WorldWideScience

Sample records for hybrid multi objective

  1. Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

    Science.gov (United States)

    2009-03-10

    Algorithm ( MOHO ) with Automatic Switching 4 Two-Objective Hybrid Optimization with a Response Surface 12 Response Surfaces using Wavelet-Based Neural...optimization. Results presented in this report confirm that MOHO is one such optimization concept that works. Multi-dimensional response surfaces... MOHO ) With Automatic Switching Among Individual Search Algorithms The MOHO software [1,2,3] that was developed as a part of this effort is a high

  2. A hybrid multi-objective evolutionary algorithm approach for ...

    Indian Academy of Sciences (India)

    The performance of the proposed multi-objective AI-NSGA-II algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and ... Department of Manufacturing, School of Mechanical Engineering, VIT University, Vellore, India; Department of Industrial and Systems Engineering, The Hong Kong ...

  3. A hybrid multi-objective evolutionary algorithm approach for ...

    Indian Academy of Sciences (India)

    V K MANUPATI

    1 Department of Manufacturing, School of Mechanical Engineering, VIT University, Vellore, India. 2 Department of Industrial and Systems Engineering, The ... algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventional non-dominated sorting genetic algorithm (CNSGA-II), ...

  4. Multi objective decision making in hybrid energy system design

    Science.gov (United States)

    Merino, Gabriel Guillermo

    The design of grid-connected photovoltaic wind generator system supplying a farmstead in Nebraska has been undertaken in this dissertation. The design process took into account competing criteria that motivate the use of different sources of energy for electric generation. The criteria considered were 'Financial', 'Environmental', and 'User/System compatibility'. A distance based multi-objective decision making methodology was developed to rank design alternatives. The method is based upon a precedence order imposed upon the design objectives and a distance metric describing the performance of each alternative. This methodology advances previous work by combining ambiguous information about the alternatives with a decision-maker imposed precedence order in the objectives. Design alternatives, defined by the photovoltaic array and wind generator installed capacities, were analyzed using the multi-objective decision making approach. The performance of the design alternatives was determined by simulating the system using hourly data for an electric load for a farmstead and hourly averages of solar irradiation, temperature and wind speed from eight wind-solar energy monitoring sites in Nebraska. The spatial variability of the solar energy resource within the region was assessed by determining semivariogram models to krige hourly and daily solar radiation data. No significant difference was found in the predicted performance of the system when using kriged solar radiation data, with the models generated vs. using actual data. The spatial variability of the combined wind and solar energy resources was included in the design analysis by using fuzzy numbers and arithmetic. The best alternative was dependent upon the precedence order assumed for the main criteria. Alternatives with no PV array or wind generator dominated when the 'Financial' criteria preceded the others. In contrast, alternatives with a nil component of PV array but a high wind generator component

  5. 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.

  6. 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.

  7. Biased Random Key Genetic Algorithm with Hybrid Decoding for Multi-objective Optimization

    OpenAIRE

    Tangpattanakul, Panwadee; Jozefowiez, Nicolas; Lopez, Pierre

    2013-01-01

    International audience; A biased random key genetic algorithm (BRKGA) is an efficient method for solving combinatorial optimization problems. It can be applied to solve both single-objective and multi-objective optimization problems. The BRKGA operates on a chromosome encoded as a key vector of real values between [0,1]. Generally, the chromosome has to be decoded by using a single decoding method in order to obtain a feasible solution. This paper presents a hybrid decoding, which combines th...

  8. An Evolutionary Mobility Aware Multi-Objective Hybrid Routing Algorithm for Heterogeneous Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Kulkarni, Nandkumar P.; Prasad, Neeli R.; Prasad, Ramjee

    change dynamically. In this paper, the authors put forward an Evolutionary Mobility aware multi-objective hybrid Routing Protocol for heterogeneous wireless sensor networks (EMRP). EMRP uses two-level hierarchical clustering. EMRP selects the optimal path from source to sink using multiple metrics...... such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. The authors study the influence of energy heterogeneity and mobility of sensor nodes on the performance of EMRP. The Performance of EMRP compared with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-Objective Routing...... Algorithm (DyMORA) using metrics such as Average Residual Energy (ARE), Delay and Normalized Routing Load. EMRP improves AES by a factor of 4.93% as compared to SHRP and 5.15% as compared to DyMORA. EMRP has a 6% lesser delay as compared with DyMORA....

  9. Multi-objective design and control of hybrid systems minimizing costs and unmet load

    Energy Technology Data Exchange (ETDEWEB)

    Bernal-Agustin, Jose L.; Dufo-Lopez, Rodolfo [Department of Electrical Engineering, University of Zaragoza, Calle Maria de Luna 3, 50018 Zaragoza (Spain)

    2009-01-15

    This paper presents, for the first time, the application of the strength Pareto evolutionary algorithm to the multi-objective design of isolated hybrid systems, minimising both the total cost throughout the useful life of the installation and the unmet load. For this task, a multi-objective evolutionary algorithm (MOEA) and a genetic algorithm (GA) have been used in order to find the best combinations of components for the hybrid system and control strategy. Also, a novel control strategy has been developed and it will be expounded in this article. As an example of application, a PV-wind-diesel system has been designed, obtaining a set of possible solutions (Pareto set) from which the designer can choose those which he/she prefers considering the costs and unmet load of each. The results obtained demonstrate the practical utility of the design method used. (author)

  10. 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.

  11. Multi-Objective Configuration Optimization of a Hybrid Energy Storage System

    Directory of Open Access Journals (Sweden)

    Shan Cheng

    2017-02-01

    Full Text Available This study aims to investigate multi-objective configuration optimization of a hybrid energy storage system (HESS. In order to maximize the stability of the wind power output with minimized HESS investment, a multi-objective model for optimal HESS configuration has been established, which proposes decreasing the installation and operation & maintenance costs of an HESS and improving the compensation satisfaction rate of wind power fluctuation. Besides, fuzzy control has been used to allocate power in the HESS for lengthening battery lifetime and ensuring HESS with enough energy to compensate the fluctuation of the next time interval. Instead of converting multiple objectives into one, a multi-objective particle swarm optimization with integration of bacteria quorum sensing and circular elimination (BC-MOPSO has been applied to provide diverse alternative solutions. In order to illustrate the feasibility and effectiveness of the proposed model and the application of BC-MOPSO, simulations along with analysis and discussion are carried out. The results verified the feasibility and effectiveness of the proposed approach.

  12. 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.

  13. A Frequency Control Approach for Hybrid Power System Using Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    Mohammed Elsayed Lotfy

    2017-01-01

    Full Text Available A hybrid power system uses many wind turbine generators (WTG and solar photovoltaics (PV in isolated small areas. However, the output power of these renewable sources is not constant and can diverge quickly, which has a serious effect on system frequency and the continuity of demand supply. In order to solve this problem, this paper presents a new frequency control scheme for a hybrid power system to ensure supplying a high-quality power in isolated areas. The proposed power system consists of a WTG, PV, aqua-electrolyzer (AE, fuel cell (FC, battery energy storage system (BESS, flywheel (FW and diesel engine generator (DEG. Furthermore, plug-in hybrid electric vehicles (EVs are implemented at the customer side. A full-order observer is utilized to estimate the supply error. Then, the estimated supply error is considered in a frequency domain. The high-frequency component is reduced by BESS and FW; while the low-frequency component of supply error is mitigated using FC, EV and DEG. Two PI controllers are implemented in the proposed system to control the system frequency and reduce the supply error. The epsilon multi-objective genetic algorithm ( ε -MOGA is applied to optimize the controllers’ parameters. The performance of the proposed control scheme is compared with that of recent well-established techniques, such as a PID controller tuned by the quasi-oppositional harmony search algorithm (QOHSA. The effectiveness and robustness of the hybrid power system are investigated under various operating conditions.

  14. A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

    Science.gov (United States)

    Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

    2014-09-01

    The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

  15. A hybrid model for multi-objective capacitated facility location network design problem

    Directory of Open Access Journals (Sweden)

    Mohammad saeed JabalAmeli

    2011-01-01

    Full Text Available One of the primary concerns on many traditional capacitated facility location/network problems is to consider transportation and setup facilities in one single objective function. This simple assumption may lead to misleading solutions since the cost of transportation is normally considered for a short period time and, obviously, the higher cost of setting up the facilities may reduce the importance of the transportation cost/network. In this paper, we introduce capacitated facility location/network design problem (CFLNDP with two separate objective functions in forms of multi-objective with limited capacity. The proposed model is solved using a new hybrid algorithm where there are two stages. In the first stage, locations of facilities and design of fundamental network are determined and in the second stage demands are allocated to the facilities. The resulted multi-objective problem is solved using Lexicography method for a well-known example from the literature with 21 node instances. We study the behaviour of the resulted problem under different scenarios in order to gain insight into the behaviour of the model in response to changes in key problem parameters.

  16. Hybrid Multi-objective Forecasting of Solar Photovoltaic Output Using Kalman Filter based Interval Type-2 Fuzzy Logic System

    DEFF Research Database (Denmark)

    Hassan, Saima; Ahmadieh Khanesar, Mojtaba; Hajizadeh, Amin

    2017-01-01

    Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic....../D) in the second hybrid algorithm. Root mean square error and maximum absolute error as the two accuracy objective are utilized to find the Pareto-optimal solution with the MOPSO and MOEA/D respectively. The proposed hybrid multi-objective designs of the interval type-2 fuzzy logic system are utilized...

  17. Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

    Directory of Open Access Journals (Sweden)

    Narong Wichapa

    2018-01-01

    Full Text Available Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any stand-alone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in Upper-Northeastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP. After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP, namely the HGP model, was tested. Finally, the vehicle routing problem (VRP for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA which hybridizes the push forward insertion heuristic (PFIH, genetic algorithm (GA and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles

  18. Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

    Science.gov (United States)

    Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

    2017-02-01

    In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

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

    Directory of Open Access Journals (Sweden)

    M. Balasubbareddy

    2015-12-01

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

  20. Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Lvjiang Yin

    2016-12-01

    Full Text Available Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T, cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II, Strength Pareto Evolutionary Algorithm 2 (SPEA2, Multiobjective Particle Swarm Optimization (OMOPSO, and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D. Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

  1. An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective Distribution Feeder Reconfiguration

    Energy Technology Data Exchange (ETDEWEB)

    Niknam, Taher [Electronic and Electrical Engineering Department, Shiraz University of Technology, Shiraz (Iran)

    2009-08-15

    This paper introduces a robust searching hybrid evolutionary algorithm to solve the multi-objective Distribution Feeder Reconfiguration (DFR). The main objective of the DFR is to minimize the real power loss, deviation of the nodes' voltage, the number of switching operations, and balance the loads on the feeders. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. This paper presents a new approach based on norm3 for the DFR problem. In the proposed method, the objective functions are considered as a vector and the aim is to maximize the distance (norm2) between the objective function vector and the worst objective function vector while the constraints are met. Since the proposed DFR is a multi objective and non-differentiable optimization problem, a new hybrid evolutionary algorithm (EA) based on the combination of the Honey Bee Mating Optimization (HBMO) and the Discrete Particle Swarm Optimization (DPSO), called DPSO-HBMO, is implied to solve it. The results of the proposed reconfiguration method are compared with the solutions obtained by other approaches, the original DPSO and HBMO over different distribution test systems. (author)

  2. Hybrid Evolutionary Metaheuristics for Concurrent Multi-Objective Design of Urban Road and Public Transit Networks

    NARCIS (Netherlands)

    Miandoabchi, Elnaz; Farahani, Reza Zanjirani; Dullaert, Wout; Szeto, W. Y.

    This paper addresses a bi-modal multi-objective discrete urban road network design problem with automobile and bus flow interaction. The problem considers the concurrent urban road and bus network design in which the authorities play a major role in designing bus network topology. The road network

  3. Multi-objective decoupling algorithm for active distance control of intelligent hybrid electric vehicle

    Science.gov (United States)

    Luo, Yugong; Chen, Tao; Li, Keqiang

    2015-12-01

    The paper presents a novel active distance control strategy for intelligent hybrid electric vehicles (IHEV) with the purpose of guaranteeing an optimal performance in view of the driving functions, optimum safety, fuel economy and ride comfort. Considering the complexity of driving situations, the objects of safety and ride comfort are decoupled from that of fuel economy, and a hierarchical control architecture is adopted to improve the real-time performance and the adaptability. The hierarchical control structure consists of four layers: active distance control object determination, comprehensive driving and braking torque calculation, comprehensive torque distribution and torque coordination. The safety distance control and the emergency stop algorithms are designed to achieve the safety and ride comfort goals. The optimal rule-based energy management algorithm of the hybrid electric system is developed to improve the fuel economy. The torque coordination control strategy is proposed to regulate engine torque, motor torque and hydraulic braking torque to improve the ride comfort. This strategy is verified by simulation and experiment using a forward simulation platform and a prototype vehicle. The results show that the novel control strategy can achieve the integrated and coordinated control of its multiple subsystems, which guarantees top performance of the driving functions and optimum safety, fuel economy and ride comfort.

  4. Capacitated Windy Rural Postman Problem with Several Vehicles: A Hybrid Multi-Objective Simulated Annealing Algorithm

    Directory of Open Access Journals (Sweden)

    Masoud Rabbani

    2016-02-01

    Full Text Available This paper presents the capacitated Windy Rural Postman Problem with several vehicles. For this problem, two objectives are considered. One of them is the minimization of the total cost of all vehicle routes expressed by the sum of the total traversing cost and another one is reduction of the maximum cost of vehicle route in order to find a set of equitable tours for the vehicles. Mathematical formulation is provided. The multi-objective simulated annealing (MOSA algorithm has been modified for solving this bi-objective NP-hard problem. To increase algorithm performance, Taguchi technique is applied to design experiments for tuning parameters of the algorithm. Numerical experiments are proposed to show efficiency of the model. Finally, the results of the MOSA have been compared with MOCS (multi-objective Cuckoo Search algorithm to validate the performance of the proposed algorithm. The experimental results indicate that the proposed algorithm provides good solutions and performs significantly better than the MOCS.

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Maryam Mousavi

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

  7. Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

    Directory of Open Access Journals (Sweden)

    Kiran Teeparthi

    2017-04-01

    Full Text Available In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators.

  8. A Hybrid Estimation of Distribution Algorithm for Multi-Objective Multi-Sourcing Intermodal Transportation Network Design Problem Considering Carbon Emissions

    Directory of Open Access Journals (Sweden)

    Shou-feng Ji

    2017-06-01

    Full Text Available The increasing concern on global warming is prompting transportation sector to take into account more sustainable operation strategies. Among them, intermodal transportation (IT has already been regarded as one of the most effective measures on carbon reductions. This paper focuses on the model and algorithm for a certain kind of IT, namely multi-objective multi-sourcing intermodal transportation network design problem (MO_MITNDP, in which carbon emission factors are specially considered. The MO_MITNDP is concerned with determining optimal transportation routes and modes for a series of freight provided by multiple sourcing places to find good balance between the total costs and time efficiencies. First, we establish a multi-objective integer programming model to formulate the MO_MITNDP with total cost (TTC and maximum flow time (MFT criteria. Specifically, carbon emission costs distinguished by the different transportation mode and route are included in the cost function. Second, to solve the MO_MITNDP, a hybrid estimation of distribution algorithm (HEDA combined with a heterogeneous marginal distribution and a multi-objective local search is proposed, in which the from the Pareto dominance scenario. Finally, based on randomly generated data and a real-life case study of Jilin Petrochemical Company (JPC, China, simulation experiments and comparisons are carried out to demonstrate the effectiveness and application value of the proposed HEDA.

  9. Multi-Objective Optimization in Battery Selection for Hybrid Electric Vehicle Applications

    Directory of Open Access Journals (Sweden)

    Aishwarya Panday

    2016-06-01

    Full Text Available This paper proclaims the battery selection for hybrid electric vehicle applications using multiobjective optimization techniques. Ashby's methodology, Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS and VIse Kriterijum-ska Optimizacija Komprominsno Resenje (VIKOR methods are employed here for the assessment. Various attributes considered for analysis are specific energy, energy density, electrical efficiency, self-discharge rate, nominal cell voltage, energy, cost and durability. The batteries considered for analysis are Liion, Ni-MH, Ni-Cd and Pb-acid. Based on the performance indices and battery attributes, selection charts and tables are presented here. It is observed that Li-ion batteries are most suitable for hybrid electric vehicle applications followed by Ni-MH batteries. The outcomes of all methods considered are uniform and promising. The results obtained are also matched up with actual practices in automotive industries. Alike results confirm the validity of this study.

  10. Multi-Objective Optimal Design of Stand-Alone Hybrid Energy System Using Entropy Weight Method Based on HOMER

    Directory of Open Access Journals (Sweden)

    Jiaxin Lu

    2017-10-01

    Full Text Available Implementation of hybrid energy system (HES is generally considered as a promising way to satisfy the electrification requirements for remote areas. In the present study, a novel decision making methodology is proposed to identify the best compromise configuration of HES from a set of feasible combinations obtained from HOMER. For this purpose, a multi-objective function, which comprises four crucial and representative indices, is formulated by applying the weighted sum method. The entropy weight method is employed as a quantitative methodology for weighting factors calculation to enhance the objectivity of decision-making. Moreover, the optimal design of a stand-alone PV/wind/battery/diesel HES in Yongxing Island, China, is conducted as a case study to validate the effectiveness of the proposed method. Both the simulation and optimization results indicate that, the optimization method is able to identify the best trade-off configuration among system reliability, economy, practicability and environmental sustainability. Several useful conclusions are given by analyzing the operation of the best configuration.

  11. Multi Objective Optimization of Flux Cored Arc Weld Parameters Using Hybrid Grey - Fuzzy Technique

    Directory of Open Access Journals (Sweden)

    M Satheesh

    2014-06-01

    Full Text Available In the present work, an attempt has been made to use the grey-based fuzzy logic method to solve correlated multiple response optimization problems in the field of flux cored arc welding. This approach converts the complex multiple objectives into a single grey-fuzzy reasoning grade. Based on the grey-fuzzy reasoning grade, optimum parameters are identified. The significant contributions of parameters are estimated using analysis of variance (ANOVA. This evaluation procedure can be used in intelligent decision making for a welding operator. The proposed and developed method has good accuracy and competency. The proposed technique provides manufacturers who develop intelligent manufacturing systems a method to facilitate the achievement of the highest level of automation.

  12. Damage and failure modelling of hybrid three-dimensional textile composites: a mesh objective multi-scale approach

    Science.gov (United States)

    Patel, Deepak K.

    2016-01-01

    This paper is concerned with predicting the progressive damage and failure of multi-layered hybrid textile composites subjected to uniaxial tensile loading, using a novel two-scale computational mechanics framework. These composites include three-dimensional woven textile composites (3DWTCs) with glass, carbon and Kevlar fibre tows. Progressive damage and failure of 3DWTCs at different length scales are captured in the present model by using a macroscale finite-element (FE) analysis at the representative unit cell (RUC) level, while a closed-form micromechanics analysis is implemented simultaneously at the subscale level using material properties of the constituents (fibre and matrix) as input. The N-layers concentric cylinder (NCYL) model (Zhang and Waas 2014 Acta Mech. 225, 1391–1417; Patel et al. submitted Acta Mech.) to compute local stress, srain and displacement fields in the fibre and matrix is used at the subscale. The 2-CYL fibre–matrix concentric cylinder model is extended to fibre and (N−1) matrix layers, keeping the volume fraction constant, and hence is called the NCYL model where the matrix damage can be captured locally within each discrete layer of the matrix volume. The influence of matrix microdamage at the subscale causes progressive degradation of fibre tow stiffness and matrix stiffness at the macroscale. The global RUC stiffness matrix remains positive definite, until the strain softening response resulting from different failure modes (such as fibre tow breakage, tow splitting in the transverse direction due to matrix cracking inside tow and surrounding matrix tensile failure outside of fibre tows) are initiated. At this stage, the macroscopic post-peak softening response is modelled using the mesh objective smeared crack approach (Rots et al. 1985 HERON 30, 1–48; Heinrich and Waas 2012 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Honolulu, HI, 23–26 April 2012. AIAA 2012-1537). Manufacturing

  13. A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle

    Science.gov (United States)

    Nejlaoui, Mohamed; Houidi, Ajmi; Affi, Zouhaier; Romdhane, Lotfi

    2017-10-01

    This paper deals with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. A combined multi-objective imperialist competitive algorithm and Monte Carlo method is developed and used for the robust multi-objective optimization of the rail vehicle system. This robust optimization of rail vehicle safety considers simultaneously the derailment angle and its standard deviation where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the rail vehicle safety to the design parameters uncertainties compared to the determinist one and to the literature results.

  14. Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing

    Directory of Open Access Journals (Sweden)

    Kumar Deepak

    2015-12-01

    Full Text Available Groundwater contamination due to leakage of gasoline is one of the several causes which affect the groundwater environment by polluting it. In the past few years, In-situ bioremediation has attracted researchers because of its ability to remediate the contaminant at its site with low cost of remediation. This paper proposed the use of a new hybrid algorithm to optimize a multi-objective function which includes the cost of remediation as the first objective and residual contaminant at the end of the remediation period as the second objective. The hybrid algorithm was formed by combining the methods of Differential Evolution, Genetic Algorithms and Simulated Annealing. Support Vector Machines (SVM was used as a virtual simulator for biodegradation of contaminants in the groundwater flow. The results obtained from the hybrid algorithm were compared with Differential Evolution (DE, Non Dominated Sorting Genetic Algorithm (NSGA II and Simulated Annealing (SA. It was found that the proposed hybrid algorithm was capable of providing the best solution. Fuzzy logic was used to find the best compromising solution and finally a pumping rate strategy for groundwater remediation was presented for the best compromising solution. The results show that the cost incurred for the best compromising solution is intermediate between the highest and lowest cost incurred for other non-dominated solutions.

  15. 4E analysis and multi objective optimization of a micro gas turbine and solid oxide fuel cell hybrid combined heat and power system

    Science.gov (United States)

    Sanaye, Sepehr; Katebi, Arash

    2014-02-01

    Energy, exergy, economic and environmental (4E) analysis and optimization of a hybrid solid oxide fuel cell and micro gas turbine (SOFC-MGT) system for use as combined generation of heat and power (CHP) is investigated in this paper. The hybrid system is modeled and performance related results are validated using available data in literature. Then a multi-objective optimization approach based on genetic algorithm is incorporated. Eight system design parameters are selected for the optimization procedure. System exergy efficiency and total cost rate (including capital or investment cost, operational cost and penalty cost of environmental emissions) are the two objectives. The effects of fuel unit cost, capital investment and system power output on optimum design parameters are also investigated. It is observed that the most sensitive and important design parameter in the hybrid system is fuel cell current density which has a significant effect on the balance between system cost and efficiency. The selected design point from the Pareto distribution of optimization results indicates a total system exergy efficiency of 60.7%, with estimated electrical energy cost 0.057 kW-1 h-1, and payback period of about 6.3 years for the investment.

  16. Optimal multi-objective reconfiguration and capacitor placement of distribution systems with the Hybrid Big Bang–Big Crunch algorithm in the fuzzy framework

    Directory of Open Access Journals (Sweden)

    Mostafa Sedighizadeh

    2016-03-01

    Full Text Available Network reconfiguration and capacitor placement are useful options applied to reduce power losses and to keep voltage profiles within permissible limits in distribution systems. This study presents an efficient algorithm for optimization of balanced and unbalanced radial distribution systems by a network reconfiguration and capacitor placement. An important property of the proposed approach is solving the multi-objective reconfiguration and capacitor placement in fuzzy framework and its high accuracy and fast convergence. The considered objectives are the minimization of total network real power losses, the minimization of buses voltage violation, and load balancing in the feeders. The proposed algorithm has been implemented in three IEEE test systems (two balanced and one unbalanced systems. Numerical results obtained by simulation show that the performance of the Hybrid Big Bang Big Crunch (HBB–BC algorithm is slightly higher than or similar to other meta-heuristic algorithms.

  17. Quick Screening of Pareto-Optimal Operating Conditions for Expanding Solvent–Steam Assisted Gravity Drainage Using Hybrid Multi-Objective Optimization Approach

    Directory of Open Access Journals (Sweden)

    Baehyun Min

    2017-07-01

    Full Text Available Solvent–steam mixture is a key factor in controlling the economic efficiency of the solvent-aided thermal injection process for producing bitumen in a highly viscous oil sands reservoir. This paper depicts a strategy to quickly provide trade-off operating conditions of the Expanding Solvent–Steam Assisted Gravity Drainage (ES-SAGD process based on Pareto-optimality. Response surface models are employed to evaluate multiple ES-SAGD scenarios at low computational costs. The surrogate models play a role of objective-estimators in the multi-objective optimization that provides qualified ES-SAGD scenarios regarding bitumen recovery, steam–energy efficiency, and solvent-energy efficiency. The developed hybrid approach detects positive or negative correlations among the performance indicators of the ES-SAGD process. The derived Pareto-optimal operating conditions give flexibility in field development planning and thereby help decision makers determine the operating parameters of the ES-SAGD process based on their preferences.

  18. Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait.

    Science.gov (United States)

    Martins, Maria; Costa, Lino; Frizera, Anselmo; Ceres, Ramón; Santos, Cristina

    2014-03-01

    Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  19. Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

    Science.gov (United States)

    Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

    2016-01-01

    Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

  20. Multi-objective energy analysis

    Energy Technology Data Exchange (ETDEWEB)

    Cherniavsky, E.A.

    1979-11-01

    Analytic models have been applied to energy-planning problems in an effort to assess the probable impacts of alternative courses of action on vital social concerns such as the quality of the environment, the state of the economy, or extent of dependence on insecure foreign energy sources. A proposed program may have a variety of effects on social objectives; beneficial results in one area may be purchased at the cost of undesirable consequences in another. A policy must be judged by its impacts on a number of social concerns. The purpose of multi-objective analysis is to identify and quantify the tradeoffs between different social objectives, and to aid policymakers in formulating decisions that achieve the best possible compromise between conflicting goals. This paper reviews approaches and techniques currently employed in multi-objective analysis. Associated problems are explored and discussed in the light of experience with applications to energy-planning models. Conclusions are drawn concerning the most-fruitful directions for future research in this area. 40 references.

  1. 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...

  2. Multi-threaded Object Streaming

    Science.gov (United States)

    Di Guida, Salvatore; Govi, Giacomo; Ojeda, Miguel; Pfeiffer, Andreas; Sipos, Roland

    2015-12-01

    The CMS experiment at the Large Hadron Collider (LHC) at CERN, Geneva, Switzerland, is made of many detectors which in total sum up to more than 75 million channels. The detector monitoring information of all channels (temperatures, voltages, etc.), detector quality, beam conditions, and other data crucial for the reconstruction and analysis of the experiment's recorded collision events is stored in an online database. A subset of that information, the “conditions data”, is copied out to another database from where it is used in the offline reconstruction and analysis processing, together with alignment data for the various detectors. Conditions data sets are accessed by a tag and an interval of validity through the offline reconstruction program CMSSW, written in C++. About 400 different types of calibration and alignment exist for the various CMS sub-detectors. With the CMS software framework moving to a multi-threaded execution model, and profiting from the experience gained during the data taking in Run-1, a major re-design of the CMS conditions software was done. During this work, a study was done to look into possible gains by using multi-threaded handling of the conditions. In this paper, we present the results of that study.

  3. Multi-threaded Object Streaming

    CERN Document Server

    Pfeiffer, Andreas; Govi, Giacomo; Ojeda, Miguel; Sipos, Roland

    2015-01-01

    The CMS experiment at CERNs Large Hadron Collider in Geneva redesigned the code handling the conditions data during the last years, aiming to increase performance and enhance maintainability. The new design includes a move to serialise all payloads before storing them into the database, allowing the handling of the payloads in external tools independent of a given software release. In this talk we present the results of performance studies done using the serialisation package from the Boost suite as well as serialisation done with the ROOT (v5) tools. Furthermore, as the Boost tools allow parallel (de-)serialisation, we show the performance gains achieved with parallel threads when de-serialising a realistic set of conditions in CMS. Without specific optimisations an overall speed up of a factor of 3-4 was achieved using multi-threaded loading and de-serialisation of our conditions.

  4. Multi-objective analysis of mixed integer planning problem according to hybrid genetic algorithm. Application of waste disposal facility to location plan; Haiburiddo arugorizumu niyoru kongo seisukeikaku mondai no tamokuteki kaiseki. haikibutsu shobunshisetsu no ricchi keikaku heno oyo

    Energy Technology Data Exchange (ETDEWEB)

    Shimizu, Yoshiaki

    1999-02-05

    This paper was concerned to rationally solve problems based on the various complicated global social environment as representative of location and arrangement problems in the wide area network, and shown to classify many of these problems into a multi-objective mixed integer planning problem. However, since the solution-obtained work was extremely large to obtain a sole exact optimum solution due to significantly increasing the solution-obtained work with a large scale of these problems, development of the solution method to emphasize a practical standpoint that an approximate solution was hopefully obtained with less effort have been paid attention. Therefore, concerning the genetic algorithm regarded as a hopeful method in recent years, problems on a usual solution-obtaining process that real variables were coded and restriction conditions were treated as a penalty function were firstly pointed out. Based on this work as a practical solution method, the combination of a problem range and the characteristics of solution methods were considered into a step construction, and a hybrid genetic algorithm using mathematical programming was proposed. As an introduction method to a parade optimum solution in a multi-objective mixed integer planning problem, this solution method was mentioned to be a practical solution method. As concrete examples, a harmful waste disposal location plan problem was given; the effectiveness was examined by numerical experiments. (translated by NEDO)

  5. 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

  6. Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

    Science.gov (United States)

    Mozaffari, Ahmad; Vajedi, Mahyar; Chehresaz, Maryyeh; Azad, Nasser L.

    2016-03-01

    The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

  7. Benchmarks for dynamic multi-objective optimisation

    CSIR Research Space (South Africa)

    Helbig, M

    2013-06-01

    Full Text Available of dynamic multi-objective optimisation algorithms (DMOAs) are highlighted. In addition, new DMOO benchmark functions with complicated Pareto-optimal sets (POSs) and approaches to develop DMOOPs with either an isolated or deceptive Pareto-optimal front (POF...

  8. Dynamic multi-objective optimization using PSO

    CSIR Research Space (South Africa)

    Helbig, M

    2012-01-01

    Full Text Available Dynamic multi-objective optimisation problems (DMOOPs) occur in many situations in the real world. These optimisation problems do not have a single goal to solve, but many goals that are in conflict with one another - improvement in one goal leads...

  9. IOT Overview: Optical Multi-Object Spectrographs

    Science.gov (United States)

    Schmidtobreick, L.; Bagnulo, S.; Jehin, E.; Marconi, G.; O'Brien, K.; Pompei, E.; Saviane, I.

    We give an introduction to the several instruments that ESO operates and which are able to perform optical multi-object spectroscopy. We point out the standard ways of reducing these spectra, the problems that occur, and the way we deal with them. A short introduction is given on how the quality control is performed.

  10. Multi-objective Transmission Planning Paper

    DEFF Research Database (Denmark)

    Xu, Zhao; Dong, Zhao Yang; Wong, Kit Po

    2009-01-01

    This paper describes a transmission expansion planning method based on multi-objective optimization (MOOP). The method starts with constructing a candidate pool of feasible expansion plans, followed by selection of the best candidates through MOOP, of which multiple objectives are tackled...... simultaneously, aiming at integrating the market operation and planning as one unified process in the market environment. Subsequently, reliability assessment is performed to evaluate and reinforce the resultant expansion plan from MOOP. The proposed method has been tested with the IEEE 14-bus system...

  11. A Multi-object Exoplanet Detecting Technique

    Science.gov (United States)

    Zhang, K.

    2011-05-01

    Exoplanet exploration is not only a meaningful astronomical action, but also has a close relation with the extra-terrestrial life. High resolution echelle spectrograph is the key instrument for measuring stellar radial velocity (RV). But with higher precision, better environmental stability and higher cost are required. An improved technique of RV means invented by David J. Erskine in 1997, External Dispersed Interferometry (EDI), can increase the RV measuring precision by combining the moderate resolution spectrograph with a fixed-delay Michelson interferometer. LAMOST with large aperture and large field of view is equipped with 16 multi-object low resolution fiber spectrographs. And these spectrographs are capable to work in medium resolution mode (R=5{K}˜10{K}). LAMOST will be one of the most powerful exoplanet detecting systems over the world by introducing EDI technique. The EDI technique is a new technique for developing astronomical instrumentation in China. The operating theory of EDI was generally verified by a feasibility experiment done in 2009. And then a multi-object exoplanet survey system based on LAMOST spectrograph was proposed. According to this project, three important tasks have been done as follows: Firstly, a simulation of EDI operating theory contains the stellar spectrum model, interferometer transmission model, spectrograph mediation model and RV solution model. In order to meet the practical situation, two detecting modes, temporal and spatial phase-stepping methods, are separately simulated. The interference spectrum is analyzed with Fourier transform algorithm and a higher resolution conventional spectrum is resolved. Secondly, an EDI prototype is composed of a multi-object interferometer prototype and the LAMOST spectrograph. Some ideas are used in the design to reduce the effect of central obscuration, for example, modular structure and external/internal adjusting frames. Another feasibility experiment was done at Xinglong Station in

  12. Multi-agent system-based event-triggered hybrid control scheme for energy internet

    DEFF Research Database (Denmark)

    Dou, Chunxia; Yue, Dong; Han, Qing Long

    2017-01-01

    This paper is concerned with an event-triggered hybrid control for the energy Internet based on a multi-agent system approach with which renewable energy resources can be fully utilized to meet load demand with high security and well dynamical quality. In the design of control, a multi-agent system...... of event-triggered hybrid control strategies whereby the multi-agent system implements the hierarchical hybrid control to achieve multiple control objectives. Finally, the effectiveness of the proposed control is validated by means of simulation results....

