-nonlinear-programming type and in order to solve it, a search algorithm can be utilized. We employ a simulated annealing approach and provide a numerical example to demonstrate the applicability of the proposed methodology.

  18. 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...

  19. Two-phase framework for optimal multi-target Lambert rendezvous

    OpenAIRE

    Bang, Jun; Ahn, Jaemyung

    2017-01-01

    This paper proposes a two-phase framework to solve an optimal multi-target Lambert rendezvous problem. The first phase solves a series of single-target rendezvous problems for all departure-arrival object pairs to generate the elementary solutions, which provides candidate rendezvous trajectories (elementary solutions). The second phase formulates a variant of traveling salesman problem (TSP) using the elementary solutions prepared in the first phase and determines the best rendezvous sequenc...

  20. Multi-stage Optimization of Matchings in Trees with Application to Kidney Exchange

    KAUST Repository

    Mankowski, Michal

    2017-07-22

    In this paper, we propose a method for multi-stage optimization of matchings in trees relative to different weight functions that assign positive weights to the edges of the trees. This method can be useful in transplantology where nodes of the tree correspond to pairs (donor, recipient) and two nodes (pairs) are connected by an edge if these pairs can exchange kidneys. Weight functions can characterize the number of exchanges, the importance of exchanges, or their compatibility.

  1. 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.

  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 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...

  4. 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

  5. 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....

  6. 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

  7. An adaptive multi-spline refinement algorithm in simulation based sailboat trajectory optimization using onboard multi-core computer systems

    Directory of Open Access Journals (Sweden)

    Dębski Roman

    2016-06-01

    Full Text Available A new dynamic programming based parallel algorithm adapted to on-board heterogeneous computers for simulation based trajectory optimization is studied in the context of “high-performance sailing”. The algorithm uses a new discrete space of continuously differentiable functions called the multi-splines as its search space representation. A basic version of the algorithm is presented in detail (pseudo-code, time and space complexity, search space auto-adaptation properties. Possible extensions of the basic algorithm are also described. The presented experimental results show that contemporary heterogeneous on-board computers can be effectively used for solving simulation based trajectory optimization problems. These computers can be considered micro high performance computing (HPC platforms-they offer high performance while remaining energy and cost efficient. The simulation based approach can potentially give highly accurate results since the mathematical model that the simulator is built upon may be as complex as required. The approach described is applicable to many trajectory optimization problems due to its black-box represented performance measure and use of OpenCL.

  8. Multi-dimensional optimization of a terawatt seeded tapered Free Electron Laser with a Multi-Objective Genetic Algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Juhao, E-mail: jhwu@SLAC.Stanford.EDU [SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States); Hu, Newman [Valley Christian High School, 100 Skyway Drive, San Jose, CA 95111 (United States); Setiawan, Hananiel [The Facility for Rare Isotope Beams, Michigan State University, East Lansing, MI 48824 (United States); Huang, Xiaobiao; Raubenheimer, Tor O. [SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States); Jiao, Yi [Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049 (China); Yu, George [Columbia University, New York, NY 10027 (United States); Mandlekar, Ajay [California Institute of Technology, Pasadena, CA 91125 (United States); Spampinati, Simone [Sincrotrone Trieste S.C.p.A. di interesse nazionale, Strada Statale 14-km 163,5 in AREA Science Park, 34149 Basovizza, Trieste (Italy); Fang, Kun [SLAC National Accelerator Laboratory, Menlo Park, CA 94025 (United States); Chu, Chungming [The Facility for Rare Isotope Beams, Michigan State University, East Lansing, MI 48824 (United States); Qiang, Ji [Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720 (United States)

    2017-02-21

    There is a great interest in generating high-power hard X-ray Free Electron Laser (FEL) in the terawatt (TW) level that can enable coherent diffraction imaging of complex molecules like proteins and probe fundamental high-field physics. A feasibility study of producing such X-ray pulses was carried out employing a configuration beginning with a Self-Amplified Spontaneous Emission FEL, followed by a “self-seeding” crystal monochromator generating a fully coherent seed, and finishing with a long tapered undulator where the coherent seed recombines with the electron bunch and is amplified to high power. The undulator tapering profile, the phase advance in the undulator break sections, the quadrupole focusing strength, etc. are parameters to be optimized. A Genetic Algorithm (GA) is adopted for this multi-dimensional optimization. Concrete examples are given for LINAC Coherent Light Source (LCLS) and LCLS-II-type systems. Analytical estimate is also developed to cross check the simulation and optimization results as a quick and complimentary tool.

  9. Investigations into the Optimization of Multi-Source Strength Brachytherapy Treatment Procedures

    CERN Document Server

    Henderson, D L; Yoo, S

    2002-01-01

    The goal of this project is to investigate the use of multi-strength and multi-specie radioactive sources in permanent prostate implant brachytherapy. In order to fulfill the requirement for an optimal dose distribution, the prescribed dose should be delivered to the target in a nearly uniform dose distribution while simultaneously sparing sensitive structures. The treatment plan should use a small number of needles and sources while satisfying the treatment requirements. The hypothesis for the use of multi-strength and/or multi-specie sources is that a better treatment plan using fewer sources and needles could be obtained than by treatment plans using single-strength sources could reduce the overall number of sources used for treatment. We employ a recently developed greedy algorithm based on the adjoint concept as the optimization search engine. The algorithm utilizes and ''adjoint ratio'', which provides a means of ranking source positions, as the pseudo-objective function. It ha s been shown that the gre...

  10. 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.

  11. A genetic algorithm for optimizing multi-pole Debye models of tissue dielectric properties

    International Nuclear Information System (INIS)

    Clegg, J; Robinson, M P

    2012-01-01

    Models of tissue dielectric properties (permittivity and conductivity) enable the interactions of tissues and electromagnetic fields to be simulated, which has many useful applications in microwave imaging, radio propagation, and non-ionizing radiation dosimetry. Parametric formulae are available, based on a multi-pole model of tissue dispersions, but although they give the dielectric properties over a wide frequency range, they do not convert easily to the time domain. An alternative is the multi-pole Debye model which works well in both time and frequency domains. Genetic algorithms are an evolutionary approach to optimization, and we found that this technique was effective at finding the best values of the multi-Debye parameters. Our genetic algorithm optimized these parameters to fit to either a Cole–Cole model or to measured data, and worked well over wide or narrow frequency ranges. Over 10 Hz–10 GHz the best fits for muscle, fat or bone were each found for ten dispersions or poles in the multi-Debye model. The genetic algorithm is a fast and effective method of developing tissue models that compares favourably with alternatives such as the rational polynomial fit. (paper)

  12. Interval Optimization Model Considering Terrestrial Ecological Impacts for Water Rights Transfer from Agriculture to Industry in Ningxia, China.

    Science.gov (United States)

    Sun, Lian; Li, Chunhui; Cai, Yanpeng; Wang, Xuan

    2017-06-14

    In this study, an interval optimization model is developed to maximize the benefits of a water rights transfer system that comprises industry and agriculture sectors in the Ningxia Hui Autonomous Region in China. The model is subjected to a number of constraints including water saving potential from agriculture and ecological groundwater levels. Ecological groundwater levels serve as performance indicators of terrestrial ecology. The interval method is applied to present the uncertainty of parameters in the model. Two scenarios regarding dual industrial development targets (planned and unplanned ones) are used to investigate the difference in potential benefits of water rights transfer. Runoff of the Yellow River as the source of water rights fluctuates significantly in different years. Thus, compensation fees for agriculture are calculated to reflect the influence of differences in the runoff. Results show that there are more available water rights to transfer for industrial development. The benefits are considerable but unbalanced between buyers and sellers. The government should establish a water market that is freer and promote the interest of agriculture and farmers. Though there has been some success of water rights transfer, the ecological impacts and the relationship between sellers and buyers require additional studies.

  13. A multi-scalar approach to theorizing socio-ecological dynamics of urban residential landscapes

    Science.gov (United States)

    Rinku Roy Chowdhury; Kelli Larson; Morgan Grove; Colin Polsky; Elizabeth Cook; Jeffrey Onsted; Laura. Ogden

    2011-01-01

    Urban residential expansion increasingly drives land use, land cover and ecological changes worldwide, yet social science theories explaining such change remain under-developed. Existing theories often focus on processes occurring at one scale, while ignoring other scales. Emerging evidence from four linked U.S. research sites suggests it is essential to examine...

  14. A Multi-Criteria Index for Ecological Evaluation of Tropical Agriculture in Southeastern Mexico

    NARCIS (Netherlands)

    Huerta, E.; Kampichler, C.; Ochoa-Gaona, S.; Jong, de B.; Hernandez-Daumas, S.; Geissen, V.

    2014-01-01

    The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity

  15. 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.

  16. Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop.

    Science.gov (United States)

    Li, Lian-Hui; Mo, Rong

    2015-01-01

    The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility.

  17. Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop.

    Directory of Open Access Journals (Sweden)

    Lian-Hui Li

    Full Text Available The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility.

  18. 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.

  19. 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.

  20. 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

  1. Design optimization of multi-layer Silicon Carbide cladding for light water reactors

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Youho, E-mail: euo@unm.edu [Department of Nuclear Engineering, University of New Mexico, MSC01 1120 1 University of New Mexico, Albuquerque, NM 87131 (United States); NO, Hee Cheon, E-mail: hcno@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); Lee, Jeong Ik, E-mail: jeongiklee@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of)

    2017-01-15

    Highlights: • SiC cladding designs are optimized with a multi-layer structural analysis code. • Layer radial thickness fraction that minimizes cladding fracture probability exists. • The demonstrated procedure is applicable for multi-layer SiC cladding design. • Duplex SiC with the inner composite fraction ∼0.4 is optimal in a reference case. • Increasing composite thermal conductivity markedly decreases SiC cladding stress. - Abstract: A parametric study that demonstrates a methodology for determining the optimum bilayer composition in a duplex SiC cladding is discussed. The structural performance of multi-layer SiC cladding design is significantly affected by radial thickness fraction of each layer. This study shows that there exists an optimal composite/monolith radial thickness fraction that minimizes failure probability for a duplex SiC cladding in steady-state operation. An exemplary reference case study shows that the duplex cladding with the inner composite fraction ∼0.4 and the outer CVD-SiC fraction ∼0.6 is found to be the optimal SiC cladding design for the current PWRs with the reference material choice for CVD-SiC and fiber reinforced composite. A marginal increase in the composite fraction from the presented optimal designs may lead to increase structural integrity by introducing some unquantified merits such as increasing damage tolerance. The major factors that affect the optimum cladding designs are temperature gradients and internal gas pressure. Clad wall thickness, thermal conductivity, and Weibull modulus are among the key design parameters/material properties.

  2. Optimal design and routing of power lines; ecological, technical and economic perspectives (OPTIPOL). Progress report 2012

    Energy Technology Data Exchange (ETDEWEB)

    Bevanger, Kjetil; Bartzke, Gundula; Broeseth, Henrik; Dahl, Espen Lie; Gjershaug, Jan Ove; Hanssen, Frank; Jacobsen, Karl-Otto; Kleven, Oddmund; Kvaloey, Paal; May, Roel; Meaas, Roger; Nygaaard, Torgeir; Resnaes, Steinar; Stokke, Sigbjoern; Thomassen, Joern