  13. Multi-objective community detection based on memetic algorithm

    National Research Council Canada - National Science Library

    Wu, Peng; Pan, Li

    2015-01-01

    .... In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective...

  14. Multi-object spring level sets (MUSCLE).

    Science.gov (United States)

    Lucas, Blake C; Kazhdan, Michael; Taylor, Russell H

    2012-01-01

    A new data structure is presented for geometrically modeling multi-objects. The model can exhibit elastic and fluid-like behavior to enable interpretability between tasks that require both deformable registration and active contour segmentation. The data structure consists of a label mask, distance field, and springls (a constellation of disconnected triangles). The representation has sub-voxel precision, is parametric, re-meshes, tracks point correspondences, and guarantees no self-intersections, air-gaps, or overlaps between adjacent structures. In this work, we show how to apply existing registration algorithms and active contour segmentation to the data structure; and as a demonstration, the data structure is used to segment cortical and subcortical structures (74 total) in the human brain.

  15. Model-Based Multi-Objective Reinforcement Learning

    NARCIS (Netherlands)

    Wiering, Marco; Withagen, Maikel; Drugan, Madalina

    2014-01-01

    This paper describes a novel multi-objective reinforcement learning algorithm. The proposed algorithm first learns a model of the multi-objective sequential decision making problem, after which this learned model is used by a multi-objective dynamic programming method to compute Pareto op-timal

  16. Multi-objective design of PV-wind-diesel-hydrogen-battery systems

    Energy Technology Data Exchange (ETDEWEB)

    Dufo-Lopez, Rodolfo; Bernal-Agustin, Jose L. [Department of Electrical Engineering, University of Zaragoza, Calle Maria de Luna 3, 50018-Zaragoza (Spain)

    2008-12-15

    This paper presents, for the first time, a triple multi-objective design of isolated hybrid systems minimizing, simultaneously, the total cost throughout the useful life of the installation, pollutant emissions (CO{sub 2}) and unmet load. For this task, a multi-objective evolutionary algorithm (MOEA) and a genetic algorithm (GA) have been used in order to find the best combination of components of the hybrid system and control strategies. As an example of application, a complex PV-wind-diesel-hydrogen-battery system has been designed, obtaining a set of possible solutions (Pareto Set). The results achieved demonstrate the practical utility of the developed design method. (author)

  17. Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

    OpenAIRE

    Khalili-Damghani, Kaveh; Shahrokh, Ayda; Pakgohar, Alireza

    2017-01-01

    [EN] In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSS...

  18. A Cluster-Based Orthogonal Multi-Objective Genetic Algorithm

    Science.gov (United States)

    Zhu, Jiankai; Dai, Guangming; Mo, Li

    Multi-objective genetic algorithm is proved to be suitable for solving multi-objective optimization problems. However, it is usually very hard to balance the convergence and diversity of a multi-objective genetic algorithm. This paper introduces a new algorithm, with both good convergence and diversity based on clustering method and multi-parent crossover operator. Meanwhile, an initial population is generated by orthogonal design to enhance the search effort of the algorithm. The experimental results on a number of test problems indicate the good performance of the Cluster-Based Orthogonal Multi-Objective Genetic Algorithm.

  19. Issues with performance measures for dynamic multi-objective optimisation

    CSIR Research Space (South Africa)

    Helbig, M

    2013-06-01

    Full Text Available In recent years a number of algorithms were proposed to solve dynamic multi-objective optimisation problems. However, a major problem in the field of dynamic multi-objective optimisation is a lack of standard performance measures to quantify...

  20. Analysing the performance of dynamic multi-objective optimisation algorithms

    CSIR Research Space (South Africa)

    Helbig, M

    2013-06-01

    Full Text Available Dynamic multi-objective optimisation problems (DMOOPs) have more than one objective, with at least one objective changing over time. Since at least two of the objectives are normally in conflict with one another, a single solution does not exist...

  1. Multi-objective vs. single-objective calibration of a hydrologic model using single- and multi-objective screening

    Science.gov (United States)

    Mai, Juliane; Cuntz, Matthias; Shafii, Mahyar; Zink, Matthias; Schäfer, David; Thober, Stephan; Samaniego, Luis; Tolson, Bryan

    2016-04-01

    Hydrologic models are traditionally calibrated against observed streamflow. Recent studies have shown however, that only a few global model parameters are constrained using this kind of integral signal. They can be identified using prior screening techniques. Since different objectives might constrain different parameters, it is advisable to use multiple information to calibrate those models. One common approach is to combine these multiple objectives (MO) into one single objective (SO) function and allow the use of a SO optimization algorithm. Another strategy is to consider the different objectives separately and apply a MO Pareto optimization algorithm. In this study, two major research questions will be addressed: 1) How do multi-objective calibrations compare with corresponding single-objective calibrations? 2) How much do calibration results deteriorate when the number of calibrated parameters is reduced by a prior screening technique? The hydrologic model employed in this study is a distributed hydrologic model (mHM) with 52 model parameters, i.e. transfer coefficients. The model uses grid cells as a primary hydrologic unit, and accounts for processes like snow accumulation and melting, soil moisture dynamics, infiltration, surface runoff, evapotranspiration, subsurface storage and discharge generation. The model is applied in three distinct catchments over Europe. The SO calibrations are performed using the Dynamically Dimensioned Search (DDS) algorithm with a fixed budget while the MO calibrations are achieved using the Pareto Dynamically Dimensioned Search (PA-DDS) algorithm allowing for the same budget. The two objectives used here are the Nash Sutcliffe Efficiency (NSE) of the simulated streamflow and the NSE of the logarithmic transformation. It is shown that the SO DDS results are located close to the edges of the Pareto fronts of the PA-DDS. The MO calibrations are hence preferable due to their supply of multiple equivalent solutions from which the

  2. Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

    Science.gov (United States)

    2010-03-01

    17 NSGA-II non-dominated sorting genetic algorithm II . . . . . . . . . . . . . . . . . . . 17 jMetal Metaheuristic Algorithms in...of metaheuristics for multi-objective problems. It provides a rich and robust set of object classes which im- plement 13 different evolutionary...Dorronsoro, and En- rique Alba. jMetal: A Java Framework for Developing Multi-Objective Optimiza- tion Metaheuristics . Technical Report ITI-2006-10

  3. Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

    Directory of Open Access Journals (Sweden)

    Kaveh Khalili-Damghani

    2017-07-01

    Full Text Available In this paper a multi-period multi-product multi-objective aggregate production planning (APP model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.

  4. Haptic Manipulation of Deformable Objects in Hybrid Bilateral Teleoperation System

    Directory of Open Access Journals (Sweden)

    Juan Manuel Ibarra-Zannatha

    2007-01-01

    Full Text Available The aim of this work is the integration of a virtual environment containing a deformable object, manipulated by an open kinematical chain virtual slave robot, to a bilateral teleoperation scheme based on a real haptic device. The virtual environment of this hybrid bilateral teleoperation system combines collision detection algorithms, dynamical, kinematical and geometrical models with a position–position and/or force–position bilateral control algorithm, to produce on the operator side the reflected forces corresponding to the virtual mechanical interactions, through a haptic device. Contact teleoperation task over the virtual environment with a flexible object is implemented and analysed.

  5. Developer Tools for Evaluating Multi-Objective Algorithms

    Science.gov (United States)

    Giuliano, Mark E.; Johnston, Mark D.

    2011-01-01

    Multi-objective algorithms for scheduling offer many advantages over the more conventional single objective approach. By keeping user objectives separate instead of combined, more information is available to the end user to make trade-offs between competing objectives. Unlike single objective algorithms, which produce a single solution, multi-objective algorithms produce a set of solutions, called a Pareto surface, where no solution is strictly dominated by another solution for all objectives. From the end-user perspective a Pareto-surface provides a tool for reasoning about trade-offs between competing objectives. From the perspective of a software developer multi-objective algorithms provide an additional challenge. How can you tell if one multi-objective algorithm is better than another? This paper presents formal and visual tools for evaluating multi-objective algorithms and shows how the developer process of selecting an algorithm parallels the end-user process of selecting a solution for execution out of the Pareto-Surface.

  6. Power magnetic devices a multi-objective design approach

    CERN Document Server

    Sudhoff, Scott D

    2014-01-01

    Presents a multi-objective design approach to the many power magnetic devices in use today Power Magnetic Devices: A Multi-Objective Design Approach addresses the design of power magnetic devices-including inductors, transformers, electromagnets, and rotating electric machinery-using a structured design approach based on formal single- and multi-objective optimization. The book opens with a discussion of evolutionary-computing-based optimization. Magnetic analysis techniques useful to the design of all the devices considered in the book are then set forth. This material is then used for ind

  7. Multi-objective optimization in graphical models

    OpenAIRE

    Rollón, Emma

    2008-01-01

    Many real-life optimization problems are combinatorial, i.e. they concern a choice of the best solution from a finite but exponentially large set of alternatives. Besides, the solution quality of many of these problems can often be evaluated from several points of view (a.k.a. criteria). In that case, each criterion may be described by a different objective function. Some important and well-known multicriteria scenarios are: · In investment optimization one wants to minimize risk ...

  8. Multiple utility constrained multi-objective programs using Bayesian theory

    Science.gov (United States)

    Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed

    2017-06-01

    A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.

  9. Multi-Label Object Categorization Using Histograms of Global Relations

    DEFF Research Database (Denmark)

    Mustafa, Wail; Xiong, Hanchen; Kraft, Dirk

    2015-01-01

    In this paper, we present an object categorization system capable of assigning multiple and related categories for novel objects using multi-label learning. In this system, objects are described using global geometric relations of 3D features. We propose using the Joint SVM method for learning...

  10. Integrative systems modeling and multi-objective optimization

    Science.gov (United States)

    This presentation presents a number of algorithms, tools, and methods for utilizing multi-objective optimization within integrated systems modeling frameworks. We first present innovative methods using a genetic algorithm to optimally calibrate the VELMA and SWAT ecohydrological ...

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

    National Research Council Canada - National Science Library

    Kavousi-Fard, Abdollah; Khodaei, Amin

    2016-01-01

    .... 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...

  12. Solving Molecular Docking Problems with Multi-Objective Metaheuristics

    Directory of Open Access Journals (Sweden)

    María Jesús García-Godoy

    2015-06-01

    Full Text Available Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this paper, we use and compare, for the first time, a set of representative multi-objective optimization algorithms applied to solve complex molecular docking problems. The approach followed is focused on optimizing the intermolecular and intramolecular energies as two main objectives to minimize. Specifically, these algorithms are: two variants of the non-dominated sorting genetic algorithm II (NSGA-II, speed modulation multi-objective particle swarm optimization (SMPSO, third evolution step of generalized differential evolution (GDE3, multi-objective evolutionary algorithm based on decomposition (MOEA/D and S-metric evolutionary multi-objective optimization (SMS-EMOA. We assess the performance of the algorithms by applying quality indicators intended to measure convergence and the diversity of the generated Pareto front approximations. We carry out a comparison with another reference mono-objective algorithm in the problem domain (Lamarckian genetic algorithm (LGA provided by the AutoDock tool. Furthermore, the ligand binding site and molecular interactions of computed solutions are analyzed, showing promising results for the multi-objective approaches. In addition, a case study of application for aeroplysinin-1 is performed, showing the effectiveness of our multi-objective approach in drug discovery.

  13. Solving molecular docking problems with multi-objective metaheuristics.

    Science.gov (United States)

    García-Godoy, María Jesús; López-Camacho, Esteban; García-Nieto, José; Aldana-Montes, Antonio J Nebroand José F

    2015-06-02

    Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this paper, we use and compare, for the first time, a set of representative multi-objective optimization algorithms applied to solve complex molecular docking problems. The approach followed is focused on optimizing the intermolecular and intramolecular energies as two main objectives to minimize. Specifically, these algorithms are: two variants of the non-dominated sorting genetic algorithm II (NSGA-II), speed modulation multi-objective particle swarm optimization (SMPSO), third evolution step of generalized differential evolution (GDE3), multi-objective evolutionary algorithm based on decomposition (MOEA/D) and S-metric evolutionary multi-objective optimization (SMS-EMOA). We assess the performance of the algorithms by applying quality indicators intended to measure convergence and the diversity of the generated Pareto front approximations. We carry out a comparison with another reference mono-objective algorithm in the problem domain (Lamarckian genetic algorithm (LGA) provided by the AutoDock tool). Furthermore, the ligand binding site and molecular interactions of computed solutions are analyzed, showing promising results for the multi-objective approaches. In addition, a case study of application for aeroplysinin-1 is performed, showing the effectiveness of our multi-objective approach in drug discovery.

  14. Genetic algorithm for multi-objective experimental optimization

    OpenAIRE

    Link, Hannes; Weuster-Botz, Dirk

    2006-01-01

    A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental eff...

  15. Genetic algorithm for multi-objective experimental optimization

    Science.gov (United States)

    Link, Hannes

    2006-01-01

    A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations). PMID:17048033

  16. 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

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

    Science.gov (United States)

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

    2015-07-20

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

  18. Wireless Sensor Network Optimization: Multi-Objective Paradigm

    Directory of Open Access Journals (Sweden)

    Muhammad Iqbal

    2015-07-01

    Full Text Available 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.

  19. Multi-objective nested algorithms for optimal reservoir operation

    Science.gov (United States)

    Delipetrev, Blagoj; Solomatine, Dimitri

    2016-04-01

    The optimal reservoir operation is in general a multi-objective problem, meaning that multiple objectives are to be considered at the same time. For solving multi-objective optimization problems there exist a large number of optimization algorithms - which result in a generation of a Pareto set of optimal solutions (typically containing a large number of them), or more precisely, its approximation. At the same time, due to the complexity and computational costs of solving full-fledge multi-objective optimization problems some authors use a simplified approach which is generically called "scalarization". Scalarization transforms the multi-objective optimization problem to a single-objective optimization problem (or several of them), for example by (a) single objective aggregated weighted functions, or (b) formulating some objectives as constraints. We are using the approach (a). A user can decide how many multi-objective single search solutions will generate, depending on the practical problem at hand and by choosing a particular number of the weight vectors that are used to weigh the objectives. It is not guaranteed that these solutions are Pareto optimal, but they can be treated as a reasonably good and practically useful approximation of a Pareto set, albeit small. It has to be mentioned that the weighted-sum approach has its known shortcomings because the linear scalar weights will fail to find Pareto-optimal policies that lie in the concave region of the Pareto front. In this context the considered approach is implemented as follows: there are m sets of weights {w1i, …wni} (i starts from 1 to m), and n objectives applied to single objective aggregated weighted sum functions of nested dynamic programming (nDP), nested stochastic dynamic programming (nSDP) and nested reinforcement learning (nRL). By employing the multi-objective optimization by a sequence of single-objective optimization searches approach, these algorithms acquire the multi-objective properties

  20. A multi-objective decision framework for lifecycle investment

    NARCIS (Netherlands)

    Timmermans, S.H.J.T.; Schumacher, J.M.; Ponds, E.H.M.

    2017-01-01

    In this paper we propose a multi-objective decision framework for lifecycle investment choice. Instead of optimizing individual strategies with respect to a single-valued objective, we suggest evaluation of classes of strategies in terms of the quality of the tradeoffs that they provide. The

  1. Multi-objective optimization in the construction industry

    NARCIS (Netherlands)

    Sariyildiz, I.S.; Bittermann, M.S.; Ciftcioglu, O.

    2008-01-01

    Multi-objective-optimization-based positioning of houses in a residential neighborhood is described. The task is the placement of the buildings in a favorable configuration constrained by two objectives, which are garden performance and visual privacy performance requirements. The method used is

  2. A Multi-objective Model for Transmission Planning Under Uncertainties

    DEFF Research Database (Denmark)

    Zhang, Chunyu; Wang, Qi; Ding, Yi

    2014-01-01

    .e. the cost of power purchase and network expansion, and the revenue of power delivery. A two-phase multi-objective PSO (MOPSO) algorithm is employed to be the solver. The feasibility of the proposed multi-objective planning approach has been verified by the 77-bus system linked with 38-bus distribution......The significant growth of distributed energy resources (DERs) associated with smart grid technologies has prompted excessive uncertainties in the transmission system. The most representative is the novel notation of commercial aggregator who has lighted a bright way for DERs to participate power...... trading and regulating in transmission level. In this paper, the aggregator caused uncertainty is analyzed first considering DERs’ correlation. During the transmission planning, a scenario-based multi-objective transmission planning (MOTP) framework is proposed to simultaneously optimize two objectives, i...

  3. 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...

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

    Directory of Open Access Journals (Sweden)

    João Soares

    2016-10-01

    Full Text Available 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-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods’ computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus. It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs, several distributed generation (DG, customers with demand response (DR contracts and energy storage systems (ESS. The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality.

  5. A hybrid genetic algorithm for solving bi-objective traveling salesman problems

    Science.gov (United States)

    Ma, Mei; Li, Hecheng

    2017-08-01

    The traveling salesman problem (TSP) is a typical combinatorial optimization problem, in a traditional TSP only tour distance is taken as a unique objective to be minimized. When more than one optimization objective arises, the problem is known as a multi-objective TSP. In the present paper, a bi-objective traveling salesman problem (BOTSP) is taken into account, where both the distance and the cost are taken as optimization objectives. In order to efficiently solve the problem, a hybrid genetic algorithm is proposed. Firstly, two satisfaction degree indices are provided for each edge by considering the influences of the distance and the cost weight. The first satisfaction degree is used to select edges in a “rough” way, while the second satisfaction degree is executed for a more “refined” choice. Secondly, two satisfaction degrees are also applied to generate new individuals in the iteration process. Finally, based on genetic algorithm framework as well as 2-opt selection strategy, a hybrid genetic algorithm is proposed. The simulation illustrates the efficiency of the proposed algorithm.

  6. 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.

  7. A multi-objective method for solving assembly line balancing problem

    Directory of Open Access Journals (Sweden)

    Hadi Pazoki Toroudi

    2017-01-01

    Full Text Available Modeling the simple assembly line balancing (SALB problem has covered a wide range of real-world applications. The recent advances in optimization problems have created the opportunities to tackle more challenging problems. This paper presents a multi-objective decision making problem to consider two objectives, cost and cycle time, for simple assembly line balancing. The problem is formulated as a mixed integer nonlinear optimization and the proposed study of this paper uses two metaheuristics to solve the resulted problem on some benchmark problems. The preliminary results have indicated that multi objective particle swarm optimization (MOPSO has provided better quality solutions while the hybrid method based on MOPSO and simulated annealing has yielded more non-dominated Pareto solutions.

  8. 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.

  9. A Hybrid Multi-Robot Control Architecture

    Science.gov (United States)

    2007-12-01

    Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command In Partial...and David Kinny. “The Gaia Method- olog for Agent-Oriented Analysis and Design”. Autonomous Agents and Multi- Agent Systems, 3(3):285–312, 2000. 56...Autonomous and Adaptive Systems. 101 Vita Daylond J. Hooper graduated from Fairborn High School in Fairborn, Ohio. He enlisted in the Army in July 1998

  10. 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 ...

  11. Conditional Random Field (CRF-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning

    Directory of Open Access Journals (Sweden)

    Ehwa Yang

    2017-03-01

    Full Text Available Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT. In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is how to associate noisy object detection results on a new frame with previously being tracked objects. In this work, we propose a multi-object tracker method called CRF-boosting which utilizes a hybrid data association method based on online hybrid boosting facilitated by a conditional random field (CRF for establishing online MOT. For data association, learned CRF is used to generate reliable low-level tracklets and then these are used as the input of the hybrid boosting. To do so, while existing data association methods based on boosting algorithms have the necessity of training data having ground truth information to improve robustness, CRF-boosting ensures sufficient robustness without such information due to the synergetic cascaded learning procedure. Further, a hierarchical feature association framework is adopted to further improve MOT accuracy. From experimental results on public datasets, we could conclude that the benefit of proposed hybrid approach compared to the other competitive MOT systems is noticeable.

  12. Optimization Modelling for Multi-Objective Supply Chains, A Case ...

    African Journals Online (AJOL)

    ... for Multi-Objective Supply Chains, A Case Study of the Oil and Gas Sector. ... AFRREV STECH: An International Journal of Science and Technology ... Alternatively, you can download the PDF file directly to your computer, from where it can ...

  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. Comparison of multi-objective evolutionary approaches for task ...

    Indian Academy of Sciences (India)

    ... solutions for the considered multi-objective scheduling problem. The algorithms are validated against a set of benchmark instances and the performance of the algorithms evaluated using standard metrics. Experimental results and performance measures infer that NSGA-II produces quality schedules compared to NSPSO ...

  15. Multi-objective evolutionary optimisation for product design and manufacturing

    CERN Document Server

    2011-01-01

    Presents state-of-the-art research in the area of multi-objective evolutionary optimisation for integrated product design and manufacturing Provides a comprehensive review of the literature Gives in-depth descriptions of recently developed innovative and novel methodologies, algorithms and systems in the area of modelling, simulation and optimisation

  16. Multi-objective Uniform-diversity Genetic Algorithm (MUGA)

    OpenAIRE

    Jamali, Ali; Nariman-zadeh, Nader; Atashkari, Kazem

    2008-01-01

    A new multi-objective uniform-diversity genetic algorithm (MUGA) has been proposed and successfully used for some test functions and for thermodynamic cycle optimization of ideal turbojet engines. It has been shown that the performance of this algorithm is superior to that

  17. Navigation Constellation Design Using a Multi-Objective Genetic Algorithm

    Science.gov (United States)

    2015-03-26

    to validate their success. The FUEGO constellation is a constellation of small satellites that detect forest fires from LEO. The first algorithm... Urbana -Champaign, 2001. 26. N. Srinivas and K. Deb, "Multi-Objective Optimization Using Non-Dominating Sorting in Genetic Algorithms," Evolutionary

  18. Multi-objective optimisation with stochastic discrete-event simulation ...

    African Journals Online (AJOL)

    The cash management of an autoteller machine (ATM) is a multi-objective optimisation problem which aims to maximise the service level provided to customers at minimum cost. This paper focus on improved cash management in a section of the South African retail banking industry, for which a decision support system ...

  19. Multi-Modal Inference in Animacy Perception for Artificial Object

    Directory of Open Access Journals (Sweden)

    Kohske Takahashi

    2011-10-01

    Full Text Available Sometimes we feel animacy for artificial objects and their motion. Animals usually interact with environments through multiple sensory modalities. Here we investigated how the sensory responsiveness of artificial objects to the environment would contribute to animacy judgment for them. In a 90-s trial, observers freely viewed four objects moving in a virtual 3D space. The objects, whose position and motion were determined following Perlin-noise series, kept drifting independently in the space. Visual flashes, auditory bursts, or synchronous flashes and bursts appeared with 1–2 s intervals. The first object abruptly accelerated their motion just after visual flashes, giving an impression of responding to the flash. The second object responded to bursts. The third object responded to synchronous flashes and bursts. The forth object accelerated at a random timing independent of flashes and bursts. The observers rated how strongly they felt animacy for each object. The results showed that the object responding to the auditory bursts was rated as having weaker animacy compared to the other objects. This implies that sensory modality through which an object interacts with the environment may be a factor for animacy perception in the object and may serve as the basis of multi-modal and cross-modal inference of animacy.

  20. Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

    Science.gov (United States)

    Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

    2017-03-21

    For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

  1. A Bayesian alternative for multi-objective ecohydrological model specification

    Science.gov (United States)

    Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

    2018-01-01

    Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

  2. MOPSO-based multi-objective TSO planning considering uncertainties

    DEFF Research Database (Denmark)

    Wang, Qi; Zhang, Chunyu; Ding, Yi

    2014-01-01

    The concerns of sustainability and climate change have posed a significant growth of renewable energy associated with smart grid technologies. Various uncertainties are the major problems need to be handled by transmission system operator (TSO) planning. This paper mainly focuses on three uncertain...... factors, i.e. load growth, generation capacity and line faults, and aims to enhance the transmission system via the multi-objective TSO planning (MOTP) approach. The proposed MOTP approach optimizes three objectives simultaneously, namely the probabilistic available transfer capability (PATC), investment...... cost and power outage cost. A two-phase MOPSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity ofPareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach...

  3. A versatile multi-objective FLUKA optimization using Genetic Algorithms

    Science.gov (United States)

    Vlachoudis, Vasilis; Antoniucci, Guido Arnau; Mathot, Serge; Kozlowska, Wioletta Sandra; Vretenar, Maurizio

    2017-09-01

    Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and space available, or in the medical field optimizing the dose delivered to a patient under a hadron treatment. The present paper describes our implementation inside flair[1] the advanced user interface of FLUKA[2,3] of a multi-objective Genetic Algorithm[Erreur ! Source du renvoi introuvable.] to facilitate the search for the optimum solution.

  4. A versatile multi-objective FLUKA optimization using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Vlachoudis Vasilis

    2017-01-01

    Full Text Available Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and space available, or in the medical field optimizing the dose delivered to a patient under a hadron treatment. The present paper describes our implementation inside flair[1] the advanced user interface of FLUKA[2,3] of a multi-objective Genetic Algorithm[Erreur ! Source du renvoi introuvable.] to facilitate the search for the optimum solution.

  5. Multi-objective particle swarm optimization using Pareto-based set and aggregation approach

    Science.gov (United States)

    Huang, Song; Wang, Yan; Ji, Zhicheng

    2017-07-01

    Multi-objective optimization problems (MOPs) need to be solved in real world recently. In this paper, a multi-objective particle swarm optimization based on Pareto set and aggregation approach was proposed to deal with MOPs. Firstly, velocities and positions were updated similar to PSO. Then, global-best set was defined in particle swarm optimizer to preserve Pareto-based set obtained by the population. Specifically, a hybrid updating strategy based on Pareto set and aggregation approach was introduced to update the global-best set and local search was carried on global-best set. Thirdly, personal-best positions were updated in decomposition way, and global-best position was selected from global-best set. Finally, ZDT instances and DTLZ instances were selected to evaluate the performance of MULPSO and the results show validity of the proposed algorithm for MOPs.

  6. Multi-objective swarm intelligence theoretical advances and applications

    CERN Document Server

    Jagadev, Alok; Panda, Mrutyunjaya

    2015-01-01

    The aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.       

  7. A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

    Directory of Open Access Journals (Sweden)

    Su YAN

    2017-06-01

    Full Text Available Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO, the network-wide 4D flight trajectories planning (N4DFTP problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs (3D position and time for all the flights in the whole airway network. Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity, an efficient model for strategic-level conflict management is developed in this paper. Specifically, a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated. In consideration of the large-scale, high-complexity, and multi-objective characteristics of the N4DFTP problem, a multi-objective multi-memetic algorithm (MOMMA that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented. It is capable of rapidly and effectively allocating 4DTs via rerouting, target time controlling, and flight level changing. Additionally, to balance the ability of exploitation and exploration of the algorithm, a special hybridization scheme is adopted for the integration of local and global search. Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4DFTP problem. The solutions achieved are competitive for elaborate decision support under a TBO environment.

  8. Multi-objective community detection based on memetic algorithm.

    Directory of Open Access Journals (Sweden)

    Peng Wu

    Full Text Available Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

  9. Object recognition through a multi-mode fiber

    Science.gov (United States)

    Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

    2017-04-01

    We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

  10. Joint Conditional Random Field Filter for Multi-Object Tracking

    Directory of Open Access Journals (Sweden)

    Luo Ronghua

    2011-03-01

    Full Text Available Object tracking can improve the performance of mobile robot especially in populated dynamic environments. A novel joint conditional random field Filter (JCRFF based on conditional random field with hierarchical structure is proposed for multi-object tracking by abstracting the data associations between objects and measurements to be a sequence of labels. Since the conditional random field makes no assumptions about the dependency structure between the observations and it allows non-local dependencies between the state and the observations, the proposed method can not only fuse multiple cues including shape information and motion information to improve the stability of tracking, but also integrate moving object detection and object tracking quite well. At the same time, implementation of multi-object tracking based on JCRFF with measurements from the laser range finder on a mobile robot is studied. Experimental results with the mobile robot developed in our lab show that the proposed method has higher precision and better stability than joint probabilities data association filter (JPDAF.

  11. High performance pseudo-analytical simulation of multi-object adaptive optics over multi-GPU systems

    KAUST Repository

    Abdelfattah, Ahmad

    2014-01-01

    Multi-object adaptive optics (MOAO) is a novel adaptive optics (AO) technique dedicated to the special case of wide-field multi-object spectrographs (MOS). It applies dedicated wavefront corrections to numerous independent tiny patches spread over a large field of view (FOV). The control of each deformable mirror (DM) is done individually using a tomographic reconstruction of the phase based on measurements from a number of wavefront sensors (WFS) pointing at natural and artificial guide stars in the field. The output of this study helps the design of a new instrument called MOSAIC, a multi-object spectrograph proposed for the European Extremely Large Telescope (E-ELT). We have developed a novel hybrid pseudo-analytical simulation scheme that allows us to accurately simulate in detail the tomographic problem. The main challenge resides in the computation of the tomographic reconstructor, which involves pseudo-inversion of a large dense symmetric matrix. The pseudo-inverse is computed using an eigenvalue decomposition, based on the divide and conquer algorithm, on multicore systems with multi-GPUs. Thanks to a new symmetric matrix-vector product (SYMV) multi-GPU kernel, our overall implementation scores significant speedups over standard numerical libraries on multicore, like Intel MKL, and up to 60% speedups over the standard MAGMA implementation on 8 Kepler K20c GPUs. At 40,000 unknowns, this appears to be the largest-scale tomographic AO matrix solver submitted to computation, to date, to our knowledge and opens new research directions for extreme scale AO simulations. © 2014 Springer International Publishing Switzerland.

  12. Multi-Objective Weather Routing of Sailing Vessels

    Directory of Open Access Journals (Sweden)

    Życzkowski Marcin

    2017-12-01

    Full Text Available The paper presents a multi-objective deterministic method of weather routing for sailing vessels. Depending on a particular purpose of sailboat weather routing, the presented method makes it possible to customize the criteria and constraints so as to fit a particular user’s needs. Apart from a typical shortest time criterion, safety and comfort can also be taken into account. Additionally, the method supports dynamic weather data: in its present version short-term, mid-term and long-term term weather forecasts are used during optimization process. In the paper the multi-objective optimization problem is first defined and analysed. Following this, the proposed method solving this problem is described in detail. The method has been implemented as an online SailAssistance application. Some representative examples solutions are presented, emphasizing the effects of applying different criteria or different values of customized parameters.

  13. 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.

  14. 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.

  15. Evolutionary Image Enhancement Using Multi-Objective Genetic Algorithm

    OpenAIRE

    Dhirendra Pal Singh; Ashish Khare

    2013-01-01

    Image Processing is the art of examining, identifying and judging the significances of the Images. Image enhancement refers to attenuation, or sharpening, of image features such as edgels, boundaries, or contrast to make the processed image more useful for analysis. Image enhancement procedures utilize the computers to provide good and improved images for study by the human interpreters. In this paper we proposed a novel method that uses the Genetic Algorithm with Multi-objective criteria to ...

  16. Multi-objective genetic algorithm for pseudoknotted RNA sequence design

    OpenAIRE

    Akito eTaneda

    2012-01-01

    RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective genetic algorithm (MOGA), MODENA, which we have previously proposed for pseudoknot-free RNA inver...

  17. MOPSO-based multi-objective TSO planning considering uncertainties

    OpenAIRE

    Wang, Qi; Zhang, Chunyu; Ding, Yi; Østergaard, Jacob

    2014-01-01

    The concerns of sustainability and climate change have posed a significant growth of renewable energy associated with smart grid technologies. Various uncertainties are the major problems need to be handled by transmission system operator (TSO) planning. This paper mainly focuses on three uncertain factors, i.e. load growth, generation capacity and line faults, and aims to enhance the transmission system via the multi-objective TSO planning (MOTP) approach. The proposed MOTP approach optimize...

  18. Multi-camera 3D Object Reconstruction for Industrial Automation

    OpenAIRE

    Bitzidou, Malamati; Chrysostomou, Dimitrios; Gasteratos, Antonios

    2012-01-01

    Part 2: Design, Manufacturing and Production Management; International audience; In this paper, a method to automate industrial manufacturing processes using an intelligent multi-camera system to assist a robotic arm on a production line is presented. The examined assembly procedure employs a volumetric method for the initial estimation of object’s properties and an octree decomposition process to generate the path plans for the robotic arm. Initially, the object is captured by four cameras a...

  19. Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm

    Science.gov (United States)

    Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.

    2017-02-01

    Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.

  20. A Novel Multi-Objective Self-Organizing Migrating Algorithm

    Directory of Open Access Journals (Sweden)

    P. Kadlec

    2011-12-01

    Full Text Available In the paper, a novel stochastic Multi-Objective Self Organizing Migrating Algorithm (MOSOMA is introduced. For the search of optima, MOSOMA employs a migration technique used in a single-objective Self Organizing Migrating Algorithm (SOMA. In order to obtain a uniform distribution of Pareto optimal solutions, a novel technique considering Euclidian distances among solutions is introduced. MOSOMA performance was tested on benchmark problems and selected electromagnetic structures. MOSOMA performance was compared with the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II and the Strength Pareto Evolutionary Algorithm 2 (SPEA2. MOSOMA excels in the uniform distribution of solutions and their completeness.

  1. Optical cryptography with biometrics for multi-depth objects.

    Science.gov (United States)

    Yan, Aimin; Wei, Yang; Hu, Zhijuan; Zhang, Jingtao; Tsang, Peter Wai Ming; Poon, Ting-Chung

    2017-10-11

    We propose an optical cryptosystem for encrypting images of multi-depth objects based on the combination of optical heterodyne technique and fingerprint keys. Optical heterodyning requires two optical beams to be mixed. For encryption, each optical beam is modulated by an optical mask containing either the fingerprint of the person who is sending, or receiving the image. The pair of optical masks are taken as the encryption keys. Subsequently, the two beams are used to scan over a multi-depth 3-D object to obtain an encrypted hologram. During the decryption process, each sectional image of the 3-D object is recovered by convolving its encrypted hologram (through numerical computation) with the encrypted hologram of a pinhole image that is positioned at the same depth as the sectional image. Our proposed method has three major advantages. First, the lost-key situation can be avoided with the use of fingerprints as the encryption keys. Second, the method can be applied to encrypt 3-D images for subsequent decrypted sectional images. Third, since optical heterodyning scanning is employed to encrypt a 3-D object, the optical system is incoherent, resulting in negligible amount of speckle noise upon decryption. To the best of our knowledge, this is the first time optical cryptography of 3-D object images has been demonstrated in an incoherent optical system with biometric keys.

  2. Estimation of subsurface geomodels by multi-objective stochastic optimization

    Science.gov (United States)

    Emami Niri, Mohammad; Lumley, David E.

    2016-06-01

    We present a new method to estimate subsurface geomodels using a multi-objective stochastic search technique that allows a variety of direct and indirect measurements to simultaneously constrain the earth model. Inherent uncertainties and noise in real data measurements may result in conflicting geological and geophysical datasets for a given area; a realistic earth model can then only be produced by combining the datasets in a defined optimal manner. One approach to solving this problem is by joint inversion of the various geological and/or geophysical datasets, and estimating an optimal model by optimizing a weighted linear combination of several separate objective functions which compare simulated and observed datasets. In the present work, we consider the joint inversion of multiple datasets for geomodel estimation, as a multi-objective optimization problem in which separate objective functions for each subset of the observed data are defined, followed by an unweighted simultaneous stochastic optimization to find the set of best compromise model solutions that fits the defined objectives, along the so-called ;Pareto front;. We demonstrate that geostatistically constrained initializations of the algorithm improves convergence speed and produces superior geomodel solutions. We apply our method to a 3D reservoir lithofacies model estimation problem which is constrained by a set of geological and geophysical data measurements and attributes, and assess the sensitivity of the resulting geomodels to changes in the parameters of the stochastic optimization algorithm and the presence of realistic seismic noise conditions.

  3. Developing Automatic Multi-Objective Optimization Methods for Complex Actuators

    Directory of Open Access Journals (Sweden)

    CHIS, R.

    2017-11-01

    Full Text Available This paper presents the analysis and multiobjective optimization of a magnetic actuator. By varying just 8 parameters of the magnetic actuator’s model the design space grows to more than 6 million configurations. Much more, the 8 objectives that must be optimized are conflicting and generate a huge objectives space, too. To cope with this complexity, we use advanced heuristic methods for Automatic Design Space Exploration. FADSE tool is one Automatic Design Space Exploration framework including different state of the art multi-objective meta-heuristics for solving NP-hard problems, which we used for the analysis and optimization of the COMSOL and MATLAB model of the magnetic actuator. We show that using a state of the art genetic multi-objective algorithm, response surface modelling methods and some machine learning techniques, the timing complexity of the design space exploration can be reduced, while still taking into consideration objective constraints so that various Pareto optimal configurations can be found. Using our developed approach, we were able to decrease the simulation time by at least a factor of 10, compared to a run that does all the simulations, while keeping prediction errors to around 1%.

  4. Multi-objective optimization of biomass to biomethane system

    Directory of Open Access Journals (Sweden)

    Nana Yan

    2016-07-01

    Full Text Available The superstructure optimization of biomass to biomethane system through digestion is conducted in this work. The system encompasses biofeedstock collection and transportation, anaerobic digestion, biogas upgrading, and digestate recycling. We propose a multicriteria mixed integer nonlinear programming (MINLP model that seeks to minimize the energy consumption and maximize the green degree and the biomethane production constrained by technology selection, mass balance, energy balance, and environmental impact. A multi-objective MINLP model is proposed and solved with a fast nondominated sorting genetic algorithm II (NSGA-II. The resulting Pareto-optimal surface reveals the trade-off among the conflicting objectives. The optimal results indicate quantitatively that higher green degree and biomethane production objectives can be obtained at the expense of destroying the performance of the energy consumption objective. Keywords: Multiobjective optimization, Biomass to biomethane system, Green degree, Mixed-integer nonlinear programming

  5. Multi-temporal land use classification using hybrid approach

    Directory of Open Access Journals (Sweden)

    Lakshmi N. Kantakumar

    2015-12-01

    Full Text Available Land use and land cover (LULC classification of a satellite image is one of the prerequisites and plays an indispensable role in many land use inventories and environmental modeling. Many studies viz., forest inventories, hydrology and biodiversity studies, etc., are in demand to account the dynamics of land use and phenology of vegetation. Multi-temporal land use classification accounts the phenology of vegetation and land use dynamics of the study area. In this study, a hybrid classification scheme was developed to prepare a multi-temporal land use classification data set of Sawantwadi taluka of Maharashtra state in India. Parametric classification methods like maximum likelihood and ISODATA clustering methods are combined with the non-parametric decision tree approach to generate the multi-temporal LULC dataset. The accuracy assessment results have shown very promising results with a 93% overall accuracy with a kappa of 0.92.

  6. Multi-Objective and Multi-Constrained Non-Additive Shortest Path Problems

    DEFF Research Database (Denmark)

    Reinhardt, Line Blander; Pisinger, David

    of this paper is to give a general framework for dominance tests for problems involving a number of non-additive criteria. These dominance tests can help eliminate paths in a dynamic programming framework when using multiple objectives. Results on real-life multi-objective problems containing non...

  7. Multi-Objective and Multi-Constrained Non-Additive Shortest Path Problems

    DEFF Research Database (Denmark)

    Reinhardt, Line Blander; Pisinger, David

    2011-01-01

    of this paper is to give a general framework for dominance tests for problems involving a number of non-additive criteria. These dominance tests can help to eliminate paths in a dynamic programming framework when using multiple objectives. Results on real-life multi-objective problems containing non...

  8. Multi-objective optimization of composite structures. A review

    Science.gov (United States)

    Teters, G. A.; Kregers, A. F.

    1996-05-01

    Studies performed on the optimization of composite structures by coworkers of the Institute of Polymers Mechanics of the Latvian Academy of Sciences in recent years are reviewed. The possibility of controlling the geometry and anisotropy of laminar composite structures will make it possible to design articles that best satisfy the requirements established for them. Conflicting requirements such as maximum bearing capacity, minimum weight and/or cost, prescribed thermal conductivity and thermal expansion, etc. usually exist for optimal design. This results in the multi-objective compromise optimization of structures. Numerical methods have been developed for solution of problems of multi-objective optimization of composite structures; parameters of the structure of the reinforcement and the geometry of the design are assigned as controlling parameters. Programs designed to run on personal computers have been compiled for multi-objective optimization of the properties of composite materials, plates, and shells. Solutions are obtained for both linear and nonlinear models. The programs make it possible to establish the Pareto compromise region and special multicriterial solutions. The problem of the multi-objective optimization of the elastic moduli of a spatially reinforced fiberglass with stochastic stiffness parameters has been solved. The region of permissible solutions and the Pareto region have been found for the elastic moduli. The dimensions of the scatter ellipse have been determined for a multidimensional Gaussian probability distribution where correlation between the composite's properties being optimized are accounted for. Two types of problems involving the optimization of a laminar rectangular composite plate are considered: the plate is considered elastic and anisotropic in the first case, and viscoelastic properties are accounted for in the second. The angle of reinforcement and the relative amount of fibers in the longitudinal direction are controlling

  9. Multi-image semi-global matching in object space

    Science.gov (United States)

    Bethmann, F.; Luhmann, T.

    2015-05-01

    Semi-Global Matching (SGM) is a widespread algorithm for image matching which is used for very different applications, ranging from real-time applications (e.g. for generating 3D data for driver assistance systems) to aerial image matching. Originally developed for stereo-image matching, several extensions have been proposed to use more than two images within the matching process (multi-baseline matching, multi-view stereo). These extensions still perform the image matching in (rectified) stereo images and combine the pairwise results afterwards to create the final solution. This paper proposes an alternative approach which is suitable for the introduction of an arbitrary number of images into the matching process and utilizes image matching by using non-rectified images. The new method differs from the original SGM method mainly in two aspects: Firstly, the cost calculation is formulated in object space within a dense voxel raster by using the grey (or colour) values of all images instead of pairwise cost calculation in image space. Secondly, the semi-global (path-wise) minimization process is transferred into object space as well, so that the result of semi-global optimization leads to index maps (instead of disparity maps) which directly indicate the 3D positions of the best matches. Altogether, this yields to an essential simplification of the matching process compared to multi-view stereo (MVS) approaches. After a description of the new method, results achieved from two different datasets (close-range and aerial) are presented and discussed.

  10. Multi-equipment condition based maintenance optimization by multi- objective genetic algorithm

    OpenAIRE

    Valcuha Štefan; Goti Aitor; Úradnícek Juraj; Navarro Ivan

    2011-01-01

    Purpose: This paper deals with the optimization of the condition based maintenance (CBM) applied on manufacturing multi-equipment system under cost and benefit criteria.Design/methodology/approach: The system is modeled using Discrete Event Simulation (DES) and optimized by means of the application of a Multi-Objective Evolutionary Algorithm (MOEA).Findings: Solution for the joint optimization of the condition based maintenance model applied on several equipment has been obtained.Research li...

  11. Multi-criteria assessment of hybrid renewable energy systems for Nigeria's coastline communities

    National Research Council Canada - National Science Library

    E O Diemuodeke; S Hamilton; A Addo

    2016-01-01

    ... the climate change in this country. Methods The HOMER hybrid optimisation software and multi-criteria decision-making, based on the TOPSIS algorithm, were used to determine the best hybrid energy system...

  12. Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

    Science.gov (United States)

    Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

    2005-01-01

    An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

  13. 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.

  14. 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

  15. Multi-Stage Hybrid Rocket Conceptual Design for Micro-Satellites Launch using Genetic Algorithm

    Science.gov (United States)

    Kitagawa, Yosuke; Kitagawa, Koki; Nakamiya, Masaki; Kanazaki, Masahiro; Shimada, Toru

    The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.

  16. A data set for evaluating the performance of multi-class multi-object video tracking

    Science.gov (United States)

    Chakraborty, Avishek; Stamatescu, Victor; Wong, Sebastien C.; Wigley, Grant; Kearney, David

    2017-05-01

    One of the challenges in evaluating multi-object video detection, tracking and classification systems is having publically available data sets with which to compare different systems. However, the measures of performance for tracking and classification are different. Data sets that are suitable for evaluating tracking systems may not be appropriate for classification. Tracking video data sets typically only have ground truth track IDs, while classification video data sets only have ground truth class-label IDs. The former identifies the same object over multiple frames, while the latter identifies the type of object in individual frames. This paper describes an advancement of the ground truth meta-data for the DARPA Neovision2 Tower data set to allow both the evaluation of tracking and classification. The ground truth data sets presented in this paper contain unique object IDs across 5 different classes of object (Car, Bus, Truck, Person, Cyclist) for 24 videos of 871 image frames each. In addition to the object IDs and class labels, the ground truth data also contains the original bounding box coordinates together with new bounding boxes in instances where un-annotated objects were present. The unique IDs are maintained during occlusions between multiple objects or when objects re-enter the field of view. This will provide: a solid foundation for evaluating the performance of multi-object tracking of different types of objects, a straightforward comparison of tracking system performance using the standard Multi Object Tracking (MOT) framework, and classification performance using the Neovision2 metrics. These data have been hosted publically.

  17. Dry Port Location Problem: A Hybrid Multi-Criteria Approach

    Directory of Open Access Journals (Sweden)

    BENTALEB Fatimazahra

    2016-03-01

    Full Text Available Choosing a location for a dry port is a problem which becomes more essential and crucial. This study deals with the problem of locating dry ports. On this matter, a model combining multi-criteria (MACBETH and mono-criteria (BARYCENTER methods to find a solution to dry port location problem has been proposed. In the first phase, a systematic literature review was carried out on dry port location problem and then a methodological classification was presented for this research. In the second phase, a hybrid multi-criteria approach was developed in order to determine the best dry port location taking different criteria into account. A Computational practice and a qualitative analysis from a case study in the Moroccan context have been provided. The results show that the optimal location is very convenient with the geographical region and the government policies.

  18. Multi-level Hybrid Cache: Impact and Feasibility

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Zhe [ORNL; Kim, Youngjae [ORNL; Ma, Xiaosong [ORNL; Shipman, Galen M [ORNL; Zhou, Yuanyuan [University of California, San Diego

    2012-02-01

    Storage class memories, including flash, has been attracting much attention as promising candidates fitting into in today's enterprise storage systems. In particular, since the cost and performance characteristics of flash are in-between those of DRAM and hard disks, it has been considered by many studies as an secondary caching layer underneath main memory cache. However, there has been a lack of studies of correlation and interdependency between DRAM and flash caching. This paper views this problem as a special form of multi-level caching, and tries to understand the benefits of this multi-level hybrid cache hierarchy. We reveal that significant costs could be saved by using Flash to reduce the size of DRAM cache, while maintaing the same performance. We also discuss design challenges of using flash in the caching hierarchy and present potential solutions.

  19. R2-Based Multi/Many-Objective Particle Swarm Optimization

    Science.gov (United States)

    Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar

    2016-01-01

    We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four well-known MOEAs. Additionally, we validate our proposal in many-objective optimization problems. In these problems, our approach showed its main strength, since it could outperform another well-known indicator-based MOEA. PMID:27656200

  20. Hybrid Multi-Layer Network Control for Emerging Cyber-Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Summerhill, Richard [Internet2, Washington, DC (United States); Lehman, Tom [Univ. of Southern California, Los Angeles, CA (United States). Information Sciences Inst. (ISI); Ghani, Nasir [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical & Computer Engineering; Boyd, Eric [Univ. Corporation for Advanced Internet Development (UCAID), Washington, DC (United States)

    2009-08-14

    There were four basic task areas identified for the Hybrid-MLN project. They are: Multi-Layer, Multi-Domain, Control Plane Architecture and Implementation; Heterogeneous DataPlane Testing; Simulation; Project Publications, Reports, and Presentations.

  1. Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

    Science.gov (United States)

    Rani, R Ranjani; Ramyachitra, D

    2016-12-01

    Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  2. Characterization of periodic extreme ultraviolet multilayer based on multi-objective evolutionary algorithm

    Science.gov (United States)

    Kuang, Shang-qi; Gong, Xue-peng; Yang, Hai-gui

    2017-11-01

    In order to refine the layered structure of extreme ultraviolet multilayers, a multi-objective evolutionary algorithm which is post-hybridized with the standard Levenberg-Marquardt algorithm is applied to analyze the grazing incidence X-ray reflectivity (GIXR) and the normal incidence extreme ultraviolet reflectance (EUVR). In this procedure, the GIXR data and EUVR data are simultaneously fitted as two objectives, and the high sensitivities of these two sets of data to layer thicknesses and densities are combined. This set of mathematical procedures is conducive to obtain a more correct model of periodic multilayers which can simultaneously describe both GIXR and EUVR measurements. As a result, the layered structure of Mo/Si multilayers with a period of about 7.0 nm is obtained.

  3. 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.

  4. Organic-Inorganic Hybrid Materials: Multi-Functional Solids for Multi-Step Reaction Processes.

    Science.gov (United States)

    Díaz, Urbano; Corma, Avelino

    2017-11-30

    The design of new hybrid materials with tailored properties at the nano-, meso-, and macro-scale, with the use of structural functional nanobuilding units, is carried out to obtain specific multi-functional materials. Organization into controlled 1D, 2D, and 3D architectures with selected functionalities is key for developing advanced catalysts, but this is hardly accomplished using conventional synthesis procedures. The use of pre-formed nanostructures, derived either from known materials or made with specific innovative synthetic methodologies, has enormous potential in the generation of multi-site catalytic materials for one-pot processes. The present concept article introduces a new archetype wherein self-assembled nanostructured builder units are the base for the design of multifunctional catalysts, which combine catalytic efficiency with fast reactant and product diffusion. The article addresses a new generation of versatile hybrid organic-inorganic multi-site catalytic materials for their use in the production of (chiral) high-added-value products within the scope of chemicals and fine chemicals production. The use of those multi-reactive solids for more nanotechnological applications, such as sensors, due to the inclusion of electron donor-acceptor structural arrays is also considered, together with the adsorption-desorption capacities due to the combination of hydrophobic and hydrophilic sub-domains. The innovative structured hybrid materials for multipurpose processes here considered, can allow the development of multi-stage one-pot reactions with industrial applications, using the materials as one nanoreactor systems, favoring more sustainable production pathways with economic, environmental and energetic advantages. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. A hybrid multi-effect distillation and adsorption cycle

    KAUST Repository

    Thu, Kyaw

    2013-04-01

    This paper describes the development of a simple hybrid desalination system of a Multi-Effect Distillation (MED) and an adsorption (AD) cycle operating at sub-atmospheric pressures and temperatures. By hybridizing the conventional MED with an AD cycle, there is a symbiotic enhancement of performances of both cycles. The performance enhancement is attributed to (i) the cascade of adsorbent\\'s regeneration temperature and this extended the usage of thermal energy emanating from the brine heater and (ii) the vapor extraction from the last MED stage by AD cycle which provides the effect of lowering saturation temperatures of all MED stages to the extent of 5°C, resulting in scavenging of heat leaks into the MED stages from the ambient. The combined effects of the hybrid cycles increase the water production capacity of the desalination plant by nearly twofolds.In this paper, we demonstrate a hybrid cycle by simulating an 8-stage MED cycle which is coupled to an adsorption cycle for direct vapor extraction from the last MED stage. The sorption properties of silica gel is utilized (acting as a mechanical vapor compressor) to reduce the saturation temperatures of MED stages. The modeling utilizes the adsorption isotherms and kinetics of the adsorbent. +. adsorbate (silica-gel. +. water) pair along with the governing equations of mass, energy and concentration. For a 8-stage MED and AD cycles operating at assorted temperatures of 65-90°C, the results show that the water production rate increases from 60% to twofolds when compared to the MED alone. The performance ratio (PR) and gain output ratio (GOR) also improve significantly. © 2012 Elsevier Ltd.

  6. Acceleration of solving the dynamic multi-objective network design problem using response surface methods

    NARCIS (Netherlands)

    Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, M.C.J.

    2014-01-01

    Optimization of externalities and accessibility using dynamic traffic management measures on a strategic level is a specific example of solving a multi-objective network design problem. Solving this optimization problem is time consuming, because heuristics like evolutionary multi objective

  7. Multi-Objective Fuzzy Linear Programming In Agricultural Production Planning

    Directory of Open Access Journals (Sweden)

    H.M.I.U. Herath

    2015-08-01

    Full Text Available Abstract Modern agriculture is characterized by a series of conflicting optimization criteria that obstruct the decision-making process in the planning of agricultural production. Such criteria are usually net profit total cost total production etc. At the same time the decision making process in the agricultural production planning is often conducted with data that accidentally occur in nature or that are fuzzy not deterministic. Such data are the yields of various crops the prices of products and raw materials demand for the product the available quantities of production factors such as water labor etc. In this paper a fuzzy multi-criteria mathematical programming model is presented. This model is applied in a region of 10 districts in Sri Lanka where paddy is cultivated under irrigated and rain fed water in the two main seasons called Yala and Maha and the optimal production plan is achieved. This study was undertaken to find out the optimal allocation of land for paddy to get a better yield while satisfying the two conflicting objectives profit maximizing and cost minimizing subjected to the utilizing of water constraint and the demand constraint. Only the availability of land constraint is considered as a crisp in nature while objectives and other constraints are treated as fuzzy. It is observed that the MOFLP is an effective method to handle more than a single objective occurs in an uncertain vague environment.

  8. Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

    Science.gov (United States)

    Holst, Terry L.

    2005-01-01

    A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

  9. 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...

  10. Game of Objects: vicarious causation and multi-modal media

    Directory of Open Access Journals (Sweden)

    Aaron Pedinotti

    2013-09-01

    Full Text Available This paper applies philosopher Graham Harman's object-oriented theory of "vicarious causation" to an analysis of the multi-modal media phenomenon known as "Game of Thrones." Examining the manner in which George R.R. Martin's best-selling series of fantasy novels has been adapted into a board game, a video game, and a hit HBO television series, it uses the changes entailed by these processes to trace the contours of vicariously generative relations. In the course of the resulting analysis, it provides new suggestions concerning the eidetic dimensions of Harman's causal model, particularly with regard to causation in linear networks and in differing types of game systems.

  11. Duo: A Human/Wearable Hybrid for Learning About Common Manipulate Objects

    National Research Council Canada - National Science Library

    Kemp, Charles C

    2002-01-01

    ... with them. Duo is a human/wearable hybrid that is designed to learn about this important domain of human intelligence by interacting with natural manipulable objects in unconstrained environments...

  12. A Multi-Sensorial Hybrid Control for Robotic Manipulation in Human-Robot Workspaces

    Directory of Open Access Journals (Sweden)

    Juan A. Corrales

    2011-10-01

    Full Text Available Autonomous manipulation in semi-structured environments where human operators can interact is an increasingly common task in robotic applications. This paper describes an intelligent multi-sensorial approach that solves this issue by providing a multi-robotic platform with a high degree of autonomy and the capability to perform complex tasks. The proposed sensorial system is composed of a hybrid visual servo control to efficiently guide the robot towards the object to be manipulated, an inertial motion capture system and an indoor localization system to avoid possible collisions between human operators and robots working in the same workspace, and a tactile sensor algorithm to correctly manipulate the object. The proposed controller employs the whole multi-sensorial system and combines the measurements of each one of the used sensors during two different phases considered in the robot task: a first phase where the robot approaches the object to be grasped, and a second phase of manipulation of the object. In both phases, the unexpected presence of humans is taken into account. This paper also presents the successful results obtained in several experimental setups which verify the validity of the proposed approach.

  13. A Multi-Objective Genetic Algorithm for Outlier Removal.

    Science.gov (United States)

    Nahum, Oren E; Yosipof, Abraham; Senderowitz, Hanoch

    2015-12-28

    Quantitative structure activity relationship (QSAR) or quantitative structure property relationship (QSPR) models are developed to correlate activities for sets of compounds with their structure-derived descriptors by means of mathematical models. The presence of outliers, namely, compounds that differ in some respect from the rest of the data set, compromise the ability of statistical methods to derive QSAR models with good prediction statistics. Hence, outliers should be removed from data sets prior to model derivation. Here we present a new multi-objective genetic algorithm for the identification and removal of outliers based on the k nearest neighbors (kNN) method. The algorithm was used to remove outliers from three different data sets of pharmaceutical interest (logBBB, factor 7 inhibitors, and dihydrofolate reductase inhibitors), and its performances were compared with those of five other methods for outlier removal. The results suggest that the new algorithm provides filtered data sets that (1) better maintain the internal diversity of the parent data sets and (2) give rise to QSAR models with much better prediction statistics. Equally good filtered data sets in terms of these metrics were obtained when another objective function was added to the algorithm (termed "preservation"), forcing it to remove certain compounds with low probability only. This option is highly useful when specific compounds should be preferably kept in the final data set either because they have favorable activities or because they represent interesting molecular scaffolds. We expect this new algorithm to be useful in future QSAR applications.

  14. A data set for evaluating the performance of multi-class multi-object video tracking

    OpenAIRE

    Chakraborty, Avishek; Stamatescu, Victor; Wong, Sebastien C.; Wigley, Grant; Kearney, David

    2017-01-01

    One of the challenges in evaluating multi-object video detection, tracking and classification systems is having publically available data sets with which to compare different systems. However, the measures of performance for tracking and classification are different. Data sets that are suitable for evaluating tracking systems may not be appropriate for classification. Tracking video data sets typically only have ground truth track IDs, while classification video data sets only have ground tru...

  15. Low-Carbon Based Multi-Objective Bi-Level Power Dispatching under Uncertainty

    Directory of Open Access Journals (Sweden)

    Xiaoyang Zhou

    2016-06-01

    Full Text Available This research examines a low-carbon power dispatch problem under uncertainty. A hybrid uncertain multi-objective bi-level model with one leader and multiple followers is established to support the decision making of power dispatch and generation. The upper level decision maker is the regional power grid corporation which allocates power quotas to each follower based on the objectives of reasonable returns, a small power surplus and low carbon emissions. The lower level decision makers are the power generation groups which decide on their respective power generation plans and prices to ensure the highest total revenue under consideration of government subsidies, environmental costs and the carbon trading. Random and fuzzy variables are adopted to describe the uncertain factors and chance constrained and expected value programming are used to handle the hybrid uncertain model. The bi-level models are then transformed into solvable single level models using a satisfaction method. Finally, a detailed case study and comparative analyses are presented to test the proposed models and approaches to validate the effectiveness and illustrate the advantages.

  16. A Lookahead Behavior Model for Multi-Agent Hybrid Simulation

    Directory of Open Access Journals (Sweden)

    Mei Yang

    2017-10-01

    Full Text Available In the military field, multi-agent simulation (MAS plays an important role in studying wars statistically. For a military simulation system, which involves large-scale entities and generates a very large number of interactions during the runtime, the issue of how to improve the running efficiency is of great concern for researchers. Current solutions mainly use hybrid simulation to gain fewer updates and synchronizations, where some important continuous models are maintained implicitly to keep the system dynamics, and partial resynchronization (PR is chosen as the preferable state update mechanism. However, problems, such as resynchronization interval selection and cyclic dependency, remain unsolved in PR, which easily lead to low update efficiency and infinite looping of the state update process. To address these problems, this paper proposes a lookahead behavior model (LBM to implement a PR-based hybrid simulation. In LBM, a minimal safe time window is used to predict the interactions between implicit models, upon which the resynchronization interval can be efficiently determined. Moreover, the LBM gives an estimated state value in the lookahead process so as to break the state-dependent cycle. The simulation results show that, compared with traditional mechanisms, LBM requires fewer updates and synchronizations.

  17. Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.

    Science.gov (United States)

    Yassa, Sonia; Chelouah, Rachid; Kadima, Hubert; Granado, Bertrand

    2013-01-01

    We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

  18. Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Min-Yin Liu

    2017-05-01

    Full Text Available Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz measured by electroencephalography (EEG mostly during non-rapid eye movement (NREM stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1 the lack of common benchmark databases, and (2 the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA, the Strength Pareto Evolutionary Algorithm (SPEA2, to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT, and two Hilbert-Huang transform (HHT based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.

  19. MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

    Directory of Open Access Journals (Sweden)

    Ramkrishna Mitra

    Full Text Available BACKGROUND: Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. METHODOLOGY/PRINCIPAL FINDING: In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA and SVM. CONCLUSIONS/SIGNIFICANCE: MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC of 0.583 and average class-wise accuracy (ACA of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive

  20. Bacterial Foraging Optimization -Genetic Algorithm for Multiple Sequence Alignment with Multi-Objectives.

    Science.gov (United States)

    Manikandan, P; Ramyachitra, D

    2017-08-18

    This research work focus on the multiple sequence alignment, as developing an exact multiple sequence alignment for different protein sequences is a difficult computational task. In this research, a hybrid algorithm named Bacterial Foraging Optimization-Genetic Algorithm (BFO-GA) algorithm is aimed to improve the multi-objectives and carrying out measures of multiple sequence alignment. The proposed algorithm employs multi-objectives such as variable gap penalty minimization, maximization of similarity and non-gap percentage. The proposed BFO-GA algorithm is measured with various MSA methods such as T-Coffee, Clustal Omega, Muscle, K-Align, MAFFT, GA, ACO, ABC and PSO. The experiments were taken on four benchmark datasets such as BAliBASE 3.0, Prefab 4.0, SABmark 1.65 and Oxbench 1.3 databases and the outcomes prove that the proposed BFO-GA algorithm obtains better statistical significance results as compared with the other well-known methods. This research study also evaluates the practicability of the alignments of BFO-GA by applying the optimal sequence to predict the phylogenetic tree by using ClustalW2 Phylogeny tool and compare with the existing algorithms by using the Robinson-Foulds (RF) distance performance metric. Lastly, the statistical implication of the proposed algorithm is computed by using the Wilcoxon Matched-Pair Signed- Rank test and also it infers better results.

  1. Impact of fuel cell power plants on multi-objective optimal operation management of distribution network

    Energy Technology Data Exchange (ETDEWEB)

    Niknam, T. [Electrical and Electronic Engineering Department, Shiraz University of Technology, Shiraz (Iran, Islamic Republic of); Zeinoddini-Meymand, H. [Islamic Azad University, Kerman Branch, Kerman (Iran, Islamic Republic of)

    2012-06-15

    This paper presents an interactive fuzzy satisfying method based on hybrid modified honey bee mating optimization and differential evolution (MHBMO-DE) to solve the multi-objective optimal operation management (MOOM) problem, which can be affected by fuel cell power plants (FCPPs). The objective functions are to minimize total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources, and deviation of bus voltages. A new interactive fuzzy satisfying method is presented to solve the multi-objective problem by assuming that the decision-maker (DM) has fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting the corresponding membership functions. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used to solve this problem because of their independence from type of the objective function and constraints. Recently researchers have presented a new evolutionary method called honey bee mating optimization (HBMO) algorithm. Original HBMO often converges to local optima, in order to overcome this shortcoming, we propose a new method that improves the mating process and also, combines the modified HBMO with DE algorithm. Numerical results for a distribution test system have been presented to illustrate the performance and applicability of the proposed method. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  2. A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach

    Science.gov (United States)

    Niakan, F.; Vahdani, B.; Mohammadi, M.

    2015-12-01

    This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.

  3. 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.)

  4. Large micromirror array for multi-object spectroscopy in space

    Science.gov (United States)

    Canonica, Michael; Zamkotsian, Frédéric; Lanzoni, Patrick; Noell, Wilfried

    2017-11-01

    Multi-object spectroscopy (MOS) is a powerful tool for space and ground-based telescopes for the study of the formation and evolution of galaxies. This technique requires a programmable slit mask for astronomical object selection. We are engaged in a European development of micromirror arrays (MMA) for generating reflective slit masks in future MOS, called MIRA. The 100 x 200 μm2 micromirrors are electrostatically tilted providing a precise angle. The main requirements are cryogenic environment capabilities, precise and uniform tilt angle over the whole device, uniformity of the mirror voltage-tilt hysteresis and a low mirror deformation. A first MMA with single-crystal silicon micromirrors was successfully designed, fabricated and tested. A new generation of micromirror arrays composed of 2048 micromirrors (32 x 64) and modelled for individual addressing were fabricated using fusion and eutectic wafer-level bonding. These micromirrors without coating show a peak-to-valley deformation less than 10 nm, a tilt angle of 24° for an actuation voltage of 130 V. Individual addressing capability of each mirror has been demonstrated using a line-column algorithm based on an optimized voltage-tilt hysteresis. Devices are currently packaged, wire-bonded and integrated to a dedicated electronics to demonstrate the individual actuation of all micromirrors on an array. An operational test of this large array with gold coated mirrors has been done at cryogenic temperature (162 K): the micromirrors were actuated successfully before, during and after the cryogenic experiment. The micromirror surface deformation was measured at cryo and is below 30 nm peak-to-valley.

  5. The multi-objective Spanish National Forest Inventory

    Energy Technology Data Exchange (ETDEWEB)

    Alberdi, I.; Vallejo, R.; Álvarez-González, J.G.; Condés, S.; González-Ferreiro, E.; Guerrero, S.

    2017-11-01

    Aim of study: To present the evolution of the current multi-objective Spanish National Forest Inventory (SNFI) through the assessment of different key indicators on challenging areas of the forestry sector. Area of study: Using information from the Second, Third and Fourth SNFI, this work provides case studies in Navarra, La Rioja, Galicia and Balearic Island regions and at national Spanish scale. Material and methods: These case studies present an estimation of reference values for dead wood by forest types, diameter-age modeling for Populus alba and Populus nigra in riparian forest, the invasiveness of alien species and the invasibility of forest types, herbivore preferences and effects on trees and shrub species, the methodology for estimating cork production , and the combination of SNFI4 information and Airborne Laser Scanning datasets with the aim of updating forest-fire behavior assessment information with a high degree of accuracy. Main results: The results show the suitability and feasibility of the proposed methodologies to estimate the indicators using SNFI data with the exception of the estimation of cork production. In this case, additional field variables were suggested in order to obtain robust estimates. Research highlights: By broadening the variables recorded, the SNFI has become an even more important source of forest information for the development of support tools for decision-making and assessment in diverse strategic fields such as those analyzed in this study.

  6. 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.

  7. Multi-objective optimization of aerostructures inspired by nature

    Science.gov (United States)

    Kearney, Adam C.

    The focus of this doctoral work is on the optimization of aircraft wing structures. The optimization was performed against the shape, size and topology of simple aircraft wing designs. A simple morphing wing actuator optimization is performed as well as a wing panel buckling topology optimization. This is done with biologically-inspired mathematical systems including a map L-system, a multi-objective genetic algorithm, and cellular structures represented by Voronoi diagrams. As with most aircraft optimizations, both studies aim to minimize the total weight of a wing while simultaneously meeting stiffness and strength requirements. Optimization is performed with the scripts developed in MATLAB as well as through the use of finite element codes, NASTRAN and LS-Dyna. The intent of this methodology is to develop unique designs inspired by nature and optimized through natural selection. The optimal designs are those with minimal weight as well as additional requirements specific to the problems. The designs and methodology have the potential to be of use in determining minimum weight designs in aircraft structures. A literature review of optimization techniques, methodology and method validation, and optimization comparisons is presented. The buckling panel optimization considered here also includes composite buckling failure and manufacturing assumptions for composite panels. The panels are optimized for mass and strength by controlling the laminate stacking sequence, stiffener size, and topology. The morphing wing is optimized for actuator loading and redundancy.