    2012-07-01

    birds in the database, compared to only 117 a year earlier. WP5 - 'A Least Cost Path (LCP) toolbox for optimal route routing of power lines', has developed an LCP-pilot to demonstrate the LCP method, based on the impact studies were undertaken prior to construction of a 420 kV transmission line in Central Norway 2005. Relevant economic, ecological and technological environment criteria based on suggestions from interested users (NGOs, government, industry, etc.), was used. LCP-pilot and a fuzzy-logic approach of this was demonstrated in the first dialogue-based workshop, 23.-24. april 2012. The seminar, which had an emphasis on criteria definitions were followed up with a working seminar that focused criterion values ??on 20 november 2012. Lecture - 'A Least Cost Path (LCP) Toolbox for Optimal Routing of Power Lines, -was presented and submitted as contributions to the conference report from 'The 10th ROW Conference' in Arizona, 'The 32nd Annual Conference of the International Association for Impact Assessment (IAIA12) ' in Porto, Portugal, and 'The ESRI European User Conference' in Oslo. WP6 - 'Birds and camouflaging of power lines', has almost completed the final report, 'Power line camouflaging. An assessment of the ecological and technical challenges'. 'Because of the budget situation in CEDREN However, completion of the report postponed until the end of April 2013. WP7 - 'Effect of line marking / modifications remedial measures against bird mortality' has almost completed the final report 'Opportunities and limitations in terms of reducing mortality in birds due to collision and electrocution.' Due to overall budget situation in CEDREN the report deferred to the end of april 2013. WP8, 'guidelines for technological solutions that may reduce mortality in birds because of the power line's', has focused topics relating to the labeling, design, insulation, camouflage and wiring. The results, which are presented in the notes and articles, will be implemented in

  3. Multi-fidelity and multi-disciplinary design optimization of supersonic business jets

    Science.gov (United States)

    Choi, Seongim

    Supersonic jets have been drawing great attention after the end of service for the Concorde was announced on April of 2003. It is believed, however, that civilian supersonic aircraft may make a viable return in the business jet market. This thesis focuses on the design optimization of feasible supersonic business jet configurations. Preliminary design techniques for mitigation of ground sonic boom are investigated while ensuring that all relevant disciplinary constraints are satisfied (including aerodynamic performance, propulsion, stability & control and structures.) In order to achieve reasonable confidence in the resulting designs, high-fidelity simulations are required, making the entire design process both expensive and complex. In order to minimize the computational cost, surrogate/approximate models are constructed using a hierarchy of different fidelity analysis tools including PASS, A502/Panair and Euler/NS codes. Direct search methods such as Genetic Algorithms (GAs) and a nonlinear SIMPLEX are employed to designs in searches of large and noisy design spaces. A local gradient-based search method can be combined with these global search methods for small modifications of candidate optimum designs. The Mesh Adaptive Direct Search (MADS) method can also be used to explore the design space using a solution-adaptive grid refinement approach. These hybrid approaches, both in search methodology and surrogate model construction, are shown to result in designs with reductions in sonic boom and improved aerodynamic performance.

  4. NDDP multi-stage flash desalination process simulator design process optimization

    International Nuclear Information System (INIS)

    Sashi Kumar, G.N.; Mahendra, A.K.; Sanyal, A.; Gouthaman, G.

    2009-03-01

    The improvement of NDDP-MSF plant's performance ratio (PR) from design value of 9.0 to 13.1 was achieved by optimizing the plant's operating parameters within the feasible zone of operation. This plant has 20% excess heat transfer area over the design condition which helped us to get a PR of 15.1 after optimization. Thus we have obtained, (1) A 45% increase in the output over design value by the optimization carried out with design heat transfer area. (2) A 68% increase in the output over design value by the optimization carried out with increased heat transfer area. This report discusses the approach, methodology and results of the optimization study carried out. A simulator, MSFSIM which predicts the performance of a multi-stage flash (MSF) desalination plant has been coupled with Genetic Algorithm (GA) optimizer. Exhaustive optimization case studies have been conducted on this plant with an objective to increase the performance ratio (PR). The steady state optimization performed was based on obtaining the best stage wise pressure profile to enhance thermal efficiency which in-turn improves the performance ratio. Apart from this, the recirculating brine flow rate was also optimized. This optimization study enabled us to increase the PR of NDDP-MSF plant from design value of 9.0 to an optimized value 13.1. The actual plant is provided with 20% additional heat transfer area over and above the design heat transfer area. Optimization with this additional heat transfer area has taken the PR to 15.1. A desire to maintain equal flashing rates in all of the stages (a feature required for long life of the plant and to avoid cascading effect of non-flashing triggered by any stage) of the MSF plant has also been achieved. The deviation in the flashing rates within stages has been reduced. The startup characteristic of the plant (i.e the variation of stage pressure and the variation of recirculation flow rate with time), have been optimized with a target to minimize the

  5. 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.

  6. 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.

  7. Biomimicry of symbiotic multi-species coevolution for discrete and continuous optimization in RFID networks

    Directory of Open Access Journals (Sweden)

    Na Lin

    2017-03-01

    Full Text Available In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS2Os, which extend the single population particle swarm optimization (PSO algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS2O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS2O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm’s performance. Then PS2O is used for solving the radio frequency identification (RFID network planning (RNP problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

  8. 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

  9. 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.

  10. Multi-Body Ski Jumper Model with Nonlinear Dynamic Inversion Muscle Control for Trajectory Optimization

    Directory of Open Access Journals (Sweden)

    Patrick Piprek

    2018-02-01

    Full Text Available This paper presents an approach to model a ski jumper as a multi-body system for an optimal control application. The modeling is based on the constrained Newton-Euler-Equations. Within this paper the complete multi-body modeling methodology as well as the musculoskeletal modeling is considered. For the musculoskeletal modeling and its incorporation in the optimization model, we choose a nonlinear dynamic inversion control approach. This approach uses the muscle models as nonlinear reference models and links them to the ski jumper movement by a control law. This strategy yields a linearized input-output behavior, which makes the optimal control problem easier to solve. The resulting model of the ski jumper can then be used for trajectory optimization whose results are compared to literature jumps. Ultimately, this enables the jumper to get a very detailed feedback of the flight. To achieve the maximal jump length, exact positioning of his body with respect to the air can be displayed.

  11. Using genetic algorithm to solve a new multi-period stochastic optimization model

    Science.gov (United States)

    Zhang, Xin-Li; Zhang, Ke-Cun

    2009-09-01

    This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.

  12. Optimization of the coherence function estimation for multi-core central processing unit

    Science.gov (United States)

    Cheremnov, A. G.; Faerman, V. A.; Avramchuk, V. S.