  8. The multi-objective Spanish National Forest Inventory

    Directory of Open Access Journals (Sweden)

    Iciar Alberdi

    2017-10-01

    Full Text Available Aim of study: To present the evolution of the current multi-objective Spanish National Forest Inventory (SNFI through the assessment of different key indicators on challenging areas of the forestry sector. Area of study: Using information from the Second, Third and Fourth SNFI, this work provides case studies in Navarra, La Rioja, Galicia and Balearic Island regions and at national Spanish scale. Material and methods: These case studies present an estimation of reference values for dead wood by forest types, diameter-age modeling for Populus alba and Populus nigra  in riparian forest, the invasiveness of alien species and the invasibility of forest types, herbivore preferences and effects on trees and shrub species, the methodology for estimating cork production , and the combination of SNFI4 information and Airborne Laser Scanning datasets with the aim of updating forest-fire behavior assessment information with a high degree of accuracy. Main results: The results show the suitability and feasibility of the proposed methodologies to estimate the indicators using SNFI data with the exception of the estimation of cork production. In this case, additional field variables were suggested in order to obtain robust estimates. Research highlights: By broadening the variables recorded, the SNFI has become an even more important source of forest information for the development of support tools for decision-making and assessment in diverse strategic fields such as those analyzed in this study.

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

    Energy Technology Data Exchange (ETDEWEB)

    Georgiadou, Paraskevi S., E-mail: pgeor@central.ntua.gr [School of Chemical Engineering, National Technical University of Athens, Zografou Campus, Athens 157 80 (Greece); Papazoglou, Ioannis A., E-mail: yannisp@ipta.demokritos.gr [Systems Reliability and Industrial Safety Laboratory, National Center of Scientific Research ' Demokritos' , Agia Paraskevi, Athens 153 10 (Greece); Kiranoudis, Chris T., E-mail: kyr@chemeng.ntua.gr [School of Chemical Engineering, National Technical University of Athens, Zografou Campus, Athens 157 80 (Greece); Markatos, Nikolaos C., E-mail: n.markatos@ntua.gr [School of Chemical Engineering, National Technical University of Athens, Zografou Campus, Athens 157 80 (Greece)

    2010-06-15

    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.

  10. Multi-objective genetic algorithm for pseudoknotted RNA sequence design

    Directory of Open Access Journals (Sweden)

    Akito eTaneda

    2012-04-01

    Full Text Available RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective genetic algorithm (MOGA, MODENA, which we have previously proposed for pseudoknot-free RNA inverse folding. In the new version of MODENA, (i a new crossover operator is implemented and (ii pseudoknot prediction methods, IPknot and HotKnots, are used to evaluate the designed RNA sequences, allowing us to perform the inverse folding of pseudoknotted RNAs. The new version of MODENA with the new crossover operator was benchmarked with a dataset composed of natural pseudoknotted RNA secondary structures, and we found that MODENA can successfully design more pseudoknotted RNAs compared to the other pseudoknot design algorithm. In addition, a sequence constraint function newly implemented in the new version of MODENA was tested by designing RNA sequences which fold into the pseudoknotted structure of a hepatitis delta virus ribozyme; as a result, we successfully designed eight RNA sequences. The new version of MODENA is downloadable from http://rna.eit.hirosaki-u.ac.jp/modena/.

  11. Multi-objective genetic algorithm for pseudoknotted RNA sequence design.

    Science.gov (United States)

    Taneda, Akito

    2012-01-01

    RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective genetic algorithm (MOGA), MODENA, which we have previously proposed for pseudoknot-free RNA inverse folding. In the new version of MODENA, (i) a new crossover operator is implemented and (ii) pseudoknot prediction methods, IPknot and HotKnots, are used to evaluate the designed RNA sequences, allowing us to perform the inverse folding of pseudoknotted RNAs. The new version of MODENA with the new crossover operator was benchmarked with a dataset composed of natural pseudoknotted RNA secondary structures, and we found that MODENA can successfully design more pseudoknotted RNAs compared to the other pseudoknot design algorithm. In addition, a sequence constraint function newly implemented in the new version of MODENA was tested by designing RNA sequences which fold into the pseudoknotted structure of a hepatitis delta virus ribozyme; as a result, we successfully designed eight RNA sequences. The new version of MODENA is downloadable from http://rna.eit.hirosaki-u.ac.jp/modena/.

  12. Pareto Optimal Solution Analysis of Convex Multi-Objective Programming Problem

    OpenAIRE

    Li Guo Zhang; Hua Zuo

    2013-01-01

    The main method of solving multi-objective programming is changing multi-objective programming problem into single objective programming problem, and then get Pareto optimal solution. Conversely, whether all Pareto optimal solutions can be obtained through appropriate method, generally the answer is negative. In this paper, the methods of norm ideal point and membership function are used to solve the multi-objective programming problem. In norm ideal point method, norm and ideal point are giv...

  13. Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

    DEFF Research Database (Denmark)

    Zhang, Chunyu; Ding, Yi; Wu, Qiuwei

    2013-01-01

    This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO...... algorithm was proposed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified...

  14. Multi-channel, multi-objective, multi-context services: The glue of the smart cities learning ecosystem.

    Directory of Open Access Journals (Sweden)

    Carlos Delgado Kloos

    2013-08-01

    Full Text Available Smart devices in combination with other digital tools have occupied the cities transforming citizens’ urban experience. People are connected any time and anywhere with their global identities changing their relation to the local. People live in glocalities, where the local and the global co-exists. Glocalities are unique and constantly changing in a lifelong-learning process in which the citizen is in the centre. There is an urgent need for services to support glocal, reciprocal and multi-episodic lifelong learning processes in digital urban spaces. In this paper we define three key attributes that these services has to fulfil for being the glue to connect and guide the complex technology-enhanced learning ecosystems in smart cities: multi-channel, multi-objective and multi- context. Finally, we give an example of these types of services and contribute with an illustrative glocalised learning scenario showing how life-long learning processes would be supported in smart cities of the future.

  15. Agricultural Tractor Selection: A Hybrid and Multi-Attribute Approach

    Directory of Open Access Journals (Sweden)

    Jorge L. García-Alcaraz

    2016-02-01

    Full Text Available Usually, agricultural tractor investments are assessed using traditional economic techniques that only involve financial attributes, resulting in reductionist evaluations. However, tractors have qualitative and quantitative attributes that must be simultaneously integrated into the evaluation process. This article reports a hybrid and multi-attribute approach to assessing a set of agricultural tractors based on AHP-TOPSIS. To identify the attributes in the model, a survey including eighteen attributes was given to agricultural machinery salesmen and farmers for determining their importance. The list of attributes was presented to a decision group for a case of study, and their importance was estimated using AHP and integrated into the TOPSIS technique. In this case, one tractor was selected from a set of six alternatives, integrating six attributes in the model: initial cost, annual maintenance cost, liters of diesel per hour, safety of the operator, maintainability and after-sale customer service offered by the supplier. Based on the results obtained, the model can be considered easy to apply and to have good acceptance among farmers and salesmen, as there are no special software requirements for the application.

  16. Multi-Objective Big Bang–Big Crunch Optimization Algorithm For Recursive Digital Filter Design

    OpenAIRE

    Ms. Rashmi Singh; Dr. H. K. Verma

    2012-01-01

    The paper represents the design of recursive second order Butterworth low pass digital filter which optimizes both the magnitude and group delay simultaneously under the Multi-Objective Big Bang-Big Crunch Optimization algorithm. Multi-Objective problem of magnitude and group delay are solved using Multi-Objective BB-BC Optimization algorithm that operates on a complex, continuous search space and optimized by statistically determining the abilities of Big Bang Phase and Big Crunch Phase. Her...

  17. A Model of Production Planning with Multi-product, Multi time period and Multi-Objective with Fuzzy Parameters

    Directory of Open Access Journals (Sweden)

    mostafa gholamrezaii rahimi

    2015-09-01

    Full Text Available Production planning is one of the most important tasks of production and operations management and decides to determine the optimum amount of production, labor and inventory levels for each period of planning horizon with regard to productive resources and limits. This research presents a multi-product and multi-objective model of production planning with fuzzy parameters and soft constraints regarding the time value of money, inventory level, labor, capacity of machines and warehouse space. The proposed model attempts to maximize profit of the sale and minimize carrying and backordering costs and changes in labor levels. Case study conducted in the aluminum factory, shows the performance of this model comparing the current situation.

  18. Approaches and Software for Multi-Objective Optimization of Nuclear Power Structures

    Directory of Open Access Journals (Sweden)

    Andrei A. Andrianov

    2012-04-01

    Full Text Available The work presents the approaches and software developed for multi-objective optimization of nuclear power structures: the modules for energy planning package MESSAGE intended for modeling purposes of developing nuclear power systems and multi-objective evaluation of its effectiveness and an integrated approach based on the method of system dynamics and parameter space investigation, allowing the problem of optimizing a nuclear power system structure in multi-objective formulation to be solved. Some results of implementation of these tools for multi-objective optimization of nuclear power structures are shown.

  19. Performance assessment of a Multi-fuel Hybrid Engine for Future Aircraft

    NARCIS (Netherlands)

    Yin, F.; Gangoli Rao, A.

    2016-01-01

    This paper presents performance assessment of the proposed hybrid engine concept using Liquid Natural Gas (LNG) and kerosene. The multi-fuel hybrid engine is a new engine concept integrated with contra rotating fans, sequential dual combustion chambers to facilitate “Energy Mix” in aviation and a

  20. Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

    Science.gov (United States)

    Ferreria, Paulo; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale

    2017-01-01

    Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

  1. Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

    Science.gov (United States)

    Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

    2017-01-01

    Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

  2. A multi-objective robust optimization model for logistics planning in the earthquake response phase

    NARCIS (Netherlands)

    Najafi, M.; Eshghi, K.; Dullaert, W.E.H.

    2013-01-01

    Usually, resources are short in supply when earthquakes occur. In such emergency situations, disaster relief organizations must use these scarce resources efficiently to achieve the best possible emergency relief. This paper therefore proposes a multi-objective, multi-mode, multi-commodity, and

  3. Multi-objective convex programming problem arising in multivariate ...

    African Journals Online (AJOL)

    user

    objective convex programming problem. The objective functions are convex and there is a single linear constraint with some upper and lower bounds. We also consider a two dimensional multivariate problem when the cost is minimized. A numerical ...

  4. Bounded Approximations for Linear Multi-Objective Planning under Uncertainty

    NARCIS (Netherlands)

    Roijers, D.M.; Scharpff, J.; Spaan, M.T.J.; Oliehoek, F.A.; de Weerdt, M.; Whiteson, S.; Chien, S.; Do, M.; Fern, A.; Ruml, W.

    2014-01-01

    Planning under uncertainty poses a complex problem in which multiple objectives often need to be balanced. When dealing with multiple objectives, it is often assumed that the relative importance of the objectives is known a priori. However, in practice human decision makers often find it hard to

  5. Multi-Level Security Cannot Realise NEC Objectives

    NARCIS (Netherlands)

    Schotanus, H.A.; Hartog, T.; Verkoelen, C.A.A.

    2012-01-01

    Multi-Level Security (MLS) is often viewed as the holy grail of information security, especially in those environments where information of different classifications is being processed. In this paper we argue that MLS cannot facilitate the right balance between need-to-protect and duty-to-share as

  6. Optimization Modelling for Multi-Objective Supply Chains, A Case ...

    African Journals Online (AJOL)

    In this study a mathematical model was developed for minimizing the distribution cost in a multi-product supply chain system. The oil and gas sector was studied to understand the underlying supply chain system. Attempt was made to identify system parameters, variables, limitations, criteria so as to be able to define the ...

  7. Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation

    CSIR Research Space (South Africa)

    Greeff, M

    2008-06-01

    Full Text Available Many optimisation problems are multi-objective and change dynamically. Many methods use a weighted average approach to the multiple objectives. This paper introduces the usage of the vector evaluated particle swarm optimiser (VEPSO) to solve dynamic...

  8. High quality factor multi-layer symmetric hybrid plasmonic microresonator for sensing applications

    Science.gov (United States)

    Zhang, Meng; Wu, Genzhu

    2017-11-01

    A multi-layer symmetric hybrid plasmonic microresonator with two high-index dielectric microrings symmetrically near the metallic structure is proposed. Benefiting from introducing two dielectric rings near the metal, the multi-layer hybrid plasmonic microresonator has a high quality factor about 10,000, while still capable of retaining a small mode volume around 1 . 27 μm3. As a potential application, the symmetric hybrid microresonator with air-filled gap regions could be used for sensing with a sensitivity high of 210 nm per refraction index unit, a large figure of merit of 1319 due to the high quality factor.

  9. Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

    Science.gov (United States)

    Akhtar, Taimoor; Shoemaker, Christine

    2016-04-01

    Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual

  10. Object oriented simulation of hybrid renewable energy systems focused on supervisor control

    OpenAIRE

    Barambones Caramazana, Oscar; González de Durana García, José María

    2009-01-01

    EFTA 2009 With eyes focused on simulation the authors review some of the main topics of Hybrid Renewable Energy Systems (HRES). Then they describe an Object Oriented model of a simple example of one of such systems, a micro-grid, oriented to designing a decentralized Supervisor Control. The model has been implemented using AnyLogic.

  11. Development of Hybrid Courses Utilizing Modules as an Objective in ATE Projects

    Science.gov (United States)

    Payne, James E.; Murphy, Richard M.; Payne, Linda L.

    2017-01-01

    Orangeburg-Calhoun Technical College (OCtech) has been awarded two National Science Foundation Advanced Technological Education (NSF-ATE) grants since 2011 that have the development of module-based hybrid courses in Engineering Technology and Mechatronics as objectives. In this article, the advantages and challenges associated with module-based…

  12. Multi-agent system-based event-triggered hybrid control scheme for energy internet

    DEFF Research Database (Denmark)

    Dou, Chunxia; Yue, Dong; Han, Qing Long

    2017-01-01

    This paper is concerned with an event-triggered hybrid control for the energy Internet based on a multi-agent system approach with which renewable energy resources can be fully utilized to meet load demand with high security and well dynamical quality. In the design of control, a multi-agent system...

  13. Multi Objectives Reactive Dispatch Optimization of an Electrical Network

    Directory of Open Access Journals (Sweden)

    Hsan HADJ ABDALLAH

    2007-01-01

    Full Text Available One of the problems of the responsible of energy production and distribution is the maintaining of an appropriate voltage profile.This task can be done by the minimization of the active losses in the transportation and transmission lines by implantation of reactive power sources to the load buses. In addition to the minimization of the active losses, other criteria can be considered as the compensation devices cost and the voltage deviation. The problem to solve is multi criteria under constraints related to the voltages, the reactive productions, the compensation devices cost and the active losses. Its resolution requires the use of the advanced algorithms.In this paper, we propose an approach based on the evolutionary algorithms (AE to solve this problem multi criterion. It is about the SPEA2 method (Improving Strength Pareto Evolutionary Algorithm.

  14. Multi-Sensor Detection of Obscured and Buried Objects

    Science.gov (United States)

    2014-12-22

    Nearest Neighbor Classifier, Year 2 • Gradient Angle Model Algorithm on Wideband EMI data Classifier, • Context Dependent Multi-Sensor Fusion ...2008.2005249 Hichem Frigui, Lijun Zhang, Paul D Gader. Context-Dependent Multisensor Fusion and Its Application to Land Mine Detection, IEEE Transactions on...researched multisensor fmultialgorithm fusion method, which is called context-dependent fusion (CDF), is motivated by the fact that the relative

  15. Event-triggered hybrid control based on multi-Agent systems for Microgrids

    DEFF Research Database (Denmark)

    Dou, Chun-xia; Liu, Bin; Guerrero, Josep M.

    2014-01-01

    This paper is focused on a multi-agent system based event-triggered hybrid control for intelligently restructuring the operating mode of an microgrid (MG) to ensure the energy supply with high security, stability and cost effectiveness. Due to the microgrid is composed of different types of distr......This paper is focused on a multi-agent system based event-triggered hybrid control for intelligently restructuring the operating mode of an microgrid (MG) to ensure the energy supply with high security, stability and cost effectiveness. Due to the microgrid is composed of different types...... of distributed energy resources, thus it is typical hybrid dynamic network. Considering the complex hybrid behaviors, a hierarchical decentralized coordinated control scheme is firstly constructed based on multi-agent sys-tem, then, the hybrid model of the microgrid is built by using differential hybrid Petri...... nets. Based on the hybrid models, an event-triggered hybrid control including three kinds of switching controls is constructed by designing multiple enabling functions that can be activated by different triggering conditions, and the interactive coordination among different switching controls is im...

  16. Hybrid Multi-Agent Control in Microgrids: Framework, Models and Implementations Based on IEC 61850

    Directory of Open Access Journals (Sweden)

    Xiaobo Dou

    2014-12-01

    Full Text Available Operation control is a vital and complex issue for microgrids. The objective of this paper is to explore the practical means of applying decentralized control by using a multi agent system in actual microgrids and devices. This paper presents a hierarchical control framework (HCF consisting of local reaction control (LRC level, local decision control (LDC level, horizontal cooperation control (HCC level and vertical cooperation control (VCC level to meet different control requirements of a microgrid. Then, a hybrid multi-agent control model (HAM is proposed to implement HCF, and the properties, functionalities and operating rules of HAM are described. Furthermore, the paper elaborates on the implementation of HAM based on the IEC 61850 Standard, and proposes some new implementation methods, such as extended information models of IEC 61850 with agent communication language and bidirectional interaction mechanism of generic object oriented substation event (GOOSE communication. A hardware design and software system are proposed and the results of simulation and laboratory tests verify the effectiveness of the proposed strategies, models and implementations.

  17. Queued Pareto Local Search for Multi-Objective Optimization

    NARCIS (Netherlands)

    Inja, M.; Kooijman, C.; de Waard, M.; Roijers, D.M.; Whiteson, S.

    2014-01-01

    Many real-world optimization problems involve balancing multiple objectives. When there is no solution that is best with respect to all objectives, it is often desirable to compute the Pareto front. This paper proposes queued Pareto local search (QPLS), which improves on existing Pareto local search

  18. Best Compromise Solutions for Stochastic Multi-Objective ...

    African Journals Online (AJOL)

    The decision maker or power system operator may have imprecise or fuzzy goals for each objective function. In order to help the operator in selecting an operating point from the obtained set of Pareto-optimal solutions, fuzzy logic theory is applied to each objective function to obtain a fuzzy membership function. The best ...

  19. Medium - Scale Projects Selection Using Multi-Objective Decision ...

    African Journals Online (AJOL)

    Sixty (60) questionnaires were administered to experienced technically oriented personnel in the study area for evaluating objective weights attached to various projects. Forty-five responded and their values of objective weights attached to project cost, environmental effects, reliability, implementability and sustainable ...

  20. Multi-element analysis of unidentified fallen objects from Tatale in ...

    African Journals Online (AJOL)

    A multi-element analysis has been carried out on two fallen objects, # 01 and # 02, using instrumental neutron activation analysis technique. A total of 17 elements were identified in object # 01 while 21 elements were found in object # 02. The two major elements in object # 01 were Fe and Mg, which together constitute ...

  1. 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

  2. Multiattribute Utility Copulas for Multi-objective Coverage Control

    Directory of Open Access Journals (Sweden)

    Valicka Christopher G.

    2014-05-01

    Full Text Available This paper presents theoretical and experimental results related to the control and coordination of multirobot systems interested in dynamically covering a compact domain while remaining proximal, so as to promote robust inter-robot communications, and while remaining collision free with respect to each other and static obstacles. A design for a novel, gradient-based controller using nonnegative definite objective functions and an overapproximation to the maximum function is presented. By using a multiattribute utility copula to scalarize the multiobjective control problem, a control law is presented that allows for flexible tuning of the tradeofs between objectives. This procedure mitigates the controller’s dependence on objective function parameters and allows for the straightforward integration of a novel global coverage objective. Simulation and experiments demonstrate the controller’s efectiveness in promoting scenarios with collision free trajectories, robust communications, and satisfactory coverage of the entire coverage domain concurrently for a group of differential drive robots.

  3. Sensitivity analysis of multi-objective optimization of CPG parameters for quadruped robot locomotion

    Science.gov (United States)

    Oliveira, Miguel; Santos, Cristina P.; Costa, Lino

    2012-09-01

    In this paper, a study based on sensitivity analysis is performed for a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm based on NSGA-II. In this system, CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, a multi-objective problem with three conflicting objectives is formulated: maximization of the velocity, the wide stability margin and the behavioral diversity. The experimental results highlight the effectiveness of this multi-objective approach and the importance of the objectives to find different walking gait solutions for the quadruped robot.

  4. HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

    Directory of Open Access Journals (Sweden)

    Shibiao Wan

    Full Text Available Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

  5. Simulation based Hardness Evaluation of a Multi-Objective Genetic Algorithm

    OpenAIRE

    Ansari, Shahab U.; Mansha, Sameen

    2014-01-01

    Studies have shown that multi-objective optimization problems are hard problems. Such problems either require longer time to converge to an optimum solution, or may not converge at all. Recently some researchers have claimed that real culprit for increasing the hardness of multi-objective problems are not the number of objectives themselves rather it is the increased size of solution set, incompatibility of solutions, and high probability of finding suboptimal solution due to increased number...

  6. Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

    Science.gov (United States)

    Kadima, Hubert; Granado, Bertrand

    2013-01-01

    We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

  7. Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

    Directory of Open Access Journals (Sweden)

    Sonia Yassa

    2013-01-01

    Full Text Available We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

  8. From object to subject: hybrid identities of indigenous women in science

    Science.gov (United States)

    McKinley, Elizabeth

    2008-12-01

    The use of hybridity today suggests a less coherent, unified and directed process than that found in the Enlightenment science's cultural imperialism, but regardless of this neither concept exists outside power and inequality. Hence, hybridity raises the question of the terms of the mixture and the conditions of mixing. Cultural hybridity produced by colonisation, under the watchful eye of science at the time, and the subsequent life in a modern world since does not obscure the power that was embedded in the moment of colonisation. Indigenous identities are constructed within and by cultural power. While we all live in a global society whose consequences no one can escape, we remain unequal participants and globalisation remains an uneven process. This article argues that power has become a constitutive element in our own hybrid identities in indigenous people's attempts to participate in science and science education. Using the indigenous peoples of Aotearoa New Zealand (called Māori) as a site of identity construction, I argue that the move from being the object of science to the subject of science, through science education in schools, brings with it traces of an earlier meaning of `hybridity' that constantly erupts into the lives of Māori women scientists.

  9. Are Speculators Unwelcome in Multi-object Auctions?

    OpenAIRE

    Marco Pagnozzi

    2010-01-01

    I consider a uniform-price auction under complete information. The possibility of resale attracts speculators who have no use value for the objects on sale. A high-value bidder may strictly prefer to let a speculator win some of the objects and then buy in the resale market, in order to keep the auction price low. Although resale induces entry by speculators and therefore increases the number of competitors, high-value bidders' incentives to "reduce demand" are also affected. Allowing resale ...

  10. Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein-small molecule docking.

    Science.gov (United States)

    Wang, Hongrui; Liu, Hongwei; Cai, Leixin; Wang, Caixia; Lv, Qiang

    2017-07-10

    In this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein-small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange. The Pareto front information generated to select lower energy conformations as representative conformation structure replicas can facilitate the convergence of the available conformational space, including available near-native structures. Furthermore, our approach directly provides min-min scenario Pareto optimal solutions, as well as a hybrid of the min-min and max-min scenario Pareto optimal solutions with lower energy conformations for use as structure templates in the REMC sampling method. These methods were validated based on a thorough analysis of a benchmark data set containing 16 benchmark test cases. An in-depth comparison between MC, REMC, multi-objective optimization-REMC (MO-REMC), and hybrid MO-REMC (HMO-REMC) sampling methods was performed to illustrate the differences between the four conformational search strategies. Our findings demonstrate that the MO-REMC and HMO-REMC conformational sampling methods are powerful approaches for obtaining protein-small molecule docking conformational predictions based on the binding energy of complexes in RosettaLigand.

  11. Country Selection Model for Sustainable Construction Businesses Using Hybrid of Objective and Subjective Information

    Directory of Open Access Journals (Sweden)

    Kang-Wook Lee

    2017-05-01

    Full Text Available An important issue for international businesses and academia is selecting countries in which to expand in order to achieve entrepreneurial sustainability. This study develops a country selection model for sustainable construction businesses using both objective and subjective information. The objective information consists of 14 variables related to country risk and project performance in 32 countries over 25 years. This hybrid model applies subjective weighting from industrial experts to objective information using a fuzzy LinPreRa-based Analytic Hierarchy Process. The hybrid model yields a more accurate country selection compared to a purely objective information-based model in experienced countries. Interestingly, the hybrid model provides some different predictions with only subjective opinions in unexperienced countries, which implies that expert opinion is not always reliable. In addition, feedback from five experts in top international companies is used to validate the model’s completeness, effectiveness, generality, and applicability. The model is expected to aid decision makers in selecting better candidate countries that lead to sustainable business success.

  12. Design of Digital IIR Filter with Conflicting Objectives Using Hybrid Gravitational Search Algorithm

    Directory of Open Access Journals (Sweden)

    D. S. Sidhu

    2015-01-01

    Full Text Available In the recent years, the digital IIR filter design as a single objective optimization problem using evolutionary algorithms has gained much attention. In this paper, the digital IIR filter design is treated as a multiobjective problem by minimizing the magnitude response error, linear phase response error and optimal order simultaneously along with meeting the stability criterion. Hybrid gravitational search algorithm (HGSA has been applied to design the digital IIR filter. GSA technique is hybridized with binary successive approximation (BSA based evolutionary search method for exploring the search space locally. The relative performance of GSA and hybrid GSA has been evaluated by applying these techniques to standard mathematical test functions. The above proposed hybrid search techniques have been applied effectively to solve the multiparameter and multiobjective optimization problem of low-pass (LP, high-pass (HP, band-pass (BP, and band-stop (BS digital IIR filter design. The obtained results reveal that the proposed technique performs better than other algorithms applied by other researchers for the design of digital IIR filter with conflicting objectives.

  13. Multi-objective optimisation with stochastic discrete-event simulation ...

    African Journals Online (AJOL)

    formulated a stochastic program of which the objective is to minimise the ... supply from the CH to ATMs, it was decided that an inventory management approach ..... lev el. One vehicle. Two vehicles. Three vehicles. Figure 7: The figure shows ...

  14. Discovering Design Principles for Soft Multi-objective Decision Making

    NARCIS (Netherlands)

    Bandaru, S.; Bittermann, M.S.; Deb, K.

    Decision-making tasks sometimes involve soft objectives. They are soft in the sense that they contain uncertainty, imprecision or vagueness. For example, decisions on built environment aim to maximize comfort or other experiential qualities. Pareto-optimal solutions to such problems can be found

  15. Best Compromise Solutions for Stochastic Multi-Objective ...

    African Journals Online (AJOL)

    Nafiisah

    costs to account for mismatch between scheduled output and actual demand in the formulation of the objective function (Bunn & Paschentis 1986), and conversion of ...... PARTI, SC, KOTHARI, DP & GUPTA, PV 1983, Economic Thermal Power. Dispatch, Institution of Engineers (India) Journal-EL, Vol. 64, pp. 126-132.

  16. Multi-sensor Object Recognition: The Case of Electronics Recycling

    NARCIS (Netherlands)

    van Dop, E.R.

    1999-01-01

    In automated object recognition systems, measurements from a single source of information do not always suffice for the reconstruction of the underlying scene. Incompleteness, inaccuracy and unreliability of the information often leaves room for multiple interpretations of the world which are

  17. TEAMBLOCKS: HYBRID ABSTRACTIONS FOR PROVABLE MULTI-AGENT AUTONOMY

    Science.gov (United States)

    2017-07-28

    of provably-correct team autonomy software. TeamBlocks includes (a) a framework for working with polynomial hybrid automata, (b) a tool to generate...12 5.2 Definitions .................................................................................................................. 12...17 Chapter 7: Modeling Vehicles and Teams

  18. Multi-Objective Optimization of Wire Antennas: Genetic Algorithms Versus Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2005-12-01

    Full Text Available The paper is aimed to the multi-objective optimization of wiremulti-band antennas. Antennas are numerically modeled using time-domainintegral-equation method. That way, the designed antennas can becharacterized in a wide band of frequencies within a single run of theanalysis. Antennas are optimized to reach the prescribed matching, toexhibit the omni-directional constant gain and to have the satisfactorypolarization purity. Results of the design are experimentally verified. The multi-objective cost function is minimized by the genetic algorithmand by the particle swarm optimization. Results of the optimization byboth the multi-objective methods are in detail compared. The combination of the time domain analysis and global optimizationmethods for the broadband antenna design and the detailed comparison ofthe multi-objective particle swarm optimization with themulti-objective genetic algorithm are the original contributions of thepaper.

  19. Structure Optimization of Stand-Alone Renewable Power Systems Based on Multi Object Function

    Directory of Open Access Journals (Sweden)

    Jae-Hoon Cho

    2016-08-01

    Full Text Available This paper presents a methodology for the size optimization of a stand-alone hybrid PV/wind/diesel/battery system while considering the following factors: total annual cost (TAC, loss of power supply probability (LPSP, and the fuel cost of the diesel generator required by the user. A new optimization algorithm and an object function (including a penalty method are also proposed; these assist with designing the best structure for a hybrid system satisfying the constraints. In hybrid energy system sources such as photovoltaic (PV, wind, diesel, and energy storage devices are connected as an electrical load supply. Because the power produced by PV and wind turbine sources is dependent on the variation of the resources (sun and wind and the load demand fluctuates, such a hybrid system must be able to satisfy the load requirements at any time and store the excess energy for use in deficit conditions. Therefore, reliability and cost are the two main criteria when designing a stand-alone hybrid system. Moreover, the operation of a diesel generator is important to achieve greater reliability. In this paper, TAC, LPSP, and the fuel cost of the diesel generator are considered as the objective variables and a hybrid teaching–learning-based optimization algorithm is proposed and used to choose the best structure of a stand-alone hybrid PV/wind/diesel/battery system. Simulation results from MATLAB support the effectiveness of the proposed method and confirm that it is more efficient than conventional methods.

  20. Improved NSGA model for multi objective operation scheduling and its evaluation

    Science.gov (United States)

    Li, Weining; Wang, Fuyu

    2017-09-01

    Reasonable operation can increase the income of the hospital and improve the patient’s satisfactory level. In this paper, by using multi object operation scheduling method with improved NSGA algorithm, it shortens the operation time, reduces the operation costand lowers the operation risk, the multi-objective optimization model is established for flexible operation scheduling, through the MATLAB simulation method, the Pareto solution is obtained, the standardization of data processing. The optimal scheduling scheme is selected by using entropy weight -Topsis combination method. The results show that the algorithm is feasible to solve the multi-objective operation scheduling problem, and provide a reference for hospital operation scheduling.

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

    Science.gov (United States)

    Najafi, Hamidreza; Najafi, Behzad

    2010-06-01

    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.

  2. 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.)

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

    OpenAIRE

    Darius Mačiūnas; Rimantas Belevičius; Juozas Kaunas

    2012-01-01

    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 includ...

  4. Object Recognition by Using Multi-level Feature Point Extraction

    OpenAIRE

    Cheng, Yang; Dubois, Timeo

    2017-01-01

    In this paper, we present a novel approach for object recognition in real-time by employing multilevel feature analysis and demonstrate the practicality of adapting feature extraction into a Naive Bayesian classification framework that enables simple, efficient, and robust performance. We also show the proposed method scales well as the number of level-classes grows. To effectively understand the patches surrounding a keypoint, the trained classifier uses hundreds of simple binary features an...

  5. Particle-based shape analysis of multi-object complexes.

    Science.gov (United States)

    Cates, Joshua; Fletcher, P Thomas; Styner, Martin; Hazlett, Heather Cody; Whitaker, Ross

    2008-01-01

    This paper presents a new method for optimizing surface point correspondences for shape modeling of multiobject anatomy, or shape complexes. The proposed method is novel in that it optimizes correspondence positions in the full, joint shape space of the object complex. Researchers have previously only considered the correspondence problem separately for each structure, thus ignoring the interstructural shape correlations that are increasingly of interest in many clinical contexts, such as the study of the effects of disease on groups of neuroanatomical structures. The proposed method uses a nonparametric, dynamic particle system to simultaneously sample object surfaces and optimize correspondence point positions. This paper also suggests a principled approach to hypothesis testing using the Hotelling T2 test in the PCA space of the correspondence model, with a simulation-based choice of the number of PCA modes. We also consider statistical analysis of object poses. The modeling and analysis methods are illustrated on brain structure complexes from an ongoing clinical study of pediatric autism.

  6. 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.

  7. Multi-Agent System based Event-Triggered Hybrid Controls for High-Security Hybrid Energy Generation Systems

    DEFF Research Database (Denmark)

    Dou, Chun-Xia; Yue, Dong; Guerrero, Josep M.

    2017-01-01

    This paper proposes multi-agent system based event- triggered hybrid controls for guaranteeing energy supply of a hybrid energy generation system with high security. First, a mul-ti-agent system is constituted by an upper-level central coordi-nated control agent combined with several lower......-level unit agents. Each lower-level unit agent is responsible for dealing with internal switching control and distributed dynamic regula-tion for its unit system. The upper-level agent implements coor-dinated switching control to guarantee the power supply of over-all system with high security. The internal...... switching control, distributed dynamic regulation and coordinated switching con-trol are designed fully dependent on the hybrid behaviors of all distributed energy resources and the logical relationships be-tween them, and interact with each other by means of the mul-ti-agent system to form hierarchical...

  8. 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.

  9. Multi-Objective Optimization of Submerged Arc Welding Process

    Directory of Open Access Journals (Sweden)

    Saurav Datta

    2010-06-01

    Full Text Available Submerged arc welding (SAW is an important metal fabrication technology specially applied to join metals of large thickness in a single pass. In order to obtain an efficient joint, several process parameters of SAW need to be studied and precisely selected to improve weld quality. Many methodologies were proposed in the past research to address this issue. However, a good number of past work seeks to optimize SAWprocess parameters with a single response only. In practical situations, not only is the influence of process parameters and their interactive effects on output responses are to be critically examined but also an attempt is to be made to optimize more than one response, simultaneously. To this end, the present study considers four process control parameters viz. voltage (OCV, wire feed rate, traverse speed and electrode stick-out. The selected weld quality characteristics related to features of bead geometry are depth of penetration, reinforcement and bead width. In the present reporting, an integrated approach capable of solving the simultaneous optimization of multi-quality responses in SAW was suggested. In the proposed approach, the responses were transformed into their individual desirability values by selecting appropriate desirability function. Assuming equal importance for all responses, these individual desirability values were aggregated to calculate the overall desirability values. Quadratic Response Surface Methodology (RSM was applied to establish a mathematical model representing overall desirability as a function involving linear, quadratic and interaction effect of process control parameters. This model was optimized finally within the experimental domain using PSO (Particle Swarm Optimization algorithm. A confirmatory test showed a satisfactory result. A detailed methodology of RSM, desirability function (DF and a PSO-based optimization approach was illustrated in the paper.

  10. 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.

  11. An adaptive evolutionary multi-objective approach based on simulated annealing.

    Science.gov (United States)

    Li, H; Landa-Silva, D

    2011-01-01

    A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.

  12. A facile approach to construct hybrid multi-shell calcium phosphate gene particles.

    Science.gov (United States)

    Xu, Zhi-xue; Zhang, Ran; Wang, You-xiang; Hu, Qiao-ling

    2010-04-01

    The calcium phosphate (CaP) particles have attracted much attention in gene therapy. How to construct stable gene particles was the determining factor. In this study, hybrid multi-shell CaP gene particles were successfully constructed. First, CaP nanoparticles served as a core and were coated with DNA for colloidal stabilization. The xi-potential of DNA-coated CaP nanoparticles was -15 mV. Then polyethylenimine (PEI) was added and adsorbed outside of the DNA layer due to the electrostatic attraction. The xi-potential of hybrid multi-shell CaP particles was slightly positive. With addition of PEI, the hybrid multi-shell particles could condense DNA effectively, which was determined by ethidium bromide (EtBr) exclusion assay. The hybrid particles were spherical and uniform with diameters of about 150 nm at proper conditions. By simple modification of PEI, the hybrid multi-shell CaP gene particles were successfully constructed. They may have great potential in gene therapy.

  13. Multi-Objective Analysis Applied to Mixed-Model Assembly Line Sequencing Problem through Elite Induced Evolutionary Method

    Science.gov (United States)

    Shimizu, Yoshiaki; Sakaguchi, Tatsuhiko; Pralomkarn, Theerayoth

    To meet higher customer satisfaction and shorter production lead time, assembly lines are shifting to mixed-model assembly lines. Accordingly, sequencing is becoming an increasingly important operation scheduling that directly affects on efficiency of the entire process. In this study, such sequencing problem at the mixed-model assembly line has been formulated as a bi-objective integer programming problem so that decision making through trade-off analysis can bring about significant production improvements. Then we have developed a multi-objective analysis method by hybridizing conventional and recent meta-heuristic methods. After showing its generic idea, the car mixed-model assembly line sequencing problem is concerned as a case study. Certain measures are also introduced to quantitatively evaluate the performances of the method through comparison.

  14. Well Field Management Using Multi-Objective Optimization

    DEFF Research Database (Denmark)

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

    2013-01-01

    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...... optimization results are presented for the Hardhof water works in Zurich, Switzerland. It is found that both methods perform better than the historical management. The constant scheduling performs best in fairly stable conditions, whereas the sequential optimization performs best in extreme situations...... 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...

  15. Thermodynamic Pareto optimization of turbojet engines using multi-objective genetic algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Atashkari, K.; Nariman-Zadeh, N.; Pilechi, A.; Jamali, A. [Department of Mechanical Engineering, Engineering Faculty, The University of Guilan, PO Box 3756, Rasht (Iran); Yao, X. [School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT (United Kingdom)

    2005-11-01

    Multi-objective genetic algorithms (GAs) are used for Pareto approach optimization of thermodynamic cycle of ideal turbojet engines. On this behalf, a new diversity preserving algorithm is proposed to enhance the performance of multi-objective evolutionary algorithms (MOEAs) in optimization problems with more than two objective functions. The important conflicting thermodynamic objectives that have been considered in this work are, namely, specific thrust (ST), thrust-specific fuel consumption (TSFC), propulsive efficiency ({eta}{sub p}), and thermal efficiency ({eta}{sub t}). In this paper, different pairs of these objective functions have been selected for two-objective optimization processes. Moreover, these objectives have also been considered for a four-objective optimization problem using the new diversity preserving algorithm of this work. The comparison results demonstrate the superiority of the new algorithm in preserving the diversity of non-dominated individuals and the quality of Pareto fronts in both two-objective and 4-objective optimization processes. Further, it is shown that some interesting and important relationships among optimal objective functions and decision variables involved in the thermodynamic cycle of turbojet engines can be discovered consequently. Such important relationships as useful optimal design principles would not have been obtained without the use of a multi-objective optimization approach. It is also demonstrated that the results of four-objective optimization can include those of two-objective optimization and, therefore, provide more choices for optimal design of thermodynamic cycle of ideal turbojet engines. (authors)

  16. Multi-objective Optimization of Process Performances when Cutting Carbon Steel with Abrasive Water Jet

    Directory of Open Access Journals (Sweden)

    M. Radovanović

    2016-12-01

    Full Text Available Multi-objective optimization of process performances (perpendicularity deviation, surface roughness and productivity when cutting carbon steel EN S235 with abrasive water jet is presented in this paper. Cutting factors (abrasive flow rate, traverse rate and standoff distance were determined when perpendicularity deviation and surface roughness are minimal and productivity is maximal. Multi-objective genetic algorithm (MOGA was used for the determination set of nondominated optimal points, known as Pareto front.

  17. Economic-environmental dispatch based on multi-objective quantum-behaved particle swarm optimization

    Science.gov (United States)

    Ling, Xiejin

    2017-04-01

    The optimal solution of economic-environmental dispatch not only leads to the condition of least generation cost but minimize the pollutant emission of power plants. Multi-Objective Quantum-behaved Particle Swarm Optimization (MOQPSO) algorithm based on Pareto Dominant strategy and crowding distance ordering is used to solve this multi-objective problem. The effectiveness of the proposed algorithm is verified by calculation example.

  18. Multi-objective optimization in spatial planning: Improving the effectiveness of multi-objective evolutionary algorithms (non-dominated sorting genetic algorithm II)

    Science.gov (United States)

    Karakostas, Spiros

    2015-05-01

    The multi-objective nature of most spatial planning initiatives and the numerous constraints that are introduced in the planning process by decision makers, stakeholders, etc., synthesize a complex spatial planning context in which the concept of solid and meaningful optimization is a unique challenge. This article investigates new approaches to enhance the effectiveness of multi-objective evolutionary algorithms (MOEAs) via the adoption of a well-known metaheuristic: the non-dominated sorting genetic algorithm II (NSGA-II). In particular, the contribution of a sophisticated crossover operator coupled with an enhanced initialization heuristic is evaluated against a series of metrics measuring the effectiveness of MOEAs. Encouraging results emerge for both the convergence rate of the evolutionary optimization process and the occupation of valuable regions of the objective space by non-dominated solutions, facilitating the work of spatial planners and decision makers. Based on the promising behaviour of both heuristics, topics for further research are proposed to improve their effectiveness.

  19. Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

    Directory of Open Access Journals (Sweden)

    Vito Trianni

    Full Text Available The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled. However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

  20. Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

    Science.gov (United States)

    Trianni, Vito; López-Ibáñez, Manuel

    2015-01-01

    The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

  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. OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

    Directory of Open Access Journals (Sweden)

    Y. Jia

    2016-06-01

    Full Text Available The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

  3. A hybrid credibility-based fuzzy multiple objective optimisation to differential pricing and inventory policies with arbitrage consideration

    Science.gov (United States)

    Ghasemy Yaghin, R.; Fatemi Ghomi, S. M. T.; Torabi, S. A.

    2015-10-01

    In most markets, price differentiation mechanisms enable manufacturers to offer different prices for their products or services in different customer segments; however, the perfect price discrimination is usually impossible for manufacturers. The importance of accounting for uncertainty in such environments spurs an interest to develop appropriate decision-making tools to deal with uncertain and ill-defined parameters in joint pricing and lot-sizing problems. This paper proposes a hybrid bi-objective credibility-based fuzzy optimisation model including both quantitative and qualitative objectives to cope with these issues. Taking marketing and lot-sizing decisions into account simultaneously, the model aims to maximise the total profit of manufacturer and to improve service aspects of retailing simultaneously to set different prices with arbitrage consideration. After applying appropriate strategies to defuzzify the original model, the resulting non-linear multi-objective crisp model is then solved by a fuzzy goal programming method. An efficient stochastic search procedure using particle swarm optimisation is also proposed to solve the non-linear crisp model.

  4. 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...

  5. Application of fuzzy goal programming approach to multi-objective linear fractional inventory model

    Science.gov (United States)

    Dutta, D.; Kumar, Pavan

    2015-09-01

    In this paper, we propose a model and solution approach for a multi-item inventory problem without shortages. The proposed model is formulated as a fractional multi-objective optimisation problem along with three constraints: budget constraint, space constraint and budgetary constraint on ordering cost of each item. The proposed inventory model becomes a multiple criteria decision-making (MCDM) problem in fuzzy environment. This model is solved by multi-objective fuzzy goal programming (MOFGP) approach. A numerical example is given to illustrate the proposed model.

  6. Intuitionistic Fuzzy Goal Programming Technique for Solving Non-Linear Multi-objective Structural Problem

    Directory of Open Access Journals (Sweden)

    Samir Dey

    2015-07-01

    Full Text Available This paper proposes a new multi-objective intuitionistic fuzzy goal programming approach to solve a multi-objective nonlinear programming problem in context of a structural design. Here we describe some basic properties of intuitionistic fuzzy optimization. We have considered a multi-objective structural optimization problem with several mutually conflicting objectives. The design objective is to minimize weight of the structure and minimize the vertical deflection at loading point of a statistically loaded three-bar planar truss subjected to stress constraints on each of the truss members. This approach is used to solve the above structural optimization model based on arithmetic mean and compare with the solution by intuitionistic fuzzy goal programming approach. A numerical solution is given to illustrate our approach.

  7. Innovative architecture design for high performance organic and hybrid multi-junction solar cells

    Science.gov (United States)

    Li, Ning; Spyropoulos, George D.; Brabec, Christoph J.

    2017-08-01

    The multi-junction concept is especially attractive for the photovoltaic (PV) research community owing to its potential to overcome the Schockley-Queisser limit of single-junction solar cells. Tremendous research interests are now focused on the development of high-performance absorbers and novel device architectures for emerging PV technologies, such as organic and perovskite PVs. It has been predicted that the multi-junction concept is able to boost the organic and perovskite PV technologies approaching the 20% and 30% benchmarks, respectively, showing a bright future of commercialization of the emerging PV technologies. In this contribution, we will demonstrate innovative architecture design for solution-processed, highly functional organic and hybrid multi-junction solar cells. A simple but elegant approach to fabricating organic and hybrid multi-junction solar cells will be introduced. By laminating single organic/hybrid solar cells together through an intermediate layer, the manufacturing cost and complexity of large-scale multi-junction solar cells can be significantly reduced. This smart approach to balancing the photocurrents as well as open circuit voltages in multi-junction solar cells will be demonstrated and discussed in detail.

  8. Multi-objective radiomics model for predicting distant failure in lung SBRT

    Science.gov (United States)

    Zhou, Zhiguo; Folkert, Michael; Iyengar, Puneeth; Westover, Kenneth; Zhang, Yuanyuan; Choy, Hak; Timmerman, Robert; Jiang, Steve; Wang, Jing

    2017-06-01

    Stereotactic body radiation therapy (SBRT) has demonstrated high local control rates in early stage non-small cell lung cancer patients who are not ideal surgical candidates. However, distant failure after SBRT is still common. For patients at high risk of early distant failure after SBRT treatment, additional systemic therapy may reduce the risk of distant relapse and improve overall survival. Therefore, a strategy that can correctly stratify patients at high risk of failure is needed. The field of radiomics holds great potential in predicting treatment outcomes by using high-throughput extraction of quantitative imaging features. The construction of predictive models in radiomics is typically based on a single objective such as overall accuracy or the area under the curve (AUC). However, because of imbalanced positive and negative events in the training datasets, a single objective may not be ideal to guide model construction. To overcome these limitations, we propose a multi-objective radiomics model that simultaneously considers sensitivity and specificity as objective functions. To design a more accurate and reliable model, an iterative multi-objective immune algorithm (IMIA) was proposed to optimize these objective functions. The multi-objective radiomics model is more sensitive than the single-objective model, while maintaining the same levels of specificity and AUC. The IMIA performs better than the traditional immune-inspired multi-objective algorithm.

  9. Remote concealing any arbitrary objects with multi-folded transformation optics

    CERN Document Server

    Zheng, Bin; Hao, Ran; Zhang, Xianmin; Liu, Xu; Li, Erping; Chen, Hongsheng

    2015-01-01

    Remote concealing any arbitrary object is a very interesting topic but is still impossible so far. In this Letter, we introduce a novel way to design a remote cloaking device that is applicable to any object at a certain distance. This is achieved by using multi-folded transformation optics in which we remotely generate a zero-field region that no field can penetrate inside but it will not disturb the far-field scattering electromagnetic field. As a result, any object in such zero-field region can stay or move freely but remains invisible. Our proposed idea can be further extended to design an object independent remote illusion optics, which can transform any arbitrary objects into another object without knowing the details of the objects. The proposed multi-folded transformation optics will be very useful in the design of remote devices.

  10. A multi-model multi-objective study to evaluate the role of metric choice on sensitivity assessment

    Science.gov (United States)

    Haghnegahdar, Amin; Razavi, Saman; Wheater, Howard; Gupta, Hoshin

    2016-04-01

    Sensitivity analysis (SA) is an essential tool for providing insight into model behavior, calibration, and uncertainty assessment. It is often overlooked that the metric choice can significantly change the assessment of model sensitivity. In order to identify important hydrological processes across various case studies, we conducted a multi-model multi-criteria sensitivity analysis using a novel and efficient technique, Variogram Analysis of Response Surfaces (VARS). The analysis was conducted using three physically-based hydrological models, applied at various scales ranging from small (hillslope) to large (watershed) scale. In each case, the sensitivity of simulated streamflow to model processes (represented through parameters) were measured using different metrics selected based on various hydrograph characteristics including high flows, low flows, and volume. It is demonstrated that metric choice has a significant influence on SA results and must be aligned with study objectives. Guidelines for identifying important model parameters from a multi-objective SA perspective is discussed as part of this study.

  11. Multi-Zone hybrid model for failure detection of the stable ventilation systems

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Soltani, Mohsen

    2010-01-01

    In this paper, a conceptual multi-zone model for climate control of a live stock building is elaborated. The main challenge of this research is to estimate the parameters of a nonlinear hybrid model. A recursive estimation algorithm, the Extended Kalman Filter (EKF) is implemented for estimation...

  12. A Hybrid FPGA/Coarse Parallel Processing Architecture for Multi-modal Visual Feature Descriptors

    DEFF Research Database (Denmark)

    Jensen, Lars Baunegaard With; Kjær-Nielsen, Anders; Alonso, Javier Díaz

    2008-01-01

    This paper describes the hybrid architecture developed for speeding up the processing of so-called multi-modal visual primitives which are sparse image descriptors extracted along contours. In the system, the first stages of visual processing are implemented on FPGAs due to their highly parallel...

  13. Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi?GPU System

    KAUST Repository

    Charara, Ali

    2014-05-04

    European Extreme Large Telescope (E-ELT) is a high priority project in ground based astronomy that aims at constructing the largest telescope ever built. MOSAIC is an instrument proposed for E-ELT using Multi- Object Adaptive Optics (MOAO) technique for astronomical telescopes, which compensates for effects of atmospheric turbulence on image quality, and operates on patches across a large FoV.

  14. An interactive visualization tool for the analysis of multi-objective embedded systems design space exploration

    NARCIS (Netherlands)

    Taghavi, T.; Pimentel, A.D.

    2011-01-01

    The design of today’s embedded systems involves a complex Design Space Exploration (DSE) process. Typically, multiple and conflicting criteria (objectives) should be optimized simultaneously such as performance, power, cost, etc. Usually, Multi-Objective Evolutionary Algorithms (MOEAs) are used to

  15. Multi-Object Spectroscopy in the Next Decade: A Conference Summary

    NARCIS (Netherlands)

    Trager, S. C.; Skillen, I.; Barcells, M.

    2016-01-01

    I present a highly-biased summary of the conference "Multi-Object Spectroscopy in the Next Decade: Big Questions, Large Surveys, and Wide Fields," held 2-6 March 2015 in Santa Cruz de la Palma, Spain. I focus on four issues in this summary: (1) complexity in objects, physics, and instruments is

  16. Performance of a genetic algorithm for solving the multi-objective, multimodel transportation network design problem

    NARCIS (Netherlands)

    Brands, Ties; van Berkum, Eric C.

    2014-01-01

    The optimization of infrastructure planning in a multimodal network is defined as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train

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

    DEFF Research Database (Denmark)

    Skov, Rasmus; Parigi, Dario; Damkilde, Lars

    2017-01-01

    This paper investigates the application of multi-objective optimization in the design of timber folded plate structures in the scope of the architectural design process. Considering contrasting objectives of structural displacement, early decay time (EDT), clarity (C50) and sound strength (G...

  18. Collaborative-Hybrid Multi-Layer Network Control for Emerging Cyber-Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, Tom [USC; Ghani, Nasir [UNM; Boyd, Eric [UCAID

    2010-08-31

    At a high level, there were four basic task areas identified for the Hybrid-MLN project. They are: o Multi-Layer, Multi-Domain, Control Plane Architecture and Implementation, including OSCARS layer2 and InterDomain Adaptation, Integration of LambdaStation and Terapaths with Layer2 dynamic provisioning, Control plane software release, Scheduling, AAA, security architecture, Network Virtualization architecture, Multi-Layer Network Architecture Framework Definition; o Heterogeneous DataPlane Testing; o Simulation; o Project Publications, Reports, and Presentations.

  19. A Multi-Objective Demand Side Management Considering ENS Cost in Smart Grids

    DEFF Research Database (Denmark)

    Yousefi Khanghah, Babak; Ghassemzadeh, Saeid; Hosseini, Seyed Hossein

    2017-01-01

    . As a whole, the main objective of this paper is to manage the load and energy storage options in a smart grid to reduce ENS, to minimize overall operation cost and to maximize DG operators’ (DGOs) profit. These goals are obtained by considering ENS cost in a multi-objective optimization problem. Distribution......In this paper a new method is presented to achieve economic exploitation and proper usage of network capacity by exerting controlling actions over flexible loads and energy storage (ES) equipment. Multi-objective planning for demand response programs (DRP) and battery management policies is carried...

  20. Interdependent multi-objective sizing and control optimisation of a renewable energy hydrogen system

    OpenAIRE

    Human, Gerhardus; Schoor, George van; Uren, Kenneth R.

    2014-01-01

    This paper presents a sizing and control optimisation architecture for the design and evaluation of a small-scale stand-alone hybrid PV-wind-battery system for the production of hydrogen (H2) using proton exchange membrane (PEM) technology. Three objectives are considered simultaneously namely cost, efficiency and reliability. For this task an optimisation approach is developed combining a single objective genetic algorithm (GA) with a multiobjective GA (MOGA) to optimise nine ...

  1. Sensor Node Deployment Approach in Wireless Sensor Network Based on Multi-objective Flower Pollination Algorithm

    Directory of Open Access Journals (Sweden)

    Faten Hajjej

    2017-06-01

    Full Text Available Wireless Sensor Network (WSN is one of the most dominant technology trends in the upcoming decades. Due to the lack of communication infrastructure, designing a WSN has posed a real challenge to the designers. WSNs should capture information from the environment, acquired, receive and retransmit them while having enough lifetime to reach many decades without external intervention. Thus, optimizing some objective functions, like energy consumption and coverage at the levels of nodes deployment is required to enhance the performances. In this work, deployment issue has been modeled as a constrained multi-objective optimization (MOO problem. The aim of this work was to find the optimal sensor nodes positions in the area of interest in terms of coverage, energy consumption and network connectivity. A new multi-objective optimization approach based on Flower Pollination Algorithm (FPA was introduced. The simulation results show that the proposed approach improve both coverage and energy consumption compared with other multi objective approaches.

  2. A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios

    Science.gov (United States)

    Yue, Wei; Wang, Yuping

    2017-01-01

    Due to the important effect of the higher order moments to portfolio returns, the aim of this paper is to make use of the third and fourth moments for fuzzy multi-objective portfolio selection model. Firstly, in order to overcome the low diversity of the obtained solution set and lead to corner solutions for the conventional higher moment portfolio selection models, a new entropy function based on Minkowski measure is proposed as a new objective function and a novel fuzzy multi-objective weighted possibilistic higher order moment portfolio model is presented. Secondly, to solve the proposed model efficiently, a new multi-objective evolutionary algorithm is designed. Thirdly, several portfolio performance evaluation techniques are used to evaluate the performance of the portfolio models. Finally, some experiments are conducted by using the data of Shanghai Stock Exchange and the results indicate the efficiency and effectiveness of the proposed model and algorithm.

  3. A Multi-Objective Optimization Approach for Multi-Head Beam-Type Placement Machines

    NARCIS (Netherlands)

    Torabi, S.A.; Hamedi, M.; Ashayeri, J.

    2010-01-01

    This paper addresses a highly challenging scheduling problem in the field of printed circuit board (PCB) assembly systems using Surface Mounting Devices (SMD). After describing some challenging optimization sub-problems relating to the heads of multi-head surface mounting placement machines, we

  4. Hybrid Metaheuristics

    CERN Document Server

    2013-01-01

    The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables readers to design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuristics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling.

  5. 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.

  6. Hybrid Parallelism for Volume Rendering on Large-, Multi-, and Many-Core Systems.

    Science.gov (United States)

    Howison, M; Bethel, E W; Childs, H

    2012-01-01

    With the computing industry trending toward multi- and many-core processors, we study how a standard visualization algorithm, raycasting volume rendering, can benefit from a hybrid parallelism approach. Hybrid parallelism provides the best of both worlds: using distributed-memory parallelism across a large numbers of nodes increases available FLOPs and memory, while exploiting shared-memory parallelism among the cores within each node ensures that each node performs its portion of the larger calculation as efficiently as possible. We demonstrate results from weak and strong scaling studies, at levels of concurrency ranging up to 216,000, and with data sets as large as 12.2 trillion cells. The greatest benefit from hybrid parallelism lies in the communication portion of the algorithm, the dominant cost at higher levels of concurrency. We show that reducing the number of participants with a hybrid approach significantly improves performance.

  7. Hybrid Parallelism for Volume Rendering on Large-, Multi-, and Many-Core Systems

    Energy Technology Data Exchange (ETDEWEB)

    Howison, Mark [Brown Univ., Providence, RI (United States); Bethel, E. Wes [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Childs, Hank [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2012-01-01

    With the computing industry trending towards multi- and many-core processors, we study how a standard visualization algorithm, ray-casting volume rendering, can benefit from a hybrid parallelism approach. Hybrid parallelism provides the best of both worlds: using distributed-memory parallelism across a large numbers of nodes increases available FLOPs and memory, while exploiting shared-memory parallelism among the cores within each node ensures that each node performs its portion of the larger calculation as efficiently as possible. We demonstrate results from weak and strong scaling studies, at levels of concurrency ranging up to 216,000, and with datasets as large as 12.2 trillion cells. The greatest benefit from hybrid parallelism lies in the communication portion of the algorithm, the dominant cost at higher levels of concurrency. We show that reducing the number of participants with a hybrid approach significantly improves performance.

  8. The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions.

    Science.gov (United States)

    Qu, Shaojian; Ji, Ying

    2016-01-01

    In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.

  9. The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions.

    Directory of Open Access Journals (Sweden)

    Shaojian Qu

    Full Text Available In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC. For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.

  10. A method for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

    Science.gov (United States)

    Ai, Xueshan; Dong, Zuo; Mo, Mingzhu

    2017-04-01

    The optimal reservoir operation is in generally a multi-objective problem. In real life, most of the reservoir operation optimization problems involve conflicting objectives, for which there is no single optimal solution which can simultaneously gain an optimal result of all the purposes, but rather a set of well distributed non-inferior solutions or Pareto frontier exists. On the other hand, most of the reservoirs operation rules is to gain greater social and economic benefits at the expense of ecological environment, resulting to the destruction of riverine ecology and reduction of aquatic biodiversity. To overcome these drawbacks, this study developed a multi-objective model for the reservoir operating with the conflicting functions of hydroelectric energy generation, irrigation and ecological protection. To solve the model with the objectives of maximize energy production, maximize the water demand satisfaction rate of irrigation and ecology, we proposed a multi-objective optimization method of variable penalty coefficient (VPC), which was based on integrate dynamic programming (DP) with discrete differential dynamic programming (DDDP), to generate a well distributed non-inferior along the Pareto front by changing the penalties coefficient of different objectives. This method was applied to an existing China reservoir named Donggu, through a course of a year, which is a multi-annual storage reservoir with multiple purposes. The case study results showed a good relationship between any two of the objectives and a good Pareto optimal solutions, which provide a reference for the reservoir decision makers.

  11. Multi area and multistage expansion-planning of electricity supply with sustainable energy development criteria: a multi objective model

    Energy Technology Data Exchange (ETDEWEB)

    Unsihuay-Vila, Clodomiro; Marangon-Lima, J.W.; Souza, A.C Zambroni de [Universidade Federal de Itajuba (UNIFEI), MG (Brazil)], emails: clodomirounsihuayvila @gmail.com, marangon@unifei.edu.br, zambroni@unifei.edu.br; Perez-Arriaga, I.J. [Universidad Pontificia Comillas, Madrid (Spain)], email: ipa@mit.edu

    2010-07-01

    A novel multi objective, multi area and multistage model to long-term expansion-planning of integrated generation and transmission corridors incorporating sustainable energy developing is presented in this paper. The proposed MESEDES model is a multi-regional multi-objective and 'bottom-up' energy model which considers the electricity generation/transmission value-chain, i.e., power generation alternatives including renewable, nuclear and traditional thermal generation along with transmission corridors. The model decides the optimal location and timing of the electricity generation/transmission abroad the multistage planning horizon. The MESEDES model considers three objectives belonging to sustainable energy development criteria such as: a) the minimization of investments and operation costs of : power generation, transmission corridors, energy efficiency (demand side management (DSM) programs) considering CO2 capture technologies; b) minimization of Life Cycle Greenhouse Gas Emissions (LC GHG); c) maximization of the diversification of electricity generation mix. The proposed model consider aspects of the carbon abatement policy under the CDM - Clean Development Mechanism or European Union Greenhouse Gas Emission Trading Scheme. A case study is used to illustrate the proposed framework. (author)

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

    Directory of Open Access Journals (Sweden)

    Amarjeet

    2017-07-01

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

  13. The role of object categories in hybrid visual and memory search.

    Science.gov (United States)

    Cunningham, Corbin A; Wolfe, Jeremy M

    2014-08-01

    In hybrid search, observers search for any of several possible targets in a visual display containing distracting items and, perhaps, a target. Wolfe (2012) found that response times (RTs) in such tasks increased linearly with increases in the number of items in the display. However, RT increased linearly with the log of the number of items in the memory set. In earlier work, all items in the memory set were unique instances (e.g., this apple in this pose). Typical real-world tasks involve more broadly defined sets of stimuli (e.g., any "apple" or, perhaps, "fruit"). The present experiments show how sets or categories of targets are handled in joint visual and memory search. In Experiment 1, searching for a digit among letters was not like searching for targets from a 10-item memory set, though searching for targets from an N-item memory set of arbitrary alphanumeric characters was like searching for targets from an N-item memory set of arbitrary objects. In Experiment 2, observers searched for any instance of N sets or categories held in memory. This hybrid search was harder than search for specific objects. However, memory search remained logarithmic. Experiment 3 illustrates the interaction of visual guidance and memory search when a subset of visual stimuli are drawn from a target category. Furthermore, we outline a conceptual model, supported by our results, defining the core components that would be necessary to support such categorical hybrid searches. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  14. Searching for the Pareto frontier in multi-objective protein design.

    Science.gov (United States)

    Nanda, Vikas; Belure, Sandeep V; Shir, Ofer M

    2017-08-01

    The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

  15. Multi-objective metaheuristics for preprocessing EEG data in brain-computer interfaces

    Science.gov (United States)

    Aler, Ricardo; Vega, Alicia; Galván, Inés M.; Nebro, Antonio J.

    2012-03-01

    In the field of brain-computer interfaces, one of the main issues is to classify the electroencephalogram (EEG) accurately. EEG signals have a good temporal resolution, but a low spatial one. In this article, metaheuristics are used to compute spatial filters to improve the spatial resolution. Additionally, from a physiological point of view, not all frequency bands are equally relevant. Both spatial filters and relevant frequency bands are user-dependent. In this article a multi-objective formulation for spatial filter optimization and frequency-band selection is proposed. Several multi-objective metaheuristics have been tested for this purpose. The experimental results show, in general, that multi-objective algorithms are able to select a subset of the available frequency bands, while maintaining or improving the accuracy obtained with the whole set. Also, among the different metaheuristics tested, GDE3, which is based on differential evolution, is the most useful algorithm in this context.

  16. Use of interactive data visualization in multi-objective forest planning.

    Science.gov (United States)

    Haara, Arto; Pykäläinen, Jouni; Tolvanen, Anne; Kurttila, Mikko

    2018-01-10

    Common to multi-objective forest planning situations is that they all require comparisons, searches and evaluation among decision alternatives. Through these actions, the decision maker can learn from the information presented and thus make well-justified decisions. Interactive data visualization is an evolving approach that supports learning and decision making in multidimensional decision problems and planning processes. Data visualization contributes the formation of mental image data and this process is further boosted by allowing interaction with the data. In this study, we introduce a multi-objective forest planning decision problem framework and the corresponding characteristics of data. We utilize the framework with example planning data to illustrate and evaluate the potential of 14 interactive data visualization techniques to support multi-objective forest planning decisions. Furthermore, broader utilization possibilities of these techniques to incorporate the provisioning of ecosystem services into forest management and planning are discussed. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Multi-objective game-theory models for conflict analysis in reservoir watershed management.

    Science.gov (United States)

    Lee, Chih-Sheng

    2012-05-01

    This study focuses on the development of a multi-objective game-theory model (MOGM) for balancing economic and environmental concerns in reservoir watershed management and for assistance in decision. Game theory is used as an alternative tool for analyzing strategic interaction between economic development (land use and development) and environmental protection (water-quality protection and eutrophication control). Geographic information system is used to concisely illustrate and calculate the areas of various land use types. The MOGM methodology is illustrated in a case study of multi-objective watershed management in the Tseng-Wen reservoir, Taiwan. The innovation and advantages of MOGM can be seen in the results, which balance economic and environmental concerns in watershed management and which can be interpreted easily by decision makers. For comparison, the decision-making process using conventional multi-objective method to produce many alternatives was found to be more difficult. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. Application of a fast and elitist multi-objective genetic algorithm to Reactive Power Dispatch

    Directory of Open Access Journals (Sweden)

    Subramanian Ramesh

    2009-01-01

    Full Text Available This paper presents an Elitist Non-Dominated Sorting Genetic Algorithm version II (NSGA-II, for solving the Reactive Power Dispatch (RPD problem. The optimal RPD problem is a nonlinear constrained multi-objective optimization problem where the real power loss and the bus voltage deviations are to be minimized. Since the problem is treated as a true multi-objective optimization problem, different trade-off solutions are provided. The decision maker has an option to choose a solution among the different trade-off solutions provided in the pareto-optimal front. The standard IEEE 30-bus test system is used and the results show the effectiveness of NSGA-II and confirm its potential to solve the multi-objective RPD problem. The results obtained by NSGA-II are compared and validated with conventional weighted sum method using Real-coded Genetic Algorithm (RGA and NSGA.

  19. Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    A. Khan

    2015-02-01

    Full Text Available This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a strong requirement to select a subset of the features before building the classifier. This proposed technique treats feature subset selection as multi-objective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA - II. The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of using NSGA-II for feature subset selection.

  20. 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 ...

  1. Solving Multi Objective ORPD Problem Using AIS Based Clonal Selection Algorithm with UPFC

    Directory of Open Access Journals (Sweden)

    B. Srinivasa Rao

    2017-03-01

    Full Text Available In this paper, a solution for the multi objective optimal reactive power dispatch problem by using an artificial immune system (AIS based clonal selection algorithm was presented. The proposed AIS based clonal selection algorithm uses cloning of antibodies and followed by hyper maturation to minimize the voltage stability index (L-index, voltage deviations at all load buses and the transmission real power losses by incorporating the multi type FACTS device namely the UPFC. The proposed algorithm also uses concepts of non dominated sorting and crowding distance comparison procedures to solve the multi objective optimization problem. Finally, a fuzzy decision maker strategy is applied to find the best compromise solution. The algorithm was implemented and tested on two standard IEEE 30-bus and 57-bus test systems with UPFC. The proposed results are compared with and without placing the UPFC by considering two objectives for optimization.