    2017-02-01

    The paper considers use of parallel processing on multi-core central processing unit for optimization of the coherence function evaluation arising in digital signal processing. Coherence function along with other methods of spectral analysis is commonly used for vibration diagnosis of rotating machinery and its particular nodes. An algorithm is given for the function evaluation for signals represented with digital samples. The algorithm is analyzed for its software implementation and computational problems. Optimization measures are described, including algorithmic, architecture and compiler optimization, their results are assessed for multi-core processors from different manufacturers. Thus, speeding-up of the parallel execution with respect to sequential execution was studied and results are presented for Intel Core i7-4720HQ и AMD FX-9590 processors. The results show comparatively high efficiency of the optimization measures taken. In particular, acceleration indicators and average CPU utilization have been significantly improved, showing high degree of parallelism of the constructed calculating functions. The developed software underwent state registration and will be used as a part of a software and hardware solution for rotating machinery fault diagnosis and pipeline leak location with acoustic correlation method.

  13. Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process

    Science.gov (United States)

    Yu, Tao; Kang, Chao; Zhao, Pan

    2018-01-01

    The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing. PMID:29385048

  14. 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.

  15. Optimization of Joint Power and Bandwidth Allocation in Multi-Spot-Beam Satellite Communication Systems

    Directory of Open Access Journals (Sweden)

    Heng Wang

    2014-01-01

    Full Text Available Multi-spot-beam technique has been widely applied in modern satellite communication systems. However, the satellite power and bandwidth resources in a multi-spot-beam satellite communication system are scarce and expensive; it is urgent to utilize the resources efficiently. To this end, dynamically allocating the power and bandwidth is an available way. This paper initially formulates the problem of resource joint allocation as a convex optimization problem, taking into account a compromise between the maximum total system capacity and the fairness among the spot beams. A joint bandwidth and power allocation iterative algorithm based on duality theory is then proposed to obtain the optimal solution of this optimization problem. Compared with the existing separate bandwidth or power optimal allocation algorithms, it is shown that the joint allocation algorithm improves both the total system capacity and the fairness among spot beams. Moreover, it is easy to be implemented in practice, as the computational complexity of the proposed algorithm is linear with the number of spot beams.

  16. Multi-region optimal deployment of renewable energy considering different interregional transmission scenarios

    International Nuclear Information System (INIS)

    Wang, Ge; Zhang, Qi; Mclellan, Benjamin C.; Li, Hailong

    2016-01-01

    Renewable energy is expected to play much more important role in future low-carbon energy system, however, renewable energy has problems with regard to load-following and regional imbalance. This study aims to plan the deployment of intermittent renewable energy in multiple regions considering the impacts of regional natural conditions and generation capacity mix as well as interregional transmission capacity using a multi-region dynamic optimization model. The model was developed to find optimized development paths toward future smart electricity systems with high level penetration of intermittent renewable energy considering regional differences and interregional transmission at national scale. As a case study, the model was applied to plan power generation in nine interconnected regions in Japan out to 2030. Four scenarios were proposed with different supporting policies for the interregional power transmission infrastructures and different nuclear power phase-out scenarios. The analysis results show that (i) the government's support for power transmission infrastructures is vital important to develop more intermittent renewable energy in appropriate regions and utilize renewable energy more efficiently; (ii) nuclear and renewable can complement rather than replace each other if enough interregional transmission capacity is provided. - Highlights: • Plan the optimal deployment of intermittent renewable energy in multiple regions. • A multi-region dynamic optimization model was developed. • The impacts of natural conditions and interregional transmission are studied. • The government's support for transmission is vital important for renewable energy. • Nuclear and renewable can complement rather than replace each other.

  17. Biomimicry of symbiotic multi-species coevolution for discrete and continuous optimization in RFID networks.

    Science.gov (United States)

    Lin, Na; Chen, Hanning; Jing, Shikai; Liu, Fang; Liang, Xiaodan

    2017-03-01

    In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS 2 Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS 2 O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS 2 O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm's performance. Then PS 2 O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

  18. Optimization of multi-color laser waveform for high-order harmonic generation

    Science.gov (United States)

    Jin, Cheng; Lin, C. D.

    2016-09-01

    With the development of laser technologies, multi-color light-field synthesis with complete amplitude and phase control would make it possible to generate arbitrary optical waveforms. A practical optimization algorithm is needed to generate such a waveform in order to control strong-field processes. We review some recent theoretical works of the optimization of amplitudes and phases of multi-color lasers to modify the single-atom high-order harmonic generation based on genetic algorithm. By choosing different fitness criteria, we demonstrate that: (i) harmonic yields can be enhanced by 10 to 100 times, (ii) harmonic cutoff energy can be substantially extended, (iii) specific harmonic orders can be selectively enhanced, and (iv) single attosecond pulses can be efficiently generated. The possibility of optimizing macroscopic conditions for the improved phase matching and low divergence of high harmonics is also discussed. The waveform control and optimization are expected to be new drivers for the next wave of breakthrough in the strong-field physics in the coming years. Project supported by the Fundamental Research Funds for the Central Universities of China (Grant No. 30916011207), Chemical Sciences, Geosciences and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U. S. Department of Energy (Grant No. DE-FG02-86ER13491), and Air Force Office of Scientific Research, USA (Grant No. FA9550-14-1-0255).

  19. 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.

  20. Multi-GPU implementation of a VMAT treatment plan optimization algorithm

    International Nuclear Information System (INIS)

    Tian, Zhen; Folkerts, Michael; Tan, Jun; Jia, Xun; Jiang, Steve B.; Peng, Fei

    2015-01-01

    Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is

  1. Multi-GPU implementation of a VMAT treatment plan optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Tian, Zhen, E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu; Folkerts, Michael; Tan, Jun; Jia, Xun, E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu; Jiang, Steve B., E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu [Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75390 (United States); Peng, Fei [Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 (United States)

    2015-06-15

    Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is

  2. Multi-Disciplinary Design Optimization of Hypersonic Air-Breathing Vehicle

    Science.gov (United States)

    Wu, Peng; Tang, Zhili; Sheng, Jianda

    2016-06-01

    A 2D hypersonic vehicle shape with an idealized scramjet is designed at a cruise regime: Mach number (Ma) = 8.0, Angle of attack (AOA) = 0 deg and altitude (H) = 30kms. Then a multi-objective design optimization of the 2D vehicle is carried out by using a Pareto Non-dominated Sorting Genetic Algorithm II (NSGA-II). In the optimization process, the flow around the air-breathing vehicle is simulated by inviscid Euler equations using FLUENT software and the combustion in the combustor is modeled by a methodology based on the well known combination effects of area-varying pipe flow and heat transfer pipe flow. Optimization results reveal tradeoffs among total pressure recovery coefficient of forebody, lift to drag ratio of vehicle, specific impulse of scramjet engine and the maximum temperature on the surface of vehicle.

  3. 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.

  4. Multi-objective Optimization Strategies Using Adjoint Method and Game Theory in Aerodynamics

    Science.gov (United States)

    Tang, Zhili

    2006-08-01

    There are currently three different game strategies originated in economics: (1) Cooperative games (Pareto front), (2) Competitive games (Nash game) and (3) Hierarchical games (Stackelberg game). Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi-criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.

  5. Distributed Multi-Commodity Network Flow Algorithm for Energy Optimal Routing in Wireless Sensor Networks.

    Directory of Open Access Journals (Sweden)

    J. Trdlicka

    2010-12-01

    Full Text Available This work proposes a distributed algorithm for energy optimal routing in a wireless sensor network. The routing problem is described as a mathematical problem by the minimum-cost multi-commodity network flow problem. Due to the separability of the problem, we use the duality theorem to derive the distributed algorithm. The algorithm computes the energy optimal routing in the network without any central node or knowledge of the whole network structure. Each node only needs to know the flow which is supposed to send or receive and the costs and capacities of the neighboring links. An evaluation of the presented algorithm on benchmarks for the energy optimal data flow routing in sensor networks with up to 100 nodes is presented.

  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. 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. A method for aggregating external operating conditions in multi-generation system optimization models

    DEFF Research Database (Denmark)

    Lythcke-Jørgensen, Christoffer Ernst; Münster, Marie; Ensinas, Adriano Viana

    2016-01-01

    This paper presents a novel, simple method for reducing external operating condition datasets to be used in multi-generation system optimization models. The method, called the Characteristic Operating Pattern (CHOP) method, is a visually-based aggregation method that clusters reference data based...... on parameter values rather than time of occurrence, thereby preserving important information on short-term relations between the relevant operating parameters. This is opposed to commonly used methods where data are averaged over chronological periods (months or years), and extreme conditions are hidden...... in the averaged values. The CHOP method is tested in a case study where the operation of a fictive Danish combined heat and power plant is optimized over a historical 5-year period. The optimization model is solved using the full external operating condition dataset, a reduced dataset obtained using the CHOP...

  9. 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.

  10. Multi-condition optimization and experimental study of impeller blades in a mixed-flow pump

    Directory of Open Access Journals (Sweden)

    Houlin Liu

    2016-05-01

    Full Text Available On the basis of design of experiment and numerical simulation, a reliable optimization method for blades of a mixed-flow pump is proposed with the maximum weighted average efficiency at multi-conditions as optimum objective. First, the performance of the model pump was measured and the test results were used to validate the simulation method. To improve the simulation accuracy, the check of the grid independence and the comparison of different turbulence models were done in detail. Then, the method of design of experiment for key geometrical parameters was used to obtain the optimization scheme. The maximum weighted average efficiency of pump at three operation conditions was chosen as optimum objective. The optimum solution was gotten and confirmed by the experiment. The results demonstrate that efficiency of the mixed-flow pump with optimized impeller increases by 3.9%, and the high-efficiency zone is increased from 0.021 to 0.040.

  11. Multi-Objective Optimization Considering Battery Degradation for a Multi-Mode Power-Split Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Xuerui Ma

    2017-07-01

    Full Text Available A multi-mode power-split (MMPS hybrid electric vehicle (HEV has two planetary gearsets and clutches/grounds which results in several operation modes with enhanced electric drive capability and better fuel economy. Basically, the battery storage system is involved in different operation modes to satisfy the power demand and minimize the fuel consumption, whereas the complicated operation modes with frequent charging/discharging will absolutely influence the battery life because of degradation. In this paper, firstly, we introduce the solid electrolyte interface (SEI film growth model based on the previous study of the battery degradation principles and was verified according to the test data. We consider both the fuel economy and battery degradation as a multi-objective problem for MMPS HEV by normalization with a weighting factor. An instantaneous optimization is implemented based on the equivalent fuel consumption concept. Then the control strategy is implemented on a simulation framework integrating the MMPS powertrain model and the SEI film growth map model over some typical driving cycles, such as New European Driving Cycle (NEDC and Urban Dynamometer Driving Schedule (UDDS. Finally, the result demonstrates that these two objectives are conflicting and the trade-off reduces the battery degradation with fuel sacrifice. Additionally, the analysis reveals how the mode selection will reflect the battery degradation.

  12. Oil fuel delivery optimization for multi product and multi depot: the case of petrol station replenishment problem (PSRP)

    Science.gov (United States)

    Surjandari, Isti; Rachman, Amar; Dianawati, Fauzia; Wibowo, R. Pramono

    2011-10-01

    With the Oil and Gas Law No. 22 of 2001, national and foreign private enterprises can invest in all sectors of Oil and Gas in Indonesia. In anticipation of this free competition, Pertamina, as a state-owned enterprises, which previously had monopolized the oil and gas business activities in Indonesia, should be able to improve services as well as the efficiency in order to compete in the free market, especially in terms of cost efficiency of fuel distribution to gas station (SPBU). To optimize the distribution activity, it is necessary to design a scheduling system and its fuel delivery routes daily to every SPBU. The determination of routes and scheduling delivery of fuel to the SPBU can be modeled as a Petrol Station Replenishment Problem (PSRP) with the multi-depot, multi-product, time windows and split deliveries, which in this study will be completed by the Tabu Search algorithm (TS). This study was conducted in the area of Bandung, the capital of West Java province, which is a big city and the neighboring city of Jakarta, the capital city of Indonesia. By using the fuel delivery data for one day, the results showed a decrease of 16.38% of the distance of the route compared to the current conditions, which impacted on the reduction of distribution costs and decrease the number of total trips by 5.22% and 3.83%.