  2. A modified interactive procedure to solve multi-objective group decision making problem

    Directory of Open Access Journals (Sweden)

    Mohammad Izadikhah

    2014-08-01

    Full Text Available Multi-objective optimization and multiple criteria decision making problems are the process of designing the best alternative by considering the incommensurable and conflicting objectives simultaneously. One of the first interactive procedures to solve multiple criteria decision making problems is STEM method. In this paper we propose a modified interactive procedure based on STEM method by calculating the weight vector of objectives which emphasize that more important objectives be closer to ideal one. We use the AHP and TOPSIS method to find these weights and develop a multi-objective group decision making procedure. Therefore the presented method tries to increase the rate of satisfactoriness of the obtained solution. Finally, a numerical example for illustration of the new method is given to clarify the main results developed in this paper.

  3. Hydro-environmental management of groundwater resources: A fuzzy-based multi-objective compromise approach

    Science.gov (United States)

    Alizadeh, Mohammad Reza; Nikoo, Mohammad Reza; Rakhshandehroo, Gholam Reza

    2017-08-01

    Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.

  4. Research of multi-point infill criteria based on multi-objective optimization front and its application on aerodynamic shape optimization

    National Research Council Canada - National Science Library

    Ma, Yang; Zhou, Wei; Han, Qilong

    2017-01-01

    In this article, efficient multi-point infill criteria based on multi-objective optimization front and its applications on analytic functions and airfoil aerodynamic shape optimization are researched...

  5. Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling

    DEFF Research Database (Denmark)

    Soares, Joao; Vale, Zita; Canizes, Bruno

    2013-01-01

    This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aimi...... calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method....

  6. Multi-Objective Scheduling Optimization Based on a Modified Non-Dominated Sorting Genetic Algorithm-II in Voltage Source Converter−Multi-Terminal High Voltage DC Grid-Connected Offshore Wind Farms with Battery Energy Storage Systems

    Directory of Open Access Journals (Sweden)

    Ho-Young Kim

    2017-07-01

    Full Text Available Improving the performance of power systems has become a challenging task for system operators in an open access environment. This paper presents an optimization approach for solving the multi-objective scheduling problem using a modified non-dominated sorting genetic algorithm in a hybrid network of meshed alternating current (AC/wind farm grids. This approach considers voltage and power control modes based on multi-terminal voltage source converter high-voltage direct current (MTDC and battery energy storage systems (BESS. To enhance the hybrid network station performance, we implement an optimal process based on the battery energy storage system operational strategy for multi-objective scheduling over a 24 h demand profile. Furthermore, the proposed approach is formulated as a master problem and a set of sub-problems associated with the hybrid network station to improve the overall computational efficiency using Benders’ decomposition. Based on the results of the simulations conducted on modified institute of electrical and electronics engineers (IEEE-14 bus and IEEE-118 bus test systems, we demonstrate and confirm the applicability, effectiveness and validity of the proposed approach.

  7. A method for comparison of growth media in objective identification of Penicillium based on multi-spectral imaging

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder; Hansen, Michael Adsetts Edberg; Frisvad, Jens Christian

    2007-01-01

    propose the use of multi-spectral imaging as a means of objective identification. Three species of the fungal genus Penicillium are subject to classification. To obtain an objective classification we use multi-spectral images. Previously, RGB images have proven useful for the purpose. We use multi...

  8. 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.

  9. Multi-objective optimization of a parallel ankle rehabilitation robot using modified differential evolution algorithm

    Science.gov (United States)

    Wang, Congzhe; Fang, Yuefa; Guo, Sheng

    2015-07-01

    Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution(DE) algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-07-01

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

  11. A probabilistic multi objective CLSC model with Genetic algorithm-ε_Constraint approach

    Directory of Open Access Journals (Sweden)

    Alireza TaheriMoghadam

    2014-05-01

    Full Text Available In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective function is maximizing the total profit. The second objective function is minimizing the use of row materials. In the other word, the second objective function is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization and for finding the pareto optimal line, Epsilon-constraint method is used. Finally a numerical example is solved with proposed approach and performance of the model is evaluated in different sizes. The results show that this approach is effective and useful for managerial decisions.

  12. Multi-Objective Planning of Multi-Type Distributed Generation Considering Timing Characteristics and Environmental Benefits

    Directory of Open Access Journals (Sweden)

    Yajing Gao

    2014-09-01

    Full Text Available This paper presents a novel approach to multi-type distributed generation (DG planning based on the analysis of investment and income brought by grid-connected DG. Firstly, the timing characteristics of loads and DG outputs, as well as the environmental benefits of DG are analyzed. Then, on the basis of the classification of daily load sequences, the typical daily load sequence and the typical daily output sequence of DG per unit capacity can be computed. The proposed planning model takes the location, capacity and types of DG into account as optimization variables. An improved adaptive genetic algorithm is proposed to solve the model. Case studies have been carried out on the IEEE 14-node distribution system to verify the feasibility and effectiveness of the proposed method and model.

  13. Enhanced performance of microbial fuel cell with a bacteria/multi-walled carbon nanotube hybrid biofilm

    Science.gov (United States)

    Zhang, Peng; Liu, Jia; Qu, Youpeng; Zhang, Jian; Zhong, Yingjuan; Feng, Yujie

    2017-09-01

    The biofilm on the anode of a microbial fuel cell (MFC) is a vital component in system, and its formation and characteristic determines the performance of the system. In this study, a bacteria/Multi-Walled Carbon Nanotube (MWCNT) hybrid biofilm is fabricated by effectively inserting the MWCNTs into the anode biofilm via an adsorption-filtration method. This hybrid biofilm has been demonstrated to be an efficient structure for improving an anode biofilm performance. Electrochemical impedance spectroscopy (EIS) results show that the hybrid biofilm takes advantage of the conductivity and structure of MWCNT to enhance the electron transfer and substrate diffusion of the biofilm. With this hybrid biofilm, the current density, power density and coulombic efficiency are increased by 46.2%, 58.8% and 84.6%, respectively, relative to naturally grown biofilm. Furthermore, the start-up time is reduced by 53.8% compared with naturally grown biofilm. The perturbation test demonstrates that this type of hybrid biofilm exhibits strong adsorption ability and enhances the biofilm's resistance to a sudden change of substrate concentration. The superior performance of the hybrid biofilm with MWCNT ;nanowire; matrix compared with naturally grown biofilm demonstrates its great potential for boosting the performance of MFCs.

  14. Hybrid 3D model for the interaction of plasma thruster plumes with nearby objects

    Science.gov (United States)

    Cichocki, Filippo; Domínguez-Vázquez, Adrián; Merino, Mario; Ahedo, Eduardo

    2017-12-01

    This paper presents a hybrid particle-in-cell (PIC) fluid approach to model the interaction of a plasma plume with a spacecraft and/or any nearby object. Ions and neutrals are modeled with a PIC approach, while electrons are treated as a fluid. After a first iteration of the code, the domain is split into quasineutral and non-neutral regions, based on non-neutrality criteria, such as the relative charge density and the Debye length-to-cell size ratio. At the material boundaries of the former quasineutral region, a dedicated algorithm ensures that the Bohm condition is met. In the latter non-neutral regions, the electron density and electric potential are obtained by solving the coupled electron momentum balance and Poisson equations. Boundary conditions for both the electric current and potential are finally obtained with a plasma sheath sub-code and an equivalent circuit model. The hybrid code is validated by applying it to a typical plasma plume–spacecraft interaction scenario, and the physics and capabilities of the model are finally discussed.

  15. M2Align: parallel multiple sequence alignment with a multi-objective metaheuristic.

    Science.gov (United States)

    Zambrano-Vega, Cristian; Nebro, Antonio J; García-Nieto, José; Aldana-Montes, José F

    2017-10-01

    Multiple sequence alignment (MSA) is an NP-complete optimization problem found in computational biology, where the time complexity of finding an optimal alignment raises exponentially along with the number of sequences and their lengths. Additionally, to assess the quality of a MSA, a number of objectives can be taken into account, such as maximizing the sum-of-pairs, maximizing the totally conserved columns, minimizing the number of gaps, or maximizing structural information based scores such as STRIKE. An approach to deal with MSA problems is to use multi-objective metaheuristics, which are non-exact stochastic optimization methods that can produce high quality solutions to complex problems having two or more objectives to be optimized at the same time. Our motivation is to provide a multi-objective metaheuristic for MSA that can run in parallel taking advantage of multi-core-based computers. The software tool we propose, called M2Align (Multi-objective Multiple Sequence Alignment), is a parallel and more efficient version of the three-objective optimizer for sequence alignments MO-SAStrE, able of reducing the algorithm computing time by exploiting the computing capabilities of common multi-core CPU clusters. Our performance evaluation over datasets of the benchmark BAliBASE (v3.0) shows that significant time reductions can be achieved by using up to 20 cores. Even in sequential executions, M2Align is faster than MO-SAStrE, thanks to the encoding method used for the alignments. M2Align is an open source project hosted in GitHub, where the source code and sample datasets can be freely obtained: https://github.com/KhaosResearch/M2Align. antonio@lcc.uma.es. Supplementary data are available at Bioinformatics online.

  16. 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.

  17. A multi-objective programming model for assessment the GHG emissions in MSW management.

    Science.gov (United States)

    Mavrotas, George; Skoulaxinou, Sotiria; Gakis, Nikos; Katsouros, Vassilis; Georgopoulou, Elena

    2013-09-01

    In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH4 generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. 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...

  19. Multi Population Hybrid Genetic Algorithms for University Course Timetabling

    Directory of Open Access Journals (Sweden)

    Mehrnaz Shirani LIRI

    2012-08-01

    Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with at the beginning of each university year. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or almost optimal feasible solutions instead of exact solutions. Genetic algorithms, because of their multidirectional search property, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP are proposed: FGARI, FGASA and FGATS. In the proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainty and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP

  20. Implementation of a framework for multi-species, multi-objective adaptive management in Delaware Bay

    Science.gov (United States)

    McGowan, Conor P.; Smith, David R.; Nichols, James D.; Lyons, James E.; Sweka, John A.; Kalasz, Kevin; Niles, Lawrence J.; Wong, Richard; Brust, Jeffrey; Davis, Michelle C.; Spear, Braddock

    2015-01-01

    Decision analytic approaches have been widely recommended as well suited to solving disputed and ecologically complex natural resource management problems with multiple objectives and high uncertainty. However, the difference between theory and practice is substantial, as there are very few actual resource management programs that represent formal applications of decision analysis. We applied the process of structured decision making to Atlantic horseshoe crab harvest decisions in the Delaware Bay region to develop a multispecies adaptive management (AM) plan, which is currently being implemented. Horseshoe crab harvest has been a controversial management issue since the late 1990s. A largely unregulated horseshoe crab harvest caused a decline in crab spawning abundance. That decline coincided with a major decline in migratory shorebird populations that consume horseshoe crab eggs on the sandy beaches of Delaware Bay during spring migration. Our approach incorporated multiple stakeholders, including fishery and shorebird conservation advocates, to account for diverse management objectives and varied opinions on ecosystem function. Through consensus building, we devised an objective statement and quantitative objective function to evaluate alternative crab harvest policies. We developed a set of competing ecological models accounting for the leading hypotheses on the interaction between shorebirds and horseshoe crabs. The models were initially weighted based on stakeholder confidence in these hypotheses, but weights will be adjusted based on monitoring and Bayesian model weight updating. These models were used together to predict the effects of management actions on the crab and shorebird populations. Finally, we used a dynamic optimization routine to identify the state dependent optimal harvest policy for horseshoe crabs, given the possible actions, the stated objectives and our competing hypotheses about system function. The AM plan was reviewed, accepted and

  1. 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

    To solve dynamic multi-optimization problems, optimization algorithms are required to converge quickly in response to changes in the environment without reducing the diversity of the found solutions. Most Multi-Objective Evolutionary Algorithms (MOEAs) are designed to solve static multiobjective...... problems. Problems emerge when the algorithms can not converge fast enough, due to scalability issues introduced by using too generic operators. This paper presents an evolutionary algorithm CONTROLEUM-GA that uses domain specific variables and operators to solve a real dynamic greenhouse climate control...... 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...

  2. Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA approach

    Directory of Open Access Journals (Sweden)

    Ripon Kumar Chakrabortty

    2013-01-01

    Full Text Available In hierarchical production planning system, Aggregate Production Planning (APP falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA approach for solving the multi-product, multi-period aggregate production planning (APP with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems.

  3. Multi-Objective Differential Evolution for Voltage Security Constrained Optimal Power Flow in Deregulated Power Systems

    Science.gov (United States)

    Roselyn, J. Preetha; Devaraj, D.; Dash, Subhransu Sekhar

    2013-11-01

    Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal

  4. Deep learning based multi-category object detection in aerial images

    Science.gov (United States)

    Sommer, Lars W.; Schuchert, Tobias; Beyerer, Jürgen

    2017-05-01

    Multi-category object detection in aerial images is an important task for many applications such as surveillance, tracking or search and rescue tasks. In recent years, deep learning approaches using features extracted by convolutional neural networks (CNN) significantly improved the detection accuracy on detection benchmark datasets compared to traditional approaches based on hand-crafted features as used for object detection in aerial images. However, these approaches are not transferable one to one on aerial images as the used network architectures have an insufficient resolution of feature maps for handling small instances. This consequently results in poor localization accuracy or missed detections as the network architectures are explored and optimized for datasets that considerably differ from aerial images in particular in object size and image fraction occupied by an object. In this work, we propose a deep neural network derived from the Faster R-CNN approach for multi- category object detection in aerial images. We show how the detection accuracy can be improved by replacing the network architecture by an architecture especially designed for handling small object sizes. Furthermore, we investigate the impact of different parameters of the detection framework on the detection accuracy for small objects. Finally, we demonstrate the suitability of our network for object detection in aerial images by comparing our network to traditional baseline approaches and deep learning based approaches on the publicly available DLR 3K Munich Vehicle Aerial Image Dataset that comprises multiple object classes such as car, van, truck, bus and camper.

  5. Design of multi-objective damping controller for gate-controlled ...

    Indian Academy of Sciences (India)

    The eigenvalue analysis is considered as the cornerstone of the performed studies in order to investigate the multi-objective methodology in which the unstable or ... Department of Electrical Engineering, Ahar branch, Islamic Azad University, Ahar, Iran; Department of Electrical Engineering, Iran University of Science and ...

  6. 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.

  7. Analysis of double support phase of biped robot and multi-objective ...

    Indian Academy of Sciences (India)

    of the authors' knowledge, it is the first attempt to solve multi-objective optimization problem in double support phase of a biped robot. Keywords. Optimal gait planning; genetic algorithm; particle swarm optimization. 1. Introduction. Biped robots are extensively studied by researchers. A biped robot should be able to walk on.

  8. Accelerating solving the dynamic multi-objective nework design problem using response surface methods

    NARCIS (Netherlands)

    Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, Michiel C.J.; Viti, F.; Immers, B.; Tampere, C.

    2011-01-01

    Multi objective optimization of externalities of traffic solving a network design problem in which Dynamic Traffic Management measures are used, is time consuming while heuristics are needed and solving the lower level requires solving the dynamic user equilibrium problem. Use of response surface

  9. Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

    Directory of Open Access Journals (Sweden)

    Zhang Xuejun

    2015-04-01

    Full Text Available The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by reasonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimization problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm (MPEA framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is proposed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution (CC algorithm combined with non-dominated sorting genetic algorithm II (NSGA-II is introduced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multi-objective genetic algorithm (MOGA, multi-objective evolutionary algorithm based on decomposition (MOEA/D, CC-based multi-objective algorithm (CCMA as well as other two MPEAs with different migration interval strategies.

  10. A new method for solving single and multi-objective fuzzy minimum ...

    Indian Academy of Sciences (India)

    Several authors have proposed different methods for solving fuzzy minimum cost flow (MCF) problems. In this paper, some single and multi-objective fuzzy MCF problems are chosen which cannot be solved by using any of the existing methods and a new method is proposed for solving such type of problems. The main ...

  11. Analysis of double support phase of biped robot and multi-objective ...

    Indian Academy of Sciences (India)

    This paper deals with multi-objective optimization in gait planning of a 7-dof biped robot during its double support phase, while ascending and descending some staircases. For determining dynamic balance margin of the robot in terms of zero-moment point, its double support phase has been assumed to be consisting of ...

  12. 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.

  13. Multi-object spectroscopy with the European ELT: scientific synergies between EAGLE and EVE

    NARCIS (Netherlands)

    Evans, C.J.; Barbuy, B.; Bonifacio, P.; Chemla, F.; Cuby, J.G.; Dalton, G.B.; Davies, B.; Disseau, K.; Dohlen, K.; Flores, H.; Gendron, E.; Guinouard, I.; Hammer, F.; Hastings, P.; Horville, D.; Jagourel, P.; Kaper, L.; Laporte, P.; Lee, D.; Morris, S.L.; Morris, T.; Myers, R.; Navarro, R.; Parr-Burman, P.; Petitjean, P.; Puech, M.; Rollinde, E.; Rousset, G.; Schnetler, H.; Welikala, N.; Wells, M.; Yang, Y.

    2012-01-01

    The EAGLE and EVE Phase A studies for instruments for the European Extremely Large Telescope (E-ELT) originated from related top-level scientific questions, but employed different (yet complementary) methods to deliver the required observations. We re-examine the motivations for a multi-object

  14. The Science Case for Multi-Object Spectroscopy on the European ELT

    NARCIS (Netherlands)

    Evans, Chris; Puech, Mathieu; Afonso, Jose; Almaini, Omar; Amram, Philippe; Aussel, Hervé; Barbuy, Beatriz; Basden, Alistair; Bastian, Nate; Battaglia, Giuseppina; Biller, Beth; Bonifacio, Piercarlo; Bouché, Nicholas; Bunker, Andy; Caffau, Elisabetta; Charlot, Stephane; Cirasuolo, Michele; Clenet, Yann; Combes, Francoise; Conselice, Chris; Contini, Thierry; Cuby, Jean-Gabriel; Dalton, Gavin; Davies, Ben; de Koter, Alex; Disseau, Karen; Dunlop, Jim; Epinat, Benoît; Fiore, Fabrizio; Feltzing, Sofia; Ferguson, Annette; Flores, Hector; Fontana, Adriano; Fusco, Thierry; Gadotti, Dimitri; Gallazzi, Anna; Gallego, Jesus; Giallongo, Emanuele; Gonçalves, Thiago; Gratadour, Damien; Guenther, Eike; Hammer, Francois; Hill, Vanessa; Huertas-Company, Marc; Ibata, Roridgo; Kaper, Lex; Korn, Andreas; Larsen, Søren; Le Fèvre, Olivier; Lemasle, Bertrand; Maraston, Claudia; Mei, Simona; Mellier, Yannick; Morris, Simon; Östlin, Göran; Paumard, Thibaut; Pello, Roser; Pentericci, Laura; Peroux, Celine; Petitjean, Patrick; Rodrigues, Myriam; Rodríguez-Muñoz, Lucía; Rouan, Daniel; Sana, Hugues; Schaerer, Daniel; Telles, Eduardo; Trager, Scott; Tresse, Laurence; Welikala, Niraj; Zibetti, Stefano; Ziegler, Bodo

    2015-01-01

    This White Paper presents the scientific motivations for a multi-object spectrograph (MOS) on the European Extremely Large Telescope (E-ELT). The MOS case draws on all fields of contemporary astronomy, from extra-solar planets, to the study of the halo of the Milky Way and its satellites, and from

  15. Multivariate Multi-Objective Allocation in Stratified Random Sampling: A Game Theoretic Approach.

    Science.gov (United States)

    Muhammad, Yousaf Shad; Hussain, Ijaz; Shoukry, Alaa Mohamd

    2016-01-01

    We consider the problem of multivariate multi-objective allocation where no or limited information is available within the stratum variance. Results show that a game theoretic approach (based on weighted goal programming) can be applied to sample size allocation problems. We use simulation technique to determine payoff matrix and to solve a minimax game.

  16. On the Effect of Populations in Evolutionary Multi-Objective Optimisation

    DEFF Research Database (Denmark)

    Giel, Oliver; Lehre, Per Kristian

    2010-01-01

    . Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm Simple Evolutionary Multi-objective Optimiser (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA...

  17. A multi-objective approach to evolving platooning strategies in intelligent transportation systems

    NARCIS (Netherlands)

    Illigen, W. van; Haasdijk, E.; Kester, L.J.H.M.

    2013-01-01

    The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective evolutionary algorithm based on NEAT and SPEA2 that evolves highlevel

  18. Study on multi-objective flexible job-shop scheduling problem considering energy consumption

    Directory of Open Access Journals (Sweden)

    Zengqiang Jiang

    2014-06-01

    Full Text Available Purpose: Build a multi-objective Flexible Job-shop Scheduling Problem(FJSP optimization model, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered, then Design a Modified Non-dominated Sorting Genetic Algorithm (NSGA-II based on blood variation for above scheduling model.Design/methodology/approach: A multi-objective optimization theory based on Pareto optimal method is used in carrying out the optimization model. NSGA-II is used to solve the model.Findings: By analyzing the research status and insufficiency of multi-objective FJSP, Find that the difference in scheduling will also have an effect on energy consumption in machining process and environmental emissions. Therefore, job-shop scheduling requires not only guaranteeing the processing quality, time and cost, but also optimizing operation plan of machines and minimizing energy consumption.Originality/value: A multi-objective FJSP optimization model is put forward, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered. According to above model, Blood-Variation-based NSGA-II (BVNSGA-II is designed. In which, the chromosome mutation rate is determined after calculating the blood relationship between two cross chromosomes, crossover and mutation strategy of NSGA-II is optimized and the prematurity of population is overcome. Finally, the performance of the proposed model and algorithm is evaluated through a case study, and the results proved the efficiency and feasibility of the proposed model and algorithm.

  19. 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

  20. 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...

  1. Multi-Objective Synthesis of Filtering Dipole Array Based on Tuning-Space Mapping

    Directory of Open Access Journals (Sweden)

    P. Vsetula

    2015-09-01

    Full Text Available In the paper, we apply tuning-space mapping to multi-objective synthesis of a filtering antenna. The antenna is going to be implemented as a planar dipole array with serial feeding. Thanks to the multi-objective approach, we can deal with conflicting requirements on gain, impedance matching, side-lobe level, and main-lobe direction. MOSOMA algorithm is applied to compute Pareto front of optimal solutions by changing lengths of dipoles and parameters of transmission lines connecting them into a serial array. Exploitation of tuning space mapping significantly reduces CPU-time demands of the multi-objective synthesis: a coarse optimization evaluates objectives using a wire model of the filtering array (4NEC2, method of moments, and a fine optimization exploits a realistic planar model of the array (CST Microwave Studio, finite integration technique. The synthesized filtering antenna was manufactured, and its parameters were measured to be compared with objectives. The number of dipoles in the array is shown to influence the match of measured parameters and objectives.

  2. Multi-Objective Programming for Lot-Sizing with Quantity Discount

    Science.gov (United States)

    Kang, He-Yau; Lee, Amy H. I.; Lai, Chun-Mei; Kang, Mei-Sung

    2011-11-01

    Multi-objective programming (MOP) is one of the popular methods for decision making in a complex environment. In a MOP, decision makers try to optimize two or more objectives simultaneously under various constraints. A complete optimal solution seldom exists, and a Pareto-optimal solution is usually used. Some methods, such as the weighting method which assigns priorities to the objectives and sets aspiration levels for the objectives, are used to derive a compromise solution. The ɛ-constraint method is a modified weight method. One of the objective functions is optimized while the other objective functions are treated as constraints and are incorporated in the constraint part of the model. This research considers a stochastic lot-sizing problem with multi-suppliers and quantity discounts. The model is transformed into a mixed integer programming (MIP) model next based on the ɛ-constraint method. An illustrative example is used to illustrate the practicality of the proposed model. The results demonstrate that the model is an effective and accurate tool for determining the replenishment of a manufacturer from multiple suppliers for multi-periods.

  3. Hybrid thiol-ene network nanocomposites based on multi(meth)acrylate POSS.

    Science.gov (United States)

    Li, Liguo; Liang, Rendong; Li, Yajie; Liu, Hongzhi; Feng, Shengyu

    2013-09-15

    First, multi(meth)acrylate functionalized POSS monomers were synthesized in this paper. Secondly, FTIR was used to evaluate the homopolymerization behaviors of multi(meth)acrylate POSS and their copolymerization behaviors in the thiol-ene reactions with octa(3-mercaptopropyl) POSS in the presence of photoinitiator. Results showed that the photopolymerization rate of multimethacrylate POSS was faster than that of multiacrylate POSS. The FTIR results also showed that the copolymerizations were dominant in the thiol-ene reactions with octa(3-mercaptopropyl) POSS, different from traditional (meth)acrylate-thiol system, in which homopolymerizations were predominant. Finally, the resulted hybrid networks based on POSS were characterized by XRD, FE-SEM, DSC, and TGA. The characterization results showed that hybrid networks based on POSS were homogeneous and exhibited high thermal stability. Copyright © 2013 Elsevier Inc. All rights reserved.

  4. Phase imaging for absorptive phase objects using hybrid uniform and structured illumination transport of intensity equation.

    Science.gov (United States)

    Zhu, Yunhui; Zhang, Zhengyun; Barbastathis, George

    2014-11-17

    Transport of intensity equation (TIE) has been a popular and convenient phase imaging method that retrieves phase profile from the measurement of intensity differentials. Conventional 2-shot uniform illumination TIE can give reliable inversion of the phase from intensity in many situations of practical interest; however, it has a null space consisting of fields with non-zero circulation of the Poynting vector. Here, we propose the hybrid illumination TIE method to disambiguate such objects. By comparing the diffraction signals using uniform and structured (sinusoidal) illumination patterns, we obtain a modulation-induced signal that depends solely on the phase gradient. In this way, we also increase signal sensitivity in the low spatial frequency region.

  5. Large-scale multi-zone optimal power dispatch using hybrid hierarchical evolution technique

    Directory of Open Access Journals (Sweden)

    Manjaree Pandit

    2014-03-01

    Full Text Available A new hybrid technique based on hierarchical evolution is proposed for large, non-convex, multi-zone economic dispatch (MZED problems considering all practical constraints. Evolutionary/swarm intelligence-based optimisation techniques are reported to be effective only for small/medium-sized power systems. The proposed hybrid hierarchical evolution (HHE algorithm is specifically developed for solving large systems. The HHE integrates the exploration and exploitation capabilities of particle swarm optimisation and differential evolution in a novel manner such that the search efficiency is improved substantially. Most hybrid techniques export or exchange features or operations from one algorithm to the other, but in HHE their entire individual features are retained. The effectiveness of the proposed algorithm has been verified on six-test systems having different sizes and complexity levels. Non-convex MZED solution for such large and complex systems has not yet been reported.

  6. Hybrid Wing Body Multi-Bay Test Article Analysis and Assembly Final Report

    Science.gov (United States)

    Velicki, Alexander; Hoffman, Krishna; Linton, Kim A.; Baraja, Jaime; Wu, Hsi-Yung T.; Thrash, Patrick

    2017-01-01

    This report summarizes work performed by The Boeing Company, through its Boeing Research & Technology organization located in Huntington Beach, California, under the Environmentally Responsible Aviation (ERA) project. The report documents work performed to structurally analyze and assemble a large-scale Multi-bay Box (MBB) Test Article capable of withstanding bending and internal pressure loadings representative of a Hybrid Wing Body (HWB) aircraft. The work included fabrication of tooling elements for use in the fabrication and assembly of the test article.

  7. An improved hybrid multi-criteria/multidimensional model for strategic industrial location selection: Casablanca industrial zones as a case study

    National Research Council Canada - National Science Library

    Boutkhoum, Omar; Hanine, Mohamed; Agouti, Tarik; Tikniouine, Abdessadek

    2015-01-01

    ..., customers, and most other things. Based on the integration of environmental, economic and social decisive elements of sustainable development, this paper presents a hybrid decision making model combining fuzzy multi-criteria analysis...

  8. Coordination strategies for distribution grid congestion management in a Multi-Actor, Multi-Objective Setting

    DEFF Research Database (Denmark)

    Andersen, Peter Bach; Hu, Junjie; Heussen, Kai

    2012-01-01

    and the handling of real-time events for reliable grid operation. This paper presents an analysis of key stakeholders in terms of their objectives and key operations. Three potential strategies for congestion management are presented and evaluated based on their complexity of implementation, the value and benefits......It is well understood that the electric vehicle as a distributed energy resource can provide valuable services to the power system. Such services, however, would have to co-exist with hard constraints imposed by EV user demands and distribution grid operation constraints. This paper aims to address...

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    Warid Warid

    Full Text Available 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.

  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 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.

  13. 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.

  14. Multi-objective Synthesis of Antennas from Special and Conventional Materials

    Directory of Open Access Journals (Sweden)

    Z. Lukes

    2011-12-01

    Full Text Available In the paper, we try to provide a comprehensive look on a multi-objective design of radiating, guiding and reflecting structures fabricated both from special materials (semiconductors, high-impedance surfaces and conventional ones (microwave substrates, fully metallic antennas. Discussions are devoted to the proper selection of the numerical solver used for evaluating partial objectives, to the selection of the domain of analysis, to the proper formulation of the multi-objective function and to the way of computing the Pareto front of optimal solutions (here, we exploit swarm-intelligence algorithms, evolutionary methods and self-organizing migrating algorithms. The above-described approaches are applied to the design of selected types of microwave antennas, transmission lines and reflectors. Considering obtained results, the paper is concluded by generalizing remarks.

  15. 8th International Conference on Multi-Objective and Goal Programming

    CERN Document Server

    Tamiz, Mehrdad; Ries, Jana

    2010-01-01

    This volume shows the state-of-the-art in both theoretical development and application of multiple objective and goal programming. Applications from the fields of supply chain management, financial portfolio selection, financial risk management, insurance, medical imaging, sustainability, nurse scheduling, project management, water resource management, and the interface with data envelopment analysis give a good reflection of current usage. A pleasing variety of techniques are used including models with fuzzy, group-decision, stochastic, interactive, and binary aspects. Additionally, two papers from the upcoming area of multi-objective evolutionary algorithms are included. The book is based on the papers of the 8th International Conference on Multi-Objective and Goal Programming (MOPGP08) which was held in Portsmouth, UK, in September 2008.

  16. Multi-objective optimization of cellular scanning strategy in selective laser melting

    DEFF Research Database (Denmark)

    Ahrari, Ali; Deb, Kalyanmoy; Mohanty, Sankhya

    2017-01-01

    The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study is to dev...... be obtained by performing merely a local search. Possible similarities in Pareto-optimal solutions are explored....... is to develop a multi-objective approach to optimize the cellular scanning strategy such that the two aforementioned defects are minimized. The decision variable in the chosen problem is a combination of the sequence in which cells are processed and one of six scanning strategies applied to each cell. Thus......, the problem is a combination of combinatorial and choice optimization, which makes the problem difficult to solve. On a process simulation domain consisting of 32 cells, our multi-objective evolutionary method is able to find a set of trade-off solutions for the defined conflicting objectives, which cannot...

  17. 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.

  18. Multi-Objective Genetic Programming with Redundancy-Regulations for Automatic Construction of Image Feature Extractors

    Science.gov (United States)

    Watchareeruetai, Ukrit; Matsumoto, Tetsuya; Takeuchi, Yoshinori; Kudo, Hiroaki; Ohnishi, Noboru

    We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-II and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem.

  19. 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.

  20. Multi-Objective Optimal Design of Switch Reluctance Motors Using Adaptive Genetic Algorithm

    Science.gov (United States)

    Rashidi, Mehran; Rashidi, Farzan

    In this paper a design methodology based on multi objective genetic algorithm (MOGA) is presented to design the switched reluctance motors with multiple conflicting objectives such as efficiency, power factor, full load torque, and full load current, specified dimension, weight of cooper and iron and also manufacturing cost. The optimally designed motor is compared with an industrial motor having the same ratings. Results verify that the proposed method gives better performance for the multi-objective optimization problems. The results of optimal design show the reduction in the specified dimension, weight and manufacturing cost, and the improvement in the power factor, full load torque, and efficiency of the motor.A major advantage of the method is its quite short response time in obtaining the optimal design.

  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

    Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative...... approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable...... performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied...

  2. Sensitivity Synthesis for MIMO Systems: A Multi Objective H^2 Approach

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1996-01-01

    A series of multi objective QTR H-infinity designproblems are considered in this paper. The problems are formulatedas a number of coupled QTR H-infinity design problems. TheseQTR H-infinity problems can be formulated as sensitivityproblems, complementary sensitivity problems, or control...... sensitivityproblems for every output (or input) in the system. It turns out thatthese multi objective QTR H-infinity design problems, based ona number of different types of sensitivity problems, can be exactlydecoupled into k\\QTR H-infinity sensitivity problems for stablesystems, where k is the number of outputs (for...... unstable systems,independent stabilization is required). Further, it is shown how to usesimilar techniques to incorporate simultaneous specifications for differentcontrol objectives such as QTR H-infinity, etc., for the sensitivities....

  3. 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.

  4. A Multi-Objective Demand Side Management Considering ENS Cost in Smart Grids

    DEFF Research Database (Denmark)

    Yousefi Khanghah, Babak; Ghassemzadeh, Saeid; Hosseini, Seyed Hossein

    2017-01-01

    In this paper a new method is presented to achieve economic exploitation and proper usage of network capacity by exerting controlling actions over flexible loads and energy storage (ES) equipment. Multi-objective planning for demand response programs (DRP) and battery management policies is carried...... out by considering energy not supplied (ENS). In order to achieve an optimal scheduling, charge/discharge control for batteries, demand response programs and dispatch of controllable distributed generations (DGs) are also considered. Then, the balanced cost and benefits of participants are evaluated....... As a whole, the main objective of this paper is to manage the load and energy storage options in a smart grid to reduce ENS, to minimize overall operation cost and to maximize DG operators’ (DGOs) profit. These goals are obtained by considering ENS cost in a multi-objective optimization problem. Distribution...