  13. Macrosystems ecology: novel methods and new understanding of multi-scale patterns and processes

    Science.gov (United States)

    Songlin Fei; Qinfeng Guo; Kevin Potter

    2016-01-01

    As the global biomes are increasingly threatened by human activities, understanding of macroscale patterns and processes is pressingly needed for effective management and policy making. Macrosystems ecology, which studies multiscale ecologicalpatterns and processes, has gained growing interest in the research community. However, as a relatively new field in...

  14. Optimal sizing of a multi-source energy plant for power heat and cooling generation

    International Nuclear Information System (INIS)

    Barbieri, E.S.; Dai, Y.J.; Morini, M.; Pinelli, M.; Spina, P.R.; Sun, P.; Wang, R.Z.

    2014-01-01

    Multi-source systems for the fulfilment of electric, thermal and cooling demand of a building can be based on different technologies (e.g. solar photovoltaic, solar heating, cogeneration, heat pump, absorption chiller) which use renewable, partially renewable and fossil energy sources. Therefore, one of the main issues of these kinds of multi-source systems is to find the appropriate size of each technology. Moreover, building energy demands depend on the climate in which the building is located and on the characteristics of the building envelope, which also influence the optimal sizing. This paper presents an analysis of the effect of different climatic scenarios on the multi-source energy plant sizing. For this purpose a model has been developed and has been implemented in the Matlab ® environment. The model takes into consideration the load profiles for electricity, heating and cooling for a whole year. The performance of the energy systems are modelled through a systemic approach. The optimal sizing of the different technologies composing the multi-source energy plant is investigated by using a genetic algorithm, with the goal of minimizing the primary energy consumption only, since the cost of technologies and, in particular, the actual tariff and incentive scenarios depend on the specific country. Moreover economic considerations may lead to inadequate solutions in terms of primary energy consumption. As a case study, the Sino-Italian Green Energy Laboratory of the Shanghai Jiao Tong University has been hypothetically located in five cities in different climatic zones. The load profiles are calculated by means of a TRNSYS ® model. Results show that the optimal load allocation and component sizing are strictly related to climatic data (e.g. external air temperature and solar radiation)

  15. 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

  16. Hybrid PV/diesel solar power system design using multi-level factor analysis optimization

    Science.gov (United States)

    Drake, Joshua P.

    Solar power systems represent a large area of interest across a spectrum of organizations at a global level. It was determined that a clear understanding of current state of the art software and design methods, as well as optimization methods, could be used to improve the design methodology. Solar power design literature was researched for an in depth understanding of solar power system design methods and algorithms. Multiple software packages for the design and optimization of solar power systems were analyzed for a critical understanding of their design workflow. In addition, several methods of optimization were studied, including brute force, Pareto analysis, Monte Carlo, linear and nonlinear programming, and multi-way factor analysis. Factor analysis was selected as the most efficient optimization method for engineering design as it applied to solar power system design. The solar power design algorithms, software work flow analysis, and factor analysis optimization were combined to develop a solar power system design optimization software package called FireDrake. This software was used for the design of multiple solar power systems in conjunction with an energy audit case study performed in seven Tibetan refugee camps located in Mainpat, India. A report of solar system designs for the camps, as well as a proposed schedule for future installations was generated. It was determined that there were several improvements that could be made to the state of the art in modern solar power system design, though the complexity of current applications is significant.

  17. Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

    Science.gov (United States)

    Ariyarit, Atthaphon; Sugiura, Masahiko; Tanabe, Yasutada; Kanazaki, Masahiro

    2018-06-01

    A multi-fidelity optimization technique by an efficient global optimization process using a hybrid surrogate model is investigated for solving real-world design problems. The model constructs the local deviation using the kriging method and the global model using a radial basis function. The expected improvement is computed to decide additional samples that can improve the model. The approach was first investigated by solving mathematical test problems. The results were compared with optimization results from an ordinary kriging method and a co-kriging method, and the proposed method produced the best solution. The proposed method was also applied to aerodynamic design optimization of helicopter blades to obtain the maximum blade efficiency. The optimal shape obtained by the proposed method achieved performance almost equivalent to that obtained using the high-fidelity, evaluation-based single-fidelity optimization. Comparing all three methods, the proposed method required the lowest total number of high-fidelity evaluation runs to obtain a converged solution.

  18. 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.

  19. Multi-point optimization of recirculation flow type casing treatment in centrifugal compressors

    Science.gov (United States)

    Tun, Min Thaw; Sakaguchi, Daisaku

    2016-06-01

    High-pressure ratio and wide operating range are highly required for a turbocharger in diesel engines. A recirculation flow type casing treatment is effective for flow range enhancement of centrifugal compressors. Two ring grooves on a suction pipe and a shroud casing wall are connected by means of an annular passage and stable recirculation flow is formed at small flow rates from the downstream groove toward the upstream groove through the annular bypass. The shape of baseline recirculation flow type casing is modified and optimized by using a multi-point optimization code with a metamodel assisted evolutionary algorithm embedding a commercial CFD code CFX from ANSYS. The numerical optimization results give the optimized design of casing with improving adiabatic efficiency in wide operating flow rate range. Sensitivity analysis of design parameters as a function of efficiency has been performed. It is found that the optimized casing design provides optimized recirculation flow rate, in which an increment of entropy rise is minimized at grooves and passages of the rotating impeller.

  20. 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.

  1. Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs

    International Nuclear Information System (INIS)

    Martorell, S.; Sanchez, A.; Carlos, S.; Serradell, V.

    2002-01-01

    One of the main concerns of the nuclear industry is to improve the availability of safety-related systems at nuclear power plants (NPPs) to achieve high safety levels. The development of efficient testing and maintenance has been traditionally one of the different ways to guarantee high levels of systems availability, which are implemented at NPP through technical specification and maintenance requirements (TS and M). On the other hand, there is a widely recognized interest in using the probabilistic risk analysis (PRA) for risk-informed applications aimed to emphasize both effective risk control and effective resource expenditures at NPPs. TS and M-related parameters in a plant are associated with controlling risk or with satisfying requirements, and are candidate to be evaluated for their resource effectiveness in risk-informed applications. The resource versus risk-control effectiveness principles formally enter in optimization problems where the cost or the burden for the plant staff is to be minimized while the risk or the availability of the safety equipment is constrained to be at a given level, and vice versa. Optimization of TS and M has been found interesting from the very beginning. However, the resolution of such a kind of optimization problem has been limited to focus on only individual TS and M-related parameters (STI, AOT, PM frequency, etc.) and/or adopting an individual optimization criterion (availability, costs, plant risks, etc.). Nevertheless, a number of reasons exist (e.g. interaction, similar scope, etc.) that justify the growing interest in the last years to focus on the simultaneous and multi-criteria optimization of TS and M. In the simultaneous optimization of TS and M-related parameters based on risk (or unavailability) and cost, like in many other engineering optimization problems, one normally faces multi-modal and non-linear objective functions and a variety of both linear and non-linear constraints. Genetic algorithms (GAs) have

  2. Multi-point optimization on meridional shape of a centrifugal pump impeller for performance improvement

    Energy Technology Data Exchange (ETDEWEB)

    Pei, Ji; Wang, Wen Jie; Yuan, Shouqi [National Research Center of Pumps, Jiangsu University, Zhenjiang (China)

    2016-11-15

    A wide operating band is important for a pump to safely perform at maximum efficiency while saving energy. To widen the operating range, a multi-point optimization process based on numerical simulations in order to improve impeller performance of a centrifugal pump used in nuclear plant applications is proposed by this research. The Reynolds average Navier Stokes equations are utilized to perform the calculations. The meridional shape of the impeller was optimized based on the following four parameters; shroud arc radius, hub arc radius, shroud angle, and hub angle as the design variables. Efficiencies calculated under 0.6Qd, 1.0Qd and 1.62Qd were selected as the three optimized objectives. The Design of experiment method was applied to generate various impellers while 35 impellers were generated by the Latin hypercube sampling method. A Response surface function based on a second order function was applied to construct a mathematical relationship between the objectives and design variables. A multi-objective genetic algorithm was utilized to solve the response surface function to obtain the best optimized objectives as well as the best combination of design parameters. The results indicated that the pump performance predicted by numerical simulation was in agreement with the experimental performance. The optimized efficiencies based on the three operating conditions were increased by 3.9 %, 6.1 % and 2.6 %, respectively. In addition, the velocity distribution, pressure distribution, streamline and turbulence kinetic energy distribution of the optimized and reference impeller were compared and analyzed to illustrate the performance improvement.

  3. Optimizing strategy software for repetitive construction projects within multi-mode resources

    Directory of Open Access Journals (Sweden)

    Remon Fayek Aziz

    2013-09-01

    Full Text Available Estimating tender data for specific project is the most essential part in construction areas as of contractor’s view such as: proposed project duration with corresponding gross value and cash flows. This paper focuses on how to calculate tender data using Optimizing Strategy Software (OSS for repetitive construction projects with identical activity’s duration in case of single number of crew such as: project duration, project/bid price, project maximum working capital, and project net present value of the studied project. A simplified multi-objective optimization software (OSS will be presented that creates best tender data to contractor compared with more feasible options generated from multi-mode resources in a given project. OSS is intended to give more scenarios which provide practical support for typical construction contractors who need to optimize resource utilization in order to minimize project duration, project/bid price, and project maximum working capital while maximizing its net present value simultaneously. OSS is designed by java programing code system to provide a number of new and unique capabilities, including: (1 Ranking the obtained optimal plans according to a set of planner specified weights representing the relative importance of duration, price, maximum working capital and net present value in the analyzed project; (2 Visualizing and viewing the generated optimal trade-off; and (3 Providing seamless integration with available project management calculations. In order to provide the aforementioned capabilities of OSS, the system is implemented and developed in four main modules: (1 A user interface module; (2 A database module; (3 A running module; (4 A connecting module. At the end of the paper, an illustrative example will be presented to demonstrate and verify the applications of the proposed software (OSS to an optimization expressway of repetitive construction project.

  4. Multi-point optimization on meridional shape of a centrifugal pump impeller for performance improvement

    International Nuclear Information System (INIS)

    Pei, Ji; Wang, Wen Jie; Yuan, Shouqi

    2016-01-01

    A wide operating band is important for a pump to safely perform at maximum efficiency while saving energy. To widen the operating range, a multi-point optimization process based on numerical simulations in order to improve impeller performance of a centrifugal pump used in nuclear plant applications is proposed by this research. The Reynolds average Navier Stokes equations are utilized to perform the calculations. The meridional shape of the impeller was optimized based on the following four parameters; shroud arc radius, hub arc radius, shroud angle, and hub angle as the design variables. Efficiencies calculated under 0.6Qd, 1.0Qd and 1.62Qd were selected as the three optimized objectives. The Design of experiment method was applied to generate various impellers while 35 impellers were generated by the Latin hypercube sampling method. A Response surface function based on a second order function was applied to construct a mathematical relationship between the objectives and design variables. A multi-objective genetic algorithm was utilized to solve the response surface function to obtain the best optimized objectives as well as the best combination of design parameters. The results indicated that the pump performance predicted by numerical simulation was in agreement with the experimental performance. The optimized efficiencies based on the three operating conditions were increased by 3.9 %, 6.1 % and 2.6 %, respectively. In addition, the velocity distribution, pressure distribution, streamline and turbulence kinetic energy distribution of the optimized and reference impeller were compared and analyzed to illustrate the performance improvement

  5. Dynamic Programming Algorithm for Generation of Optimal Elimination Trees for Multi-frontal Direct Solver Over H-refined Grids

    KAUST Repository

    AbouEisha, Hassan M.; Moshkov, Mikhail; Calo, Victor M.; Paszynski, Maciej; Goik, Damian; Jopek, Konrad

    2014-01-01

    In this paper we present a dynamic programming algorithm for finding optimal elimination trees for computational grids refined towards point or edge singularities. The elimination tree is utilized to guide the multi-frontal direct solver algorithm

  6. 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.