  5. A multi-objective set covering problem: A case study of warehouse allocation in truck industry

    Directory of Open Access Journals (Sweden)

    Atefeh Malekinezhad

    2011-01-01

    Full Text Available Designing distribution centers is normally formulated in a form of set covering where is primary objective is to minimize the number of connected facilities. However, there are other issues affecting our decision on selecting suitable distribution centers such as weather conditions, temperature, infrastructure facilities, etc. In this paper, we propose a multi-objective set covering techniques where different objectives are considered in an integrated model. The proposed model of this paper is implemented for a real-world case study of truck-industry and the results are analyzed.

  6. MULTIPLE OBJECTS

    Directory of Open Access Journals (Sweden)

    A. A. Bosov

    2015-04-01

    Full Text Available Purpose. The development of complicated techniques of production and management processes, information systems, computer science, applied objects of systems theory and others requires improvement of mathematical methods, new approaches for researches of application systems. And the variety and diversity of subject systems makes necessary the development of a model that generalizes the classical sets and their development – sets of sets. Multiple objects unlike sets are constructed by multiple structures and represented by the structure and content. The aim of the work is the analysis of multiple structures, generating multiple objects, the further development of operations on these objects in application systems. Methodology. To achieve the objectives of the researches, the structure of multiple objects represents as constructive trio, consisting of media, signatures and axiomatic. Multiple object is determined by the structure and content, as well as represented by hybrid superposition, composed of sets, multi-sets, ordered sets (lists and heterogeneous sets (sequences, corteges. Findings. In this paper we study the properties and characteristics of the components of hybrid multiple objects of complex systems, proposed assessments of their complexity, shown the rules of internal and external operations on objects of implementation. We introduce the relation of arbitrary order over multiple objects, we define the description of functions and display on objects of multiple structures. Originality.In this paper we consider the development of multiple structures, generating multiple objects.Practical value. The transition from the abstract to the subject of multiple structures requires the transformation of the system and multiple objects. Transformation involves three successive stages: specification (binding to the domain, interpretation (multiple sites and particularization (goals. The proposed describe systems approach based on hybrid sets

  7. X-Spec, A Multi-Object, Trans-Millimeter-Wave Spectrometer for CCAT

    OpenAIRE

    Bradford, C.M.; Hailey-Dunsheath, S.; Shirokoff, E.; Hollister, M.; Mckenney, C. M.; Leduc, H. G.; Reck, T.; Chapman, S.C.; Tikomirov, A.; Nikola, T.; Zmuidzinas, J.

    2014-01-01

    We present the result of a design study for X-Spec, a multi-beam, R=400-700 survey spectrometer covering 190-520 GHz under development for CCAT. It is designed to measure the bright atomic fine-structure and molecular rotational transitions that cool galaxies' interstellar gas, in particular, the 158 µm rest-frame [CII] transition, in thousands to tens of thousands of galaxies ranging from z=9 to z=3.5. With the wide bandwidth and multi-object capability, X-Spec / CCAT will be more powerful t...

  8. Electronic design automation of analog ICs combining gradient models with multi-objective evolutionary algorithms

    CERN Document Server

    Rocha, Frederico AE; Lourenço, Nuno CC; Horta, Nuno CG

    2013-01-01

    This book applies to the scientific area of electronic design automation (EDA) and addresses the automatic sizing of analog integrated circuits (ICs). Particularly, this book presents an approach to enhance a state-of-the-art layout-aware circuit-level optimizer (GENOM-POF), by embedding statistical knowledge from an automatically generated gradient model into the multi-objective multi-constraint optimization kernel based on the NSGA-II algorithm. The results showed allow the designer to explore the different trade-offs of the solution space, both through the achieved device sizes, or the resp

  9. Selection of hybrid vehicle for green environment using multi-attributive border approximation area comparison method

    Directory of Open Access Journals (Sweden)

    Tapas Kumar Biswas

    2018-02-01

    Full Text Available The mobility sector including all kinds of transportation systems are facing global challenges in re-spect of green environmental issues. There has been a paradigm shift in the concept of design and manufacturing of automotive vehicles keeping in mind the scarcity of fossil fuel and the impact of emission on environment due to burning of it. The addition of hybrid and electric vehicles in pas-senger car segment has got significant momentum to address the global challenges. This research investigates the performance of a group of hybrid vehicles from customers’ perspective. Among the different brands that are available in the hybrid vehicle market, smart customers have given pri-ority to vehicle cost, mileage, tail pipe emission, comfortness and high tank size volume for long drive. Considering these attributes, selection strategy for hybrid vehicles has been developed using entropy based multi-attributive border approximation area comparison (MABAC method. This research highlights the best hybrid vehicle which reduces air pollution in cities with other significant environmental benefits, reduces dependence on foreign energy imports and minimizes the annual fuel cost.

  10. Robust multi-objective calibration strategies - possibilities for improving flood forecasting

    Science.gov (United States)

    Krauße, T.; Cullmann, J.; Saile, P.; Schmitz, G. H.

    2012-10-01

    Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless of whether the objective is aggregated of several criteria that measure different (possibly opposite) aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. Nonetheless, there is a major disadvantage of automatic calibration procedures that understand the problem of model calibration just as the solution of an optimisation problem: due to the complex-shaped response surface, the estimated solution of the optimisation problem can result in different near-optimum parameter vectors that can lead to a very different performance on the validation data. Bárdossy and Singh (2008) studied this problem for single-objective calibration problems using the example of hydrological models and proposed a geometrical sampling approach called Robust Parameter Estimation (ROPE). This approach applies the concept of data depth in order to overcome the shortcomings of automatic calibration procedures and find a set of robust parameter vectors. Recent studies confirmed the effectivity of this method. However, all ROPE approaches published so far just identify robust model

  11. Robust multi-objective calibration strategies – possibilities for improving flood forecasting

    Directory of Open Access Journals (Sweden)

    G. H. Schmitz

    2012-10-01

    Full Text Available Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless of whether the objective is aggregated of several criteria that measure different (possibly opposite aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. Nonetheless, there is a major disadvantage of automatic calibration procedures that understand the problem of model calibration just as the solution of an optimisation problem: due to the complex-shaped response surface, the estimated solution of the optimisation problem can result in different near-optimum parameter vectors that can lead to a very different performance on the validation data. Bárdossy and Singh (2008 studied this problem for single-objective calibration problems using the example of hydrological models and proposed a geometrical sampling approach called Robust Parameter Estimation (ROPE. This approach applies the concept of data depth in order to overcome the shortcomings of automatic calibration procedures and find a set of robust parameter vectors. Recent studies confirmed the effectivity of this method. However, all ROPE approaches published so far just identify

  12. 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

  13. The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework.

    NARCIS (Netherlands)

    Possel, B.; Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, M.C.J.

    2016-01-01

    Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types

  14. Economic and Emission Dispatch Using Ensemble Multi-Objective Differential Evolution Algorithm

    Directory of Open Access Journals (Sweden)

    Xiaobing Yu

    2018-02-01

    Full Text Available In the past two decades, China’s manufacturing industry has achieved great success. However, pollution and environmental impacts have become more serious while this industry has grown. The economic and emission dispatch (EED problem is a typical multi-objective optimization problem with conflicting fuel costs and pollution emission objectives. An ensemble multi-objective differential evolution (EMODE is proposed to tackle the EED problem. First, the equality constraints of the problem have been transformed into inequality constraints. Next, two mutation strategies DE/rand/1 and DE/current-to-rand/1 have been implemented to improve the conventional DE. The performance of the proposed algorithm is evaluated on six test functions and the numerical results have indicated that the proposed algorithm is effective. The proposed algorithm EMODE is used to solve a series of six generators and eleven generators in the EED problem. The experimental results obtained are compared with those reported using single optimization algorithms and multi-objective evolutionary algorithms (MOEAs. The results have revealed that the proposed algorithm EMODE either matches or outperforms those algorithms. The proposed algorithm is an effective candidate to optimize the manufacturing industry of China.

  15. A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem.

    Science.gov (United States)

    Khelifi, Lazhar; Mignotte, Max

    2017-08-01

    Image segmentation fusion is defined as the set of methods which aim at merging several image segmentations, in a manner that takes full advantage of the complementarity of each one. Previous relevant researches in this field have been impeded by the difficulty in identifying an appropriate single segmentation fusion criterion, providing the best possible, i.e., the more informative, result of fusion. In this paper, we propose a new model of image segmentation fusion based on multi-objective optimization which can mitigate this problem, to obtain a final improved result of segmentation. Our fusion framework incorporates the dominance concept in order to efficiently combine and optimize two complementary segmentation criteria, namely, the global consistency error and the F-measure (precision-recall) criterion. To this end, we present a hierarchical and efficient way to optimize the multi-objective consensus energy function related to this fusion model, which exploits a simple and deterministic iterative relaxation strategy combining the different image segments. This step is followed by a decision making task based on the so-called "technique for order performance by similarity to ideal solution". Results obtained on two publicly available databases with manual ground truth segmentations clearly show that our multi-objective energy-based model gives better results than the classical mono-objective one.

  16. Development of Pareto strategy multi-objective function method for the optimum design of ship structures

    Directory of Open Access Journals (Sweden)

    Seung-Soo Na

    2016-11-01

    Full Text Available It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS multi-objective function method and Evolutionary Strategy (ES multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.

  17. #JeSuisCharlie: Towards a Multi-Method Study of Hybrid Media Events

    Directory of Open Access Journals (Sweden)

    Johanna Sumiala

    2016-10-01

    Full Text Available This article suggests a new methodological model for the study of hybrid media events with global appeal. This model, developed in the project on the 2015 Charlie Hebdo attacks in Paris, was created specifically for researching digital media—and in particular, Twitter. The article is structured as follows. Firstly, the methodological scope is discussed against the theoretical context, e.g. the theory of media events. In the theoretical discussion, special emphasis is given to i disruptive, upsetting, or disintegrative media events and hybrid media events and ii the conditions of today’s heterogeneous and globalised media communication landscape. Secondly, the article introduces a multi-method approach developed for the analysis of hybrid media events. In this model, computational social science—namely, automated content analysis (ACA and social network analytics (SNA—are combined with a qualitative approach—specifically, digital ethnography. The article outlines three key phases for research in which the interplay between quantitative and qualitative approaches is played out. In the first phase, preliminary digital ethnography is applied to provide the outline of the event. In the second phase, quantitative social network analytics are applied to construct the digital field for research. In this phase, it is necessary to map a what is circulating on the websites and b where this circulation takes place. The third and final phase applies a qualitative approach and digital ethnography to provide a more nuanced, in-depth interpretation of what (substance/content is circulating and how this material connects with the ‘where’ in the digital landscape, hence constituting links and connections in the hybrid media landscape. In conclusion, the article reflects on how this multi-method approach contributes to understanding the workings of today’s hybrid media events: how they create and maintain symbolic battles over certain imagined

  18. Multi-locus estimates of population structure and migration in a fence lizard hybrid zone.

    Directory of Open Access Journals (Sweden)

    Adam D Leaché

    Full Text Available A hybrid zone between two species of lizards in the genus Sceloporus (S. cowlesi and S. tristichus on the Mogollon Rim in Arizona provides a unique opportunity to study the processes of lineage divergence and merging. This hybrid zone involves complex interactions between 2 morphologically and ecologically divergent subspecies, 3 chromosomal groups, and 4 mitochondrial DNA (mtDNA clades. The spatial patterns of divergence between morphology, chromosomes and mtDNA are discordant, and determining which of these character types (if any reflects the underlying population-level lineages that are of interest has remained impeded by character conflict. The focus of this study is to estimate the number of populations interacting in the hybrid zone using multi-locus nuclear data, and to then estimate the migration rates and divergence time between the inferred populations. Multi-locus estimates of population structure and gene flow were obtained from 12 anonymous nuclear loci sequenced for 93 specimens of Sceloporus. Population structure estimates support two populations, and this result is robust to changes to the prior probability distribution used in the Bayesian analysis and the use of spatially-explicit or non-spatial models. A coalescent analysis of population divergence suggests that gene flow is high between the two populations, and that the timing of divergence is restricted to the Pleistocene. The hybrid zone is more accurately described as involving two populations belonging to S. tristichus, and the presence of S. cowlesi mtDNA haplotypes in the hybrid zone is an anomaly resulting from mitochondrial introgression.

  19. European multi-centre study of the Nucleus Hybrid L24 cochlear implant

    NARCIS (Netherlands)

    Lenarz, T.; James, C.; Cuda, D.; Fitzgerald O'Connor, A.; Frachet, B.; Frijns, J.H.M.; Klenzner, T.; Laszig, R.; Manrique, M.; Marx, M.; Merkus, P.; Mylanus, E.A.M.; Offeciers, E.; Pesch, J.; Ramos-Macias, A.; Robier, A.; Sterkers, O.; Uziel, A.

    2013-01-01

    Objectives: To investigate the preservation of residual hearing in subjects who received the Nucleus Hybrid L24 cochlear implant. To investigate the performance benefits up to one year post-implantation in terms of speech recognition, sound quality, and quality of life. Design: Prospective, with

  20. Multi-objective optimized fuzzy-PID controllers for fourth order nonlinear systems

    Directory of Open Access Journals (Sweden)

    M.J. Mahmoodabadi

    2016-06-01

    Full Text Available In this paper, the Multi-objective Genetic Algorithm (MOGA is used to obtain the Pareto frontiers of conflicting objective functions for the fuzzy-Proportional-Integral-Derivative (fuzzy-PID controllers. The ball–beam and inverted pendulum fourth order nonlinear systems are regarded as nonlinear benchmarks. The considered objective functions for the ball–beam system are the distance error of the ball, the angle error of the beam, and the control effort. For the inverted pendulum system, the objective functions are the distance error of the cart, the angle error of the pendulum, and the control effort, which must be minimized simultaneously. The Pareto fronts are compared with those obtained by Multi-objective Particle Swarm Optimization (MOPSO. Four points are chosen from nondominated solutions of the obtained Pareto fronts based on the three conflicting objective functions and used for illustration of the state variables of the controlled systems. Obtained results elucidate the efficiency of the proposed controller in order to control nonlinear systems.

  1. A genetic algorithm based multi-objective shape optimization scheme for cementless femoral implant.

    Science.gov (United States)

    Chanda, Souptick; Gupta, Sanjay; Kumar Pratihar, Dilip

    2015-03-01

    The shape and geometry of femoral implant influence implant-induced periprosthetic bone resorption and implant-bone interface stresses, which are potential causes of aseptic loosening in cementless total hip arthroplasty (THA). Development of a shape optimization scheme is necessary to achieve a trade-off between these two conflicting objectives. The objective of this study was to develop a novel multi-objective custom-based shape optimization scheme for cementless femoral implant by integrating finite element (FE) analysis and a multi-objective genetic algorithm (GA). The FE model of a proximal femur was based on a subject-specific CT-scan dataset. Eighteen parameters describing the nature of four key sections of the implant were identified as design variables. Two objective functions, one based on implant-bone interface failure criterion, and the other based on resorbed proximal bone mass fraction (BMF), were formulated. The results predicted by the two objective functions were found to be contradictory; a reduction in the proximal bone resorption was accompanied by a greater chance of interface failure. The resorbed proximal BMF was found to be between 23% and 27% for the trade-off geometries as compared to ∼39% for a generic implant. Moreover, the overall chances of interface failure have been minimized for the optimal designs, compared to the generic implant. The adaptive bone remodeling was also found to be minimal for the optimally designed implants and, further with remodeling, the chances of interface debonding increased only marginally.

  2. Multiple Realities and Hybrid Objects: A Creative Approach of Schizophrenic Delusion

    Directory of Open Access Journals (Sweden)

    Michel Cermolacce

    2018-02-01

    Full Text Available Delusion is usually considered in DSM 5 as a false belief based on incorrect inference about external reality, but the issue of delusion raises crucial concerns, especially that of a possible (or absent continuity between delusional and normal experiences, and the understanding of delusional experience. In the present study, we first aim to consider delusion from a perspectivist angle, according to the Multiple Reality Theory (MRT. In this model inherited from Alfred Schütz and recently addressed by Gallagher, we are not confronting one reality only, but several (such as the reality of everyday life, of imaginary life, of work, of delusion, etc.. In other terms, the MRT states that our own experience is not drawing its meaning from one reality identified as the outer reality but rather from a multiplicity of realities, each with their own logic and style. Two clinical cases illustrate how the Multiple Realities Theory (MRT may help address the reality of delusion. Everyday reality and the reality of delusion may be articulated under a few conditions, such as compossibility [i.e., Double Book-Keeping (DBK, in Bleulerian terms] or flexibility. There are indeed possible bridges between them. Possible links with neuroscience or psychoanalysis are evoked. As the subject is confronting different realities, so do the objects among and toward which a subject is evolving. We call such objects Hybrid Objects (HO due to their multiple belonging. They can operate as shifters, i.e., as some functional operators letting one switch from one reality to another. In the final section, we will emphasize how delusion flexibility, as a dynamic interaction between Multiple Realities, may offer psychotherapeutic possibilities within some reality shared with others, entailing relocation of the present subjects in regained access to some flexibility via Multiple Realities and perspectivism.

  3. A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network.

    Science.gov (United States)

    Guo, Hongliang; Meng, Yan; Jin, Yaochu

    2009-12-01

    A major research challenge of multi-robot systems is to predict the emerging behaviors from the local interactions of the individual agents. Biological systems can generate robust and complex behaviors through relatively simple local interactions in a world characterized by rapid changes, high uncertainty, infinite richness, and limited availability of information. Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and development of biological organisms from cells. In this paper, inspired by biological organisms, we propose a distributed GRN-based algorithm for a multi-robot construction task. Through this algorithm, multiple robots can self-organize autonomously into different predefined shapes, and self-reorganize adaptively under dynamic environments. This developmental process is evolved using a multi-objective optimization algorithm to achieve a shorter travel distance and less convergence time. Furthermore, a theoretical proof of the system's convergence is also provided. Various case studies have been conducted in the simulation, and the results show the efficiency and convergence of the proposed method.

  4. Object Manifold Alignment for Multi-Temporal High Resolution Remote Sensing Images Classification

    Science.gov (United States)

    Gao, G.; Zhang, M.; Gu, Y.

    2017-05-01

    Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, "pepper and salt" appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and "pepper and salt" problem, an object-based manifold alignment method is proposed. Firstly, multi-temporal multispectral images are cut to superpixels by simple linear iterative clustering (SLIC) respectively. Secondly, some features obtained from superpixels are formed as vector. Thirdly, a majority voting manifold alignment method aiming at solving high resolution problem is proposed and mapping the vector data to alignment space. At last, all the data in the alignment space are classified by using KNN method. Multi-temporal images from different areas or the same area are both considered in this paper. In the experiments, 2 groups of multi-temporal HR images collected by China GF1 and GF2 satellites are used for performance evaluation. Experimental results indicate that the proposed method not only has significantly outperforms than traditional domain adaptation methods in classification accuracy, but also effectively overcome the problem of "pepper and salt".

  5. Breaking the Deadlock: Simultaneously Discovering Attribute Matching and Cluster Matching with Multi-Objective Metaheuristics.

    Science.gov (United States)

    Liu, Haishan; Dou, Dejing; Wang, Hao

    2012-08-01

    In this paper, we present a data mining approach to address challenges in the matching of heterogeneous datasets. In particular, we propose solutions to two problems that arise in integrating information from different results of scientific research. The first problem, attribute matching, involves discovery of correspondences among distinct numeric features (attributes) that are used to characterize datasets that have been collected and analyzed in different research labs. The second problem, cluster matching, involves discovery of matchings between patterns (clusters) across datasets. We treat both of these problems together as a multi-objective optimization problem. A multi-objective metaheuristics algorithm is described to find the optimal solution and compared with the genetic algorithm. The utility of this approach is demonstrated in a series of experiments using synthetic and realistic datasets that are designed to simulate heterogeneous data from different sources.

  6. 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.

  7. Extraction of battery parameters of the equivalent circuit model using a multi-objective genetic algorithm

    Science.gov (United States)

    Brand, Jonathan; Zhang, Zheming; Agarwal, Ramesh K.

    2014-02-01

    A simple but reasonably accurate battery model is required for simulating the performance of electrical systems that employ a battery for example an electric vehicle, as well as for investigating their potential as an energy storage device. In this paper, a relatively simple equivalent circuit based model is employed for modeling the performance of a battery. A computer code utilizing a multi-objective genetic algorithm is developed for the purpose of extracting the battery performance parameters. The code is applied to several existing industrial batteries as well as to two recently proposed high performance batteries which are currently in early research and development stage. The results demonstrate that with the optimally extracted performance parameters, the equivalent circuit based battery model can accurately predict the performance of various batteries of different sizes, capacities, and materials. Several test cases demonstrate that the multi-objective genetic algorithm can serve as a robust and reliable tool for extracting the battery performance parameters.

  8. Multi-Objective Genetic Algorithm for voltage stability enhancement using rescheduling and FACTS devices

    Directory of Open Access Journals (Sweden)

    J. Preetha Roselyn

    2014-09-01

    Full Text Available This paper presents the application of Multi-Objective Genetic Algorithm to solve the Voltage Stability Constrained Optimal Power Flow (VSCOPF problem. Two different control strategies are proposed to improve voltage stability of the system under different operating conditions. The first approach is based on the corrective control in contingency state with minimization of voltage stability index and real power control variable adjustments as objectives. The second approach involves optimal placement and sizing of multi-type FACTS devices, Static VAR Compensator and Thyristor Controlled Series Capacitor along with generator rescheduling for minimization of voltage stability index and investment cost of FACTS devices. A fuzzy based approach is employed to get the best compromise solution from the trade off curve to aid the decision maker. The effectiveness of the proposed VSCOPF problem is demonstrated on two typical systems, IEEE 30-bus and IEEE 57 bus test systems.

  9. A new method for feature selection based on fuzzy similarity measures using multi objective genetic algorithm

    Directory of Open Access Journals (Sweden)

    Hassan Nosrati Nahook

    2014-06-01

    Full Text Available Feature selection (FS is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on fuzzy similarity measures by multi objective genetic algorithm (FSFSM - MOGA is introduced and performance of the proposed method on published data sets from UCI was evaluated. The results show the efficiency of the method is compared with the conventional version. When this method multi-objective genetic algorithms and fuzzy similarity measures used in CFS method can improve it.

  10. MultiController: the PLC-SCADA object for advanced regulation

    CERN Document Server

    Ortola, J

    2007-01-01

    Nowadays, industrial solutions with PLCs (Programmable Logic Controllers) have basic control loop features. The SCADA (Supervisory Control And Data Acquisition) system is a key point of the process control system due to an efficient HMI (Human Machine Interfaces) that provides an open method of tuning and leading possibilities. As a consequence, advanced control algorithms have to be developed and implemented for those PLC-SCADA solutions in order to provide perspectives in solving complex and critical regulation problems. The MultiController is an object integrated for a large scale project at CERN (the European Organization for Nuclear Research) named LHC-GCS (Large Hadron Collider - Gas Control System). It is developed for a Framework called CERN-UNICOS based on PLC-SCADA facilities. The MultiController object offers various advanced control loop strategies. It gives to the user advanced control algorithms like PID, Smith Predictor, PFC, GPC and RST. It is implemented as a monolithic entity (in PLC and SCA...

  11. Multi-objective genetic algorithm for the optimization of a flat-plate solar thermal collector.

    Science.gov (United States)

    Mayer, Alexandre; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timoteo; Deparis, Olivier

    2014-10-20

    We present a multi-objective genetic algorithm we developed for the optimization of a flat-plate solar thermal collector. This collector consists of a waffle-shaped Al substrate with NiCrOx cermet and SnO(2) anti-reflection conformal coatings. Optimal geometrical parameters are determined in order to (i) maximize the solar absorptance α and (ii) minimize the thermal emittance ε. The multi-objective genetic algorithm eventually provides a whole set of Pareto-optimal solutions for the optimization of α and ε, which turn out to be competitive with record values found in the literature. In particular, a solution that enables α = 97.8% and ε = 4.8% was found.

  12. 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

    acids, while maintaining sufficiently high growth rate and minimizing the secretion of undesired byproducts. Homo-productions of acetic, lactic and succinic acids were targeted as examples. Engineered E. coli strains capable of producing homo-acetic and homo-lactic acids could be developed by taking......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...... this systems approach for the minimal identification of gene knockout targets. Also, failure to predict effective gene knockout targets for the homo-succinic acid production suggests that the multi-objective optimization is useful in assessing the suitability of a microorganism as a host strain...

  13. A performance-oriented power transformer design methodology using multi-objective evolutionary optimization

    OpenAIRE

    Adly, Amr A.; Abd-El-Hafiz, Salwa K.

    2014-01-01

    Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of tr...

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

    Directory of Open Access Journals (Sweden)

    Qing-chun Meng

    Full Text Available 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.

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

    OpenAIRE

    Zhou, Zhanhong; Cai, Ming

    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 ...

  16. 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.

  17. Evolutionary Multi-Objective Optimization of Trace Transform for Invariant Feature Extraction

    OpenAIRE

    Albukhanajer, WA; Jin, Y.; Briffa, JA; Williams, G

    2012-01-01

    Trace transform is one representation of images that uses different functionals applied on the image function. When the functional is integral, it becomes identical to the well-known Radon transform, which is a useful tool in computed tomography medical imaging. The key question in Trace transform is to select the best combination of the Trace functionals to produce the optimal triple feature, which is a challenging task. In this paper, we adopt a multi-objective evolutionary algorithm adapte...

  18. Artificial emotion triggered stochastic behavior transitions with motivational gain effects for multi-objective robot tasks

    Science.gov (United States)

    Dağlarli, Evren; Temeltaş, Hakan

    2007-04-01

    This paper presents artificial emotional system based autonomous robot control architecture. Hidden Markov model developed as mathematical background for stochastic emotional and behavior transitions. Motivation module of architecture considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors. Also motivational gain effects of proposed architecture can be observed on the executing behaviors during simulation.

  19. Multi-objective genetic algorithm for solving N-version program design problem

    Energy Technology Data Exchange (ETDEWEB)

    Yamachi, Hidemi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan) and Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamachi@nit.ac.jp; Tsujimura, Yasuhiro [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: tujimr@nit.ac.jp; Kambayashi, Yasushi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: yasushi@nit.ac.jp; Yamamoto, Hisashi [Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamamoto@cc.tmit.ac.jp

    2006-09-15

    N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost.

  20. Performance Analysis of a Hybrid Overset Multi-Block Application on Multiple Architectures

    Science.gov (United States)

    Djomehri, M. Jahed; Biswas, Rupak

    2003-01-01

    This paper presents a detailed performance analysis of a multi-block overset grid compu- tational fluid dynamics app!ication on multiple state-of-the-art computer architectures. The application is implemented using a hybrid MPI+OpenMP programming paradigm that exploits both coarse and fine-grain parallelism; the former via MPI message passing and the latter via OpenMP directives. The hybrid model also extends the applicability of multi-block programs to large clusters of SNIP nodes by overcoming the restriction that the number of processors be less than the number of grid blocks. A key kernel of the application, namely the LU-SGS linear solver, had to be modified to enhance the performance of the hybrid approach on the target machines. Investigations were conducted on cacheless Cray SX6 vector processors, cache-based IBM Power3 and Power4 architectures, and single system image SGI Origin3000 platforms. Overall results for complex vortex dynamics simulations demonstrate that the SX6 achieves the highest performance and outperforms the RISC-based architectures; however, the best scaling performance was achieved on the Power3.

  1. Development of multi-objective genetic algorithm concurrent subspace optimization (MOGACSSO) method with robustness

    Science.gov (United States)

    Parashar, Sumeet

    Most engineering design problems are complex and multidisciplinary in nature, and quite often require more than one objective (cost) function to be extremized simultaneously. For multi-objective optimization problems, there is not a single optimum solution, but a set of optimum solutions called the Pareto set. The primary goal of this research is to develop a heuristic solution strategy to enable multi-objective optimization of highly coupled multidisciplinary design applications, wherein each discipline is able to retain some degree of autonomous control during the process. To achieve this goal, this research extends the capability of the Multi-Objective Pareto Concurrent Subspace Optimization (MOPCSSO) method to generate large numbers of non-dominated solutions in each cycle, with subsequent update and refinement, thereby greatly increasing efficiency. While the conventional MOPCSSO approach is easily able to generate Pareto solutions, it will only generate one Pareto solution at a time. In order to generate the complete Pareto front, MOPCSSO requires multiple runs (translating into many system convergence cycles) using different initial staring points. In this research, a Genetic Algorithm-based heuristic solution strategy is developed for multi-objective problems in coupled multidisciplinary design. The Multi-Objective Genetic Algorithm Concurrent Subspace Optimization (MOGACSSO) method allows for the generation of relatively evenly distributed Pareto solutions in a faster and more efficient manner than repeated implementation of MOPCSSO. While achieving an optimum design, it is often also desirable that the optimum design be robust to uncontrolled parameter variations. In this research, the capability of the MOGACSSO method is also extended to generate Pareto points that are robust in terms of performance and feasibility, for given uncontrolled parameter variations. The Roust-MOGACSSO method developed in this research can generate a large number of designs

  2. Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System

    KAUST Repository

    Charara, Ali

    2014-11-01

    The European Extremely Large Telescope project (E-ELT) is one of Europe\\'s highest priorities in ground-based astronomy. ELTs are built on top of a variety of highly sensitive and critical astronomical instruments. In particular, a new instrument called MOSAIC has been proposed to perform multi-object spectroscopy using the Multi-Object Adaptive Optics (MOAO) technique. The core implementation of the simulation lies in the intensive computation of a tomographic reconstruct or (TR), which is used to drive the deformable mirror in real time from the measurements. A new numerical algorithm is proposed (1) to capture the actual experimental noise and (2) to substantially speed up previous implementations by exposing more concurrency, while reducing the number of floating-point operations. Based on the Matrices Over Runtime System at Exascale numerical library (MORSE), a dynamic scheduler drives all computational stages of the tomographic reconstruct or simulation and allows to pipeline and to run tasks out-of order across different stages on heterogeneous systems, while ensuring data coherency and dependencies. The proposed TR simulation outperforms asymptotically previous state-of-the-art implementations up to 13-fold speedup. At more than 50000 unknowns, this appears to be the largest-scale AO problem submitted to computation, to date, and opens new research directions for extreme scale AO simulations. © 2014 IEEE.

  3. DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm.

    Science.gov (United States)

    Chaves-González, José M; Vega-Rodríguez, Miguel A

    2014-02-01

    In this paper, we use an adapted multi-objective version of the differential evolution (DE) metaheuristics for the design and generation of reliable DNA libraries that can be used for computation. DNA sequence design is a very relevant task in many recent research fields, e.g. nanotechnology or DNA computing. Specifically, DNA computing is a new computational model which uses DNA molecules as information storage and their possible biological interactions as processing operators. Therefore, the possible reactions and interactions among molecules must be strictly controlled to prevent incorrect computations. The design of reliable DNA libraries for bio-molecular computing is an NP-hard combinatorial problem which involves many heterogeneous and conflicting design criteria. For this reason, we modelled DNA sequence design as a multiobjective optimization problem and we solved it by using an adapted multi-objective version of DE metaheuristics. Seven different bio-chemical design criteria have been simultaneously considered to obtain high quality DNA sequences which are suitable for molecular computing. Furthermore, we have developed the multiobjective standard fast non-dominated sorting genetic algorithm (NSGA-II) in order to perform a formal comparative study by using multi-objective indicators. Additionally, we have also compared our results with other relevant results published in the literature. We conclude that our proposal is a promising approach which is able to generate reliable real-world DNA sequences that significantly improve other DNA libraries previously published in the literature. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  4. Multidisciplinary design optimization of vehicle instrument panel based on multi-objective genetic algorithm

    Science.gov (United States)

    Wang, Ping; Wu, Guangqiang

    2013-03-01

    Typical multidisciplinary design optimization(MDO) has gradually been proposed to balance performances of lightweight, noise, vibration and harshness(NVH) and safety for instrument panel(IP) structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene(PP) under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment(DOE), response surface model(RSM), and collaborative optimization(CO) are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm(MOGA), the optimal Pareto sets are completed to solve the multi-objective optimization(MOO) problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.

  5. Multi-Objective Optimization of Spacecraft Trajectories for Small-Body Coverage Missions

    Science.gov (United States)

    Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren

    2017-01-01

    Visual coverage of surface elements of a small-body object requires multiple images to be taken that meet many requirements on their viewing angles, illumination angles, times of day, and combinations thereof. Designing trajectories capable of maximizing total possible coverage may not be useful since the image target sequence and the feasibility of said sequence given the rotation-rate limitations of the spacecraft are not taken into account. This work presents a means of optimizing, in a multi-objective manner, surface target sequences that account for such limitations.

  6. 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

    The objective of this paper is to investigate optimum process parameters in Friction Stir Welding (FSW) to minimize residual stresses in the work piece and maximize production efficiency meanwhile satisfying process specific constraints as well. More specifically, the choices of tool rotational...... speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2-dimensional sequentially coupled thermomechanical...

  7. Multi-objective Optimization of Process Parameters in Friction Stir Welding

    DEFF Research Database (Denmark)

    Tutum, Cem Celal; Hattel, Jesper Henri

    The objective of this paper is to investigate optimum process parameters in Friction Stir Welding (FSW) to minimize residual stresses in the work piece and maximize production efficiency meanwhile satisfying process specific constraints as well. More specifically, the choices of tool rotational...... speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2- dimensional sequentially coupled thermo...

  8. Novelty detection of foreign objects in food using multi-modal X-ray imaging

    DEFF Research Database (Denmark)

    Einarsdottir, Hildur; Emerson, Monica Jane; Clemmensen, Line Katrine Harder

    2016-01-01

    In this paper we demonstrate a method for novelty detection of foreign objects in food products using grating-based multimodal X-ray imaging. With this imaging technique three modalities are available with pixel correspondence, enhancing organic materials such as wood chips, insects and soft...... plastics not detectable by conventional X-ray absorption radiography. We conduct experiments, where several food products are imaged with common foreign objects typically found in the food processing industry. To evaluate the benefit from using this multi-contrast X-ray technique over conventional X...

  9. 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.

  10. Performance Optimizing Multi-Objective Adaptive Control with Time-Varying Model Reference Modification

    Science.gov (United States)

    Nguyen, Nhan T.; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

    2017-01-01

    This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.

  11. NARMAX model identification of a palm oil biodiesel engine using multi-objective optimization differential evolution

    Science.gov (United States)

    Mansor, Zakwan; Zakaria, Mohd Zakimi; Nor, Azuwir Mohd; Saad, Mohd Sazli; Ahmad, Robiah; Jamaluddin, Hishamuddin

    2017-09-01

    This paper presents the black-box modelling of palm oil biodiesel engine (POB) using multi-objective optimization differential evolution (MOODE) algorithm. Two objective functions are considered in the algorithm for optimization; minimizing the number of term of a model structure and minimizing the mean square error between actual and predicted outputs. The mathematical model used in this study to represent the POB system is nonlinear auto-regressive moving average with exogenous input (NARMAX) model. Finally, model validity tests are applied in order to validate the possible models that was obtained from MOODE algorithm and lead to select an optimal model.

  12. Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem

    DEFF Research Database (Denmark)

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

    2015-01-01

    Non-trivial real world decision-making processes usually involve multiple parties having potentially conflicting interests over a set of issues. State-of-the-art multi-objective evolutionary algorithms (MOEA) are well known to solve this class of complex real-world problems. In this paper, we...... compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...

  13. 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.

  14. 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...

  15. Project overview of OPTIMOS-EVE: the fibre-fed multi-object spectrograph for the E-ELT

    NARCIS (Netherlands)

    Navarro, R.; Chemla, F.; Bonifacio, P.; Flores, H.; Guinouard, I.; Huet, J.-M.; Puech, M.; Royer, F.; Pragt, J.H.; Wulterkens, G.; Sawyer, E.C.; Caldwell, M.E.; Tosh, I.A.J.; Whalley, M.S.; Woodhouse, G.F.W.; Spanò, P.; Di Marcantonio, P.; Andersen, M.I.; Dalton, G.B.; Kaper, L.; Hammer, F.

    2010-01-01

    OPTIMOS-EVE (OPTical Infrared Multi Object Spectrograph - Extreme Visual Explorer) is the fibre fed multi object spectrograph proposed for the European Extremely Large Telescope (E-ELT), planned to be operational in 2018 at Cerro Armazones (Chile). It is designed to provide a spectral resolution of

  16. A Novel Tunable Multi-Frequency Hybrid Vibration Energy Harvester Using Piezoelectric and Electromagnetic Conversion Mechanisms

    Directory of Open Access Journals (Sweden)

    Zhenlong Xu

    2016-01-01

    Full Text Available This paper presents a novel tunable multi-frequency hybrid energy harvester (HEH. It consists of a piezoelectric energy harvester (PEH and an electromagnetic energy harvester (EMEH, which are coupled with magnetic interaction. An electromechanical coupling model was developed and numerically simulated. The effects of magnetic force, mass ratio, stiffness ratio, and mechanical damping ratios on the output power were investigated. A prototype was fabricated and characterized by experiments. The measured first peak power increases by 16.7% and 833.3% compared with that of the multi-frequency EMEH and the multi-frequency PEH, respectively. It is 2.36 times more than the combined output power of the linear PEH and linear EMEH at 22.6 Hz. The half-power bandwidth for the first peak power is also broadened. Numerical results agree well with the experimental data. It is indicated that magnetic interaction can tune the resonant frequencies. Both magnetic coupling configuration and hybrid conversion mechanism contribute to enhancing the output power and widening the operation bandwidth. The magnitude and direction of magnetic force have significant effects on the performance of the HEH. This proposed HEH is an effective approach to improve the generating performance of the micro-scale energy harvesting devices in low-frequency range.

  17. A study on a multi-stage hybrid gasifier-engine system

    Energy Technology Data Exchange (ETDEWEB)

    Bhattacharya, S.C.; San Shwe Hla; Hoang-Luang Pham [Asian Institute of Technology, Pathumthani (Thailand). Energy Program

    2001-12-01

    This paper presents the results of a study on a multi-stage hybrid biomass-charcoal gasification to produce low tar content gas for engine application using coconut shell as a fuel. The performance of a gasifier-engine system consisting of the hybrid biomass-charcoal gasifier, a gas cleaning/cooling system and a diesel engine is also discussed. The lowest tar content found in hybrid coconut shell-charcoal gasification was 28 mgNm{sup -3}. Using a spray tower, producer gas could be cooled down to 40{sup o}C; almost tar-free gas was obtained after cooling the producer gas from the hybrid gasifier system. A three-cylinder Perkins diesel engine was tested at a constant speed of 1500 rpm on diesel alone and dual fuel modes of operation. A maximum of 81% of the total heat energy input was replaced by the producer gas at an electricity generation of 11.44 kWe. (author)

  18. Fabrication of hybrid molecular devices using multi-layer graphene break junctions.

    Science.gov (United States)

    Island, J O; Holovchenko, A; Koole, M; Alkemade, P F A; Menelaou, M; Aliaga-Alcalde, N; Burzurí, E; van der Zant, H S J

    2014-11-26

    We report on the fabrication of hybrid molecular devices employing multi-layer graphene (MLG) flakes which are patterned with a constriction using a helium ion microscope or an oxygen plasma etch. The patterning step allows for the localization of a few-nanometer gap, created by electroburning, that can host single molecules or molecular ensembles. By controlling the width of the sculpted constriction, we regulate the critical power at which the electroburning process begins. We estimate the flake temperature given the critical power and find that at low powers it is possible to electroburn MLG with superconducting contacts in close proximity. Finally, we demonstrate the fabrication of hybrid devices with superconducting contacts and anthracene-functionalized copper curcuminoid molecules. This method is extendable to spintronic devices with ferromagnetic contacts and a first step towards molecular integrated circuits.

  19. Control Systems for a Dynamic Multi-Physics Model of a Nuclear Hybrid Energy System

    Energy Technology Data Exchange (ETDEWEB)

    Greenwood, Michael Scott [ORNL; Fugate, David W [ORNL; Cetiner, Sacit M [ORNL

    2017-01-01

    A Nuclear Hybrid Energy System (NHES) uses a nuclear reactor as the basic power generation unit, and the power generated is used by multiple customers as either thermal power, electrical power, or both. The definition and architecture of a particular NHES can be adapted based on the needs and opportunities of different local markets. For example, locations in need of potable water may be best served by coupling a desalination plant to the NHES. Similarly, a location near oil refineries may have a need for emission-free hydrogen production. Using the flexible, multi-domain capabilities of Modelica, Argonne National Laboratory, Idaho National Laboratory, and Oak Ridge National Laboratory are investigating the dynamics (e.g., thermal hydraulics and electrical generation/consumption) and cost of a hybrid system. This paper examines the NHES work underway, emphasizing the control system developed for individual subsystems and the overall supervisory control system.

  20. Multi-Color Luminescence and Sensing of Rare Earth Hybrids by Ionic Exchange Modification.

    Science.gov (United States)

    Weng, Han; Yan, Bing

    2016-07-01

    Luminescent rare earth coordination polymers [H2NMe2]3[Y(DPA)3] ([H2NMe2](+) = dimethyl amino cation; H2DPA = 2,6-dipicolinic acid) are synthesized and is further modified by the ionic exchange reaction of [H2NMe2](+) cation with rare earth ions, which is named as RE(3+) ⊂ [Y(DPA)3] (RE = Eu, Tb, Sm, Dy) hybrid systems. The multi-color can be tuned for these functionalized hybrid systems and even white color luminescence can be integrated for Sm(3+) ⊂ [Y(DPA)3]. Besides, the fluorescent sensing property of Tb(3+) ⊂ [Y(DPA)3] system is checked, which shows high selectivity towards Cr(3+) with the concentration of 10(-5) mol⋅L(-1).

  1. ∊-constraint heat transfer search (∊-HTS algorithm for solving multi-objective engineering design problems

    Directory of Open Access Journals (Sweden)

    Mohamed A. Tawhid

    2018-01-01

    Full Text Available In this paper, an effective ∊-constraint heat transfer search (∊-HTS algorithm for the multi-objective engineering design problems is presented. This algorithm is developed to solve multi-objective optimization problems by evaluating a set of single objective sub-problems. The effectiveness of the proposed algorithm is checked by implementing it on multi-objective benchmark problems that have various characteristics of Pareto front such as discrete, convex, and non-convex. This algorithm is also tested for several distinctive multi-objective engineering design problems, such as four bar truss problem, gear train problem, multi-plate disc brake design, speed reducer problem, welded beam design, and spring design problem. Moreover, the numerical experimentation shows that the proposed algorithm generates the solution to represent true Pareto front.

  2. ASSESSMENT OF THE PRODUCTIBILITY OF HYBRID NODES USING THE MULTI-CRITERIA METHOD

    Directory of Open Access Journals (Sweden)

    Tomasz Urbański

    2014-06-01

    Full Text Available The article presents an assessment of the productibility of hybrid nodes. The hybrid node is a new structural element the implementation of which causes numerous (especially technological problems that need to be solved. The most important element of the hybrid node is the so-called connector, the choice of which is a complex and difficult problem. It involves taking into account many aspects (constructional, strength-related, technological, economic in order to make sure that the choice is as objective as possible. Therefore, an attempt to acquire a comprehensive view at the problem requires that a set of accurate criteria be used for assessment. In this paper, the author undertakes such an attempt. The expert method presented herein allows for choosing the right connector.

  3. Multi-objective optimisation for musculoskeletal modelling: application to a planar elbow model.

    Science.gov (United States)

    Dumas, Raphaël; Moissenet, Florent; Lafon, Yoann; Cheze, Laurence

    2014-10-01

    One of the open issues in musculoskeletal modelling remains the choice of the objective function that is used to solve the muscular redundancy problem. Some authors have recently proposed to introduce joint reaction forces in the objective function, and the question of the weights associated with musculo-tendon forces and joint reaction forces arose. This question typically deals with a multi-objective optimisation problem. The aim of this study is to illustrate, on a planar elbow model, the ensemble of optimal solutions (i.e. Pareto front) and the solution of a global objective method that represent different compromises between musculo-tendon forces, joint compression force, and joint shear force. The solutions of the global objective method, based either on the minimisation of the sum of the squared musculo-tendon forces alone or on the minimisation of the squared joint compression force and shear force together, are in the same range. Minimising either the squared joint compression force or shear force alone leads to extreme force values. The exploration of the compromises between these forces illustrates the existence of major interactions between the muscular and joint structures. Indeed, the joint reaction forces relate to the projection of the sum of the musculo-tendon forces. An illustration of these interactions, due to the projection relation, is that the Pareto front is not a large surface, like in a typical three-objective optimisation, but almost a curve. These interactions, and the possibility to take them into account by a multi-objective optimisation, seem essential for the application of musculoskeletal modelling to joint pathologies. © IMechE 2014.

  4. Multi-Objective Trajectory Planning of FFSM Carrying a Heavy Payload

    Directory of Open Access Journals (Sweden)

    Yong Liu

    2015-09-01

    Full Text Available Aiming at carrying a heavy payload to a desired pose (including position and orientation, a multi-objective optimization-based approach for maximum-payload trajectory planning of free-floating space manipulators (FFSM is proposed in this paper. The presented approach corresponds to two typical applications: (i the manipulator joints attain the desired states; (ii the inertial pose of the end-effector (pose with respect to the inertial frame attains the desired values, for which a novel two-stage method is presented. Firstly, for the purpose of reducing computational complexity, dynamics equations are derived using a spatial operator algebra (SOA method. Secondly, objective functions are defined according to the improvement of load-carrying capacity and pose requirements of the end-effector. Then, the joint trajectories are specified using sinusoidal polynomial functions. Finally, a multi-objective particle optimization (MOPSO algorithm is employed to obtain a non-dominated solution set, during which process particles that do not satisfy the constraints are eliminated. Simulations are performed for a 7-DOF FFSM, considering three and five objectives for optimization in the two applications, respectively. The results demonstrate that the proposed approach can provide satisfactory joint trajectories and improve load-carrying capacity effectively.

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

    Directory of Open Access Journals (Sweden)

    M. Amiri

    2012-10-01

    Full Text Available Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnover rate, Treynor index and Sharpe index. Semivariance is used as a downside risk measure. The proposed model of this paper uses fuzzy variables for return rate and semivariance. A multi-objective fuzzy mean-semivariance portfolio optimization model is implemented and fuzzy programming technique is adopted to solve the resulted problem. The proposed model of this paper has gathered the information of mutual fund traded on Nasdaq from 2007 to 2009 and Pareto optimal solutions are obtained considering different weights for objective functions. The results of asset allocation, rate of return and risk of each cluster are also determined and they are compared with the results of two clustering methods.

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

    Science.gov (United States)

    Li, Honglin; Zhang, Hailei; Zheng, Mingyue; Luo, Jie; Kang, Ling; Liu, Xiaofeng; Wang, Xicheng; Jiang, Hualiang

    2009-02-11

    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. 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. 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.

  7. 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.

  8. Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions.

    Science.gov (United States)

    Sweetapple, Christine; Fu, Guangtao; Butler, David

    2014-05-15

    This study investigates the potential of control strategy optimisation for the reduction of operational greenhouse gas emissions from wastewater treatment in a cost-effective manner, and demonstrates that significant improvements can be realised. A multi-objective evolutionary algorithm, NSGA-II, is used to derive sets of Pareto optimal operational and control parameter values for an activated sludge wastewater treatment plant, with objectives including minimisation of greenhouse gas emissions, operational costs and effluent pollutant concentrations, subject to legislative compliance. Different problem formulations are explored, to identify the most effective approach to emissions reduction, and the sets of optimal solutions enable identification of trade-offs between conflicting objectives. It is found that multi-objective optimisation can facilitate a significant reduction in greenhouse gas emissions without the need for plant redesign or modification of the control strategy layout, but there are trade-offs to consider: most importantly, if operational costs are not to be increased, reduction of greenhouse gas emissions is likely to incur an increase in effluent ammonia and total nitrogen concentrations. Design of control strategies for a high effluent quality and low costs alone is likely to result in an inadvertent increase in greenhouse gas emissions, so it is of key importance that effects on emissions are considered in control strategy development and optimisation. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics

    Directory of Open Access Journals (Sweden)

    Mirko M. Stojiljković

    2014-12-01

    Full Text Available In this paper, a methodology for multi-objective optimization of trigeneration plants is presented. It is primarily applicable to the systems for buildings’ energy supply characterized by high load variations on daily, weekly and annual bases, as well as the components applicable for flexible operation. The idea is that this approach should enable high accuracy and flexibility in mathematical modeling, while remaining efficient enough. The optimization problem is structurally decomposed into two new problems. The main problem of synthesis and design optimization is combinatorial and solved with different metaheuristic methods. For each examined combination of the synthesis and design variables, when calculating the values of the objective functions, the inner, mixed integer linear programming operation optimization problem is solved with the branch-and-cut method. The applicability of the exploited metaheuristic methods is demonstrated. This approach is compared with the alternative, superstructure-based approach. The potential for combining them is also examined. The methodology is applied for multi-objective optimization of a trigeneration plant that could be used for the energy supply of a real residential settlement in Niš, Serbia. Here, two objectives are considered: annual total costs and primary energy consumption. Results are obtained in the form of a Pareto chart using the epsilon-constraint method.

  10. Optimum analysis of pavement maintenance using multi-objective genetic algorithms

    Directory of Open Access Journals (Sweden)

    Amr A. Elhadidy

    2015-04-01

    Full Text Available Road network expansion in Egypt is considered as a vital issue for the development of the country. This is done while upgrading current road networks to increase the safety and efficiency. A pavement management system (PMS is a set of tools or methods that assist decision makers in finding optimum strategies for providing and maintaining pavements in a serviceable condition over a given period of time. A multi-objective optimization problem for pavement maintenance and rehabilitation strategies on network level is discussed in this paper. A two-objective optimization model considers minimum action costs and maximum condition for used road network. In the proposed approach, Markov-chain models are used for predicting the performance of road pavement and to calculate the expected decline at different periods of time. A genetic-algorithm-based procedure is developed for solving the multi-objective optimization problem. The model searched for the optimum maintenance actions at adequate time to be implemented on an appropriate pavement. Based on the computing results, the Pareto optimal solutions of the two-objective optimization functions are obtained. From the optimal solutions represented by cost and condition, a decision maker can easily obtain the information of the maintenance and rehabilitation planning with minimum action costs and maximum condition. The developed model has been implemented on a network of roads and showed its ability to derive the optimal solution.

  11. A new method for decision making in multi-objective optimization problems

    Directory of Open Access Journals (Sweden)

    Oscar Brito Augusto

    2012-08-01

    Full Text Available Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.

  12. Environment-Aware Production Schedulingfor Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach.

    Science.gov (United States)

    Zhang, Rui

    2017-12-25

    The traditional way of scheduling production processes often focuses on profit-driven goals (such as cycle time or material cost) while tending to overlook the negative impacts of manufacturing activities on the environment in the form of carbon emissions and other undesirable by-products. To bridge the gap, this paper investigates an environment-aware production scheduling problem that arises from a typical paint shop in the automobile manufacturing industry. In the studied problem, an objective function is defined to minimize the emission of chemical pollutants caused by the cleaning of painting devices which must be performed each time before a color change occurs. Meanwhile, minimization of due date violations in the downstream assembly shop is also considered because the two shops are interrelated and connected by a limited-capacity buffer. First, we have developed a mixed-integer programming formulation to describe this bi-objective optimization problem. Then, to solve problems of practical size, we have proposed a novel multi-objective particle swarm optimization (MOPSO) algorithm characterized by problem-specific improvement strategies. A branch-and-bound algorithm is designed for accurately assessing the most promising solutions. Finally, extensive computational experiments have shown that the proposed MOPSO is able to match the solution quality of an exact solver on small instances and outperform two state-of-the-art multi-objective optimizers in literature on large instances with up to 200 cars.

  13. Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

    Directory of Open Access Journals (Sweden)

    Jing Qiu

    2017-07-01

    Full Text Available This paper presents a multi-objective transmission expansion planning (TEP framework. Rather than using the conventional deterministic reliability criterion, a risk component based on the probabilistic reliability criterion is incorporated into the TEP objectives. This risk component can capture the stochastic nature of power systems, such as load and wind power output variations, component availability, and incentive-based demand response (IBDR costs. Specifically, the formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to conduct risk analysis. Thus, a final expansion plan can be selected according to individual risk preferences. Moreover, the economic value of IBDR is modeled and integrated into the cost objective. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and risk are provided. The proposed approach is numerically verified on the Garver’s six-bus, IEEE 24-bus RTS and Polish 2383-bus systems. Case study results demonstrate that the proposed approach can effectively reduce cost and hedge risk in relation to increasing wind power integration.

  14. The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem

    Science.gov (United States)

    Aytaç Adalı, Esra; Tuş Işık, Ayşegül

    2017-10-01

    A decision making process requires the values of conflicting objectives for alternatives and the selection of the best alternative according to the needs of decision makers. Multi-objective optimization methods may provide solution for this selection. In this paper it is aimed to present the laptop selection problem based on MOORA plus full multiplicative form (MULTIMOORA) and multi-objective optimization on the basis of simple ratio analysis (MOOSRA) which are relatively new multi-objective optimization methods. The novelty of this paper is solving this problem with the MULTIMOORA and MOOSRA methods for the first time.

  15. Multi-Objective Lead-Time Control Problem with Stochastic Constraints

    Directory of Open Access Journals (Sweden)

    M. Molavi

    2014-10-01

    Full Text Available This research intends to find out any development of a robust multi-objective for lead time optimal control problem in a multi-stage assembly system model. Assembly system modeling is possible by the help of the open queue network. A working station includes one or infinite servers and just manufacturing or assembly operations are performed therein. Each part has a separate entry process and independent of each other. It is completely based upon Poisson process.Serving Lead Time of Stations are also independent of each other and therefore exponential distribution of each parameter is controllable. All stations have bounded uncertain unrecyclable wastes which are completely independent in compliance with Erlang distribution. Uncertainty in the problem parameters has been suggested as robust multi-objective optimal control model in which we have three incompatible target functions including cyclic operation cost minimization, average lead time minimization and lead time variance. Finally, target progress method has been applied in order to achieve serving optimal speeds and solve discrete time of the main problem approximately. The proposed model could present a suitable solution even for the same problem as mentioned in other related papers along with some considerable results in parameter uncertainty conditions.

  16. Multi-objective evolutionary optimization for greywater reuse in municipal sewer systems.

    Science.gov (United States)

    Penn, Roni; Friedler, Eran; Ostfeld, Avi

    2013-10-01

    Sustainable design and implementation of greywater reuse (GWR) has to achieve an optimum compromise between costs and potable water demand reduction. Studies show that GWR is an efficient tool for reducing potable water demand. This study presents a multi-objective optimization model for estimating the optimal distribution of different types of GWR homes in an existing municipal sewer system. Six types of GWR homes were examined. The model constrains the momentary wastewater (WW) velocity in the sewer pipes (which is responsible for solids movement). The objective functions in the optimization model are the total WW flow at the outlet of the neighborhoods sewer system and the cost of the on-site GWR treatment system. The optimization routing was achieved by an evolutionary multi-objective optimization coupled with hydrodynamic simulations of a representative sewer system of a neighborhood located at the coast of Israel. The two non-dominated best solutions selected were the ones having either the smallest WW flow discharged at the outlet of the neighborhood sewer system or the lowest daily cost. In both solutions most of the GWR types chosen were the types resulting with the smallest water usage. This lead to only a small difference between the two best solutions, regarding the diurnal patterns of the WW flows at the outlet of the neighborhood sewer system. However, in the upstream link a substantial difference was depicted between the diurnal patterns. This difference occurred since to the upstream links only few homes, implementing the same type of GWR, discharge their WW, and in each solution a different type of GWR was implemented in these upstream homes. To the best of our knowledge this is the first multi-objective optimization model aimed at quantitatively trading off the cost of local/onsite GW spatially distributed reuse treatments, and the total amount of WW flow discharged into the municipal sewer system under unsteady flow conditions. Copyright © 2013

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

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    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.

  18. A novel hybrid Particle Swarm Optimizer with multi verse optimizer for global numerical optimization and Optimal Reactive Power Dispatch problem

    Directory of Open Access Journals (Sweden)

    Pradeep Jangir

    2017-04-01

    Full Text Available Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new technique hybrid Particle Swarm Optimization (PSO-Multi verse Optimizer (MVO is exercised on some unconstraint benchmark test functions and the most common problem of the modern power system named Optimal Reactive Power Dispatch (ORPD is optimized using the novel hybrid meta-heuristic optimization algorithm Particle Swarm Optimization-Multi Verse Optimizer (HPSO-MVO method. Hybrid PSO-MVO is combination of PSO used for exploitation phase and MVO for exploration phase in uncertain environment. Position and Speed of particle is modernised according to location of universes in each iteration. The hybrid PSO-MVO method has a fast convergence rate due to use of roulette wheel selection method. For the ORPD solution, standard IEEE-30 bus test system is used. The hybrid PSO-MVO method is implemented to solve the proposed problem. The problems considered in the ORPD are fuel cost reduction, Voltage profile improvement, Voltage stability enhancement, Active power loss minimization and Reactive power loss minimization. The results obtained with hybrid PSO-MVO method is compared with other techniques such as Particle Swarm Optimization (PSO and Multi Verse Optimizer (MVO. Analysis of competitive results obtained from HPSO-MVO validates its effectiveness compare to standard PSO and MVO algorithm.

  19. When the Lowest Energy Does Not Induce Native Structures: Parallel Minimization of Multi-Energy Values by Hybridizing Searching Intelligences

    Science.gov (United States)

    Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

    2012-01-01

    Background Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. Results A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. Conclusions This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise. PMID:23028708

  20. Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty

    Directory of Open Access Journals (Sweden)

    Sonia Irshad Mari

    2016-10-01

    Full Text Available Researchers and practitioners are taking more interest in developing sustainable garment supply chains in recent times. On the other hand, the supply chain manager drops sustainability objectives while coping with unexpected natural and man-made disruption risks. Hence, supply chain managers are now trying to develop sustainable supply chains that are simultaneously resilient enough to cope with disruption risks. Owing to the importance of the considered issue, this study proposed a network optimization model for a sustainable and resilient supply chain network by considering sustainability via embodied carbon footprints and carbon emissions and resilience by considering resilience index. In this paper, initially, a possibilistic fuzzy multi-objective sustainable and resilient supply chain network model is developed for the garment industry considering economic, sustainable, and resilience objectives. Secondly, a possibilistic fuzzy linguistic weight-based interactive solution method is proposed. Finally, a numerical case example is presented to show the applicability of the proposed model and solution methodology.

  1. Optimum design of pultrusion process via evolutionary multi-objective optimization

    DEFF Research Database (Denmark)

    Tutum, Cem Celal; Baran, Ismet; Deb, Kalyanmoy

    2014-01-01

    with a well-known EMO algorithm, i.e., nondominated sorting genetic algorithm (NSGA-II), to simultaneously maximize the pulling speed and minimize “total energy consumption” (TEC) which is defined as a measure of total heating area(s) and associated temperature(s). Finally, the results of the evolutionary...... is “cheap” and therefore is an attractive and efficient tool for autonomous (numerical) optimization. Optimization problems in engineering in general comprise multiple objectives often having conflict with each other. Evolutionary multi-objective optimization (EMO) algorithms provide an ideal way of solving...... computation step is used as starting guesses for a serial application of a of gradient-based classical algorithm to improve the convergence. As a result, a set of optimal solutions are obtained for different trade-offs between the conflicting objectives. The trade-off solution, thus obtained, would remain...

  2. Multi-objective flexible job shop scheduling problem using variable neighborhood evolutionary algorithm

    Science.gov (United States)

    Wang, Chun; Ji, Zhicheng; Wang, Yan

    2017-07-01

    In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.

  3. A multi-objective approach to the assignment of stock keeping units to unidirectional picking lines

    Directory of Open Access Journals (Sweden)

    Le Roux, G. J.

    2017-05-01

    Full Text Available An order picking system in a distribution centre consisting of parallel unidirectional picking lines is considered. The objectives are to minimise the walking distance of the pickers, the largest volume of stock on a picking line over all picking lines, the number of small packages, and the total penalty incurred for late distributions. The problem is formulated as a multi-objective multiple knapsack problem that is not solvable in a realistic time. Population-based algorithms, including the artificial bee colony algorithm and the genetic algorithm, are also implemented. The results obtained from all algorithms indicate a substantial improvement on all objectives relative to historical assignments. The genetic algorithm delivers the best performance.

  4. Discriminant Feature Selection by Genetic Programming: Towards a domain independent multi-class object detection system.

    Directory of Open Access Journals (Sweden)

    Jacques-André Landry

    2005-02-01

    Full Text Available In order to implement a multi-class object detection system, an efficient object representation is needed; in this short paper, we present a feature selection method based on the Genetic Algorithm paradigm. This method allows for the identification of a set of features that best represent the classes in the problem at hand. The idea would then be to have a broad set of features to describe any object, and then to use the presented feature selection method to adapt the description to the actual needs of the classification problem. Furthermore, the tree like solutions generated by the method can be interpreted and modified for increased generality. A brief review of literature, the first implementation of the method and the first results are presented here. The method shows potential to be used as a building block of a detection system, although further experimentation is underway in order to fully asses the power of the method.

  5. Power control method on VSC-HVDC in a hybrid multi-infeed HVDC system

    DEFF Research Database (Denmark)

    Liu, Yan; Chen, Zhe

    2012-01-01

    Multi-infeed HVDC (MIDC) system connected with VSC-HVDC links and LCC-HVDC links is a new structure in modern power systems, which can be called hybrid multi-infeed HVDC (HMIDC) system. The paper presents the voltage stability analysis of a HMIDC system modeled from a possible future Danish power...... stability of the system with induction machine loads. The proposed power control method based on a VSC power circle makes fully use of the VSC-HVDC capacity to support system voltage during fault situations. Simulation model of the studied HMIDC system is built in PSCAD. Simulation studies under different...... system and proposes a power control method to improve the system voltage stability. Dynamic loads are considered in the studied system and represented by induction machines. The paper analyses voltage recovery process of an induction machine during a fault and gives the necessary condition of the voltage...

  6. Mechanism of Methylene Blue adsorption on hybrid laponite-multi-walled carbon nanotube particles.

    Science.gov (United States)

    Manilo, Maryna; Lebovka, Nikolai; Barany, Sandor

    2016-04-01

    The kinetics of adsorption and parameters of equilibrium adsorption of Methylene Blue (MB) on hybrid laponite-multi-walled carbon nanotube (NT) particles in aqueous suspensions were determined. The laponite platelets were used in order to facilitate disaggregation of NTs in aqueous suspensions and enhance the adsorption capacity of hybrid particles for MB. Experiments were performed at room temperature (298 K), and the laponite/NT ratio (Xl) was varied in the range of 0-0.5. For elucidation of the mechanism of MB adsorption on hybrid particles, the electrical conductivity of the system as well as the electrokinetic potential of laponite-NT hybrid particles were measured. Three different stages in the kinetics of adsorption of MB on the surface of NTs or hybrid laponite-NT particles were discovered to be a fast initial stage I (adsorption time t=0-10 min), a slower intermediate stage II (up to t=120 min) and a long-lasting final stage III (up to t=24hr). The presence of these stages was explained accounting for different types of interactions between MB and adsorbent particles, as well as for the changes in the structure of aggregates of NT particles and the long-range processes of restructuring of laponite platelets on the surface of NTs. The analysis of experimental data on specific surface area versus the value of Xl evidenced in favor of the model with linear contacts between rigid laponite platelets and NTs. It was also concluded that electrostatic interactions control the first stage of adsorption at low MB concentrations. Copyright © 2015. Published by Elsevier B.V.

  7. Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion

    Science.gov (United States)

    Budavári, Tamás; Szalay, Alexander S.; Loredo, Thomas J.

    2017-03-01

    Astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection and describes an incremental strategy for separating real objects from artifacts in ongoing surveys. The idea is to produce low-threshold single-epoch catalogs and to accumulate information across epochs. This is in contrast to more conventional strategies based on co-added or stacked images. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting that there is no object or one object in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a Gaussian noise setting, comparing results based on single and stacked exposures to results based on a series of single-epoch catalog summaries. Our procedure amounts to generalized cross-matching: it is the product of a factor accounting for the matching of the estimated fluxes of the candidate sources and a factor accounting for the matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalogs can detect sources with similar sensitivity and selectivity compared to stacking. The probabilistic cross-matching framework underlying our approach plays an important role in maintaining detection sensitivity and points toward generalizations that could accommodate variability and complex object structure.

  8. The Improved SVM Multi Objects' Identification For the Uncalibrated Visual Servoing

    Directory of Open Access Journals (Sweden)

    Min Wang

    2009-03-01

    Full Text Available For the assembly of multi micro objects in micromanipulation, the first task is to identify multi micro parts. We present an improved support vector machine algorithm, which employs invariant moments based edge extraction to obtain feature attribute and then presents a heuristic attribute reduction algorithm based on rough set's discernibility matrix to obtain attribute reduction, with using support vector machine to identify and classify the targets. The visual servoing is the second task. For avoiding the complicated calibration of intrinsic parameter of camera, We apply an improved broyden's method to estimate the image jacobian matrix online, which employs chebyshev polynomial to construct a cost function to approximate the optimization value, obtaining a fast convergence for online estimation. Last, a two DOF visual controller based fuzzy adaptive PD control law for micro-manipulation is presented. The experiments of micro-assembly of micro parts in microscopes confirm that the proposed methods are effective and feasible.

  9. Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

    Directory of Open Access Journals (Sweden)

    Syed Ali Abbas Kazmi

    2017-02-01

    Full Text Available Distribution networks (DNWs are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN needs to consider choices, primarily distributed generation (DG, network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs based on mult-objective optimizations (MOOs in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at

  10. Behavioral dynamics and neural grounding of a dynamic field theory of multi-object tracking.

    Science.gov (United States)

    Spencer, J P; Barich, K; Goldberg, J; Perone, S

    2012-09-01

    The ability to dynamically track moving objects in the environment is crucial for efficient interaction with the local surrounds. Here, we examined this ability in the context of the multi-object tracking (MOT) task. Several theories have been proposed to explain how people track moving objects; however, only one of these previous theories is implemented in a real-time process model, and there has been no direct contact between theories of object tracking and the growing neural literature using ERPs and fMRI. Here, we present a neural process model of object tracking that builds from a Dynamic Field Theory of spatial cognition. Simulations reveal that our dynamic field model captures recent behavioral data examining the impact of speed and tracking duration on MOT performance. Moreover, we show that the same model with the same trajectories and parameters can shed light on recent ERP results probing how people distribute attentional resources to targets vs. distractors. We conclude by comparing this new theory of object tracking to other recent accounts, and discuss how the neural grounding of the theory might be effectively explored in future work.

  11. An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

    Directory of Open Access Journals (Sweden)

    Xuanhu He

    2015-03-01

    Full Text Available Optimal power flow (OPF objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.

  12. Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks

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    Zhi Chen

    2014-10-01

    Full Text Available Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms.

  13. Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks

    Science.gov (United States)

    Chen, Zhi; Li, Shuai; Yue, Wenjing

    2014-01-01

    Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms. PMID:25360579

  14. Multi-objective