  7. Study on collaborative optimization control of ventilation and radon reduction system based on multi-agent technology

    International Nuclear Information System (INIS)

    Dai Jianyong; Meng Lingcong; Zou Shuliang

    2015-01-01

    According to the radioactive safety features such as radon and its progeny, combined with the theory of ventilation system, structure of multi-agent system for ventilation and radon reduction system is constructed with the application of multi agent technology. The function attribute of the key agent and the connection between the nodes in the multi-agent system are analyzed to establish the distributed autonomous logic structure and negotiation mechanism of multi agent system of ventilation and radon reduction system, and thus to implement the coordination optimization control of the multi-agent system. The example analysis shows that the system structure of the multi-agent system of ventilation and reducing radon system and its collaborative mechanism can improve and optimize the radioactive pollutants control, which provides a theoretical basis and important application prospect. (authors)

  8. Multi-organ segmentation from multi-phase abdominal CT via 4D graphs using enhancement, shape and location optimization.

    Science.gov (United States)

    Linguraru, Marius George; Pura, John A; Chowdhury, Ananda S; Summers, Ronald M

    2010-01-01

    The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis (CAD) applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four abdominal organs from 4D CT data using graph cuts. Contrast-enhanced CT scans were obtained at two phases: non-contrast and portal venous. Intra-patient data were spatially normalized by non-linear registration. Then 4D erosion using population historic information of contrast-enhanced liver, spleen, and kidneys was applied to multi-phase data to initialize the 4D graph and adapt to patient specific data. CT enhancement information and constraints on shape, from Parzen windows, and location, from a probabilistic atlas, were input into a new formulation of a 4D graph. Comparative results demonstrate the effects of appearance and enhancement, and shape and location on organ segmentation.

  9. AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

    Science.gov (United States)

    Tsai, Wen-Ping; Chang, Fi-John; Chang, Li-Chiu; Herricks, Edwin E.

    2015-11-01

    Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs.

  10. Deciphering the microbial ecology in bio- gas reactors for optimizing the anaerobic digestion process

    DEFF Research Database (Denmark)

    Zhu, Xinyu

    of the basic microbial metabolism and ecology, methanogenic microbial communities were enriched in a lab-scale continuous stirred-tank reactor (CSTR) fed with synthetic feedstocks. In the experiment, the substrates used were stepwise simplified (i.e. polysaccharide, monosaccharide, short chain fatty acids...

  11. 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.

  12. 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.

  13. Multi-variable optimization of PEMFC cathodes using an agglomerate model

    Energy Technology Data Exchange (ETDEWEB)

    Secanell, M.; Suleman, A.; Djilali, N. [Institute for Integrated Energy Systems and Department Mechanical Engineering, University of Victoria, PO Box 3055 STN CSC, Victoria, BC (Canada); Karan, K. [Queen' s-RMC Fuel Cell Research Centre and Department Chemical Engineering, Queen' s University, Kingston, Ont. (Canada)

    2007-06-30

    A comprehensive numerical framework for cathode electrode design is presented and applied to predict the catalyst layer and the gas diffusion layer parameters that lead to an optimal electrode performance at different operating conditions. The design and optimization framework couples an agglomerate cathode catalyst layer model to a numerical gradient-based optimization algorithm. The set of optimal parameters is obtained by solving a multi-variable optimization problem. The parameters are the catalyst layer platinum loading, platinum to carbon ratio, amount of electrolyte in the agglomerate and the gas diffusion layer porosity. The results show that the optimal catalyst layer composition and gas diffusion layer porosity depend on operating conditions. At low current densities, performance is mainly improved by increasing platinum loading to values above 1 mg cm{sup -2}, moderate values of electrolyte volume fraction, 0.5, and low porosity, 0.1. At higher current densities, performance is improved by reducing the platinum loading to values below 0.35 mg cm{sup -2} and increasing both electrolyte volume fraction, 0.55, and porosity 0.32. The underlying improvements due to the optimized compositions are analyzed in terms of the spatial distribution of the various overpotentials, and the effect of the agglomerate structure parameters (radius and electrolyte film) are investigated. The paper closes with a discussion of the optimized composition obtained in this study in the context of available experimental data. The analysis suggests that reducing the solid phase volume fraction inside the catalyst layer might lead to improved electrode performance. (author)

  14. 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.

  15. 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.

  16. The equivalence of multi-criteria methods for radiotherapy plan optimization

    International Nuclear Information System (INIS)

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

    2009-01-01

    Several methods can be used to achieve multi-criteria optimization of radiation therapy treatment planning, which strive for Pareto-optimality. The property of the solution being Pareto optimal is desired, because it guarantees that no criteria can be improved without deteriorating another criteria. The most widely used methods are the weighted-sum method, in which the different treatment objectives are weighted, and constrained optimization methods, in which treatment goals are set and the algorithm has to find the best plan fulfilling these goals. The constrained method used in this paper, the 2pεc (2-phase ε-constraint) method is based on the ε-constraint method, which generates Pareto-optimal solutions. Both approaches are uniquely related to each other. In this paper, we will show that it is possible to switch from the constrained method to the weighted-sum method by using the Lagrange multipliers from the constrained optimization problem, and vice versa by setting the appropriate constraints. In general, the theory presented in this paper can be useful in cases where a new situation is slightly different from the original situation, e.g. in online treatment planning, with deformations of the volumes of interest, or in automated treatment planning, where changes to the automated plan have to be made. An example of the latter is given where the planner is not satisfied with the result from the constrained method and wishes to decrease the dose in a structure. By using the Lagrange multipliers, a weighted-sum optimization problem is constructed, which generates a Pareto-optimal solution in the neighbourhood of the original plan, but fulfills the new treatment objectives.

  17. 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)

  18. 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.

  19. 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

  20. 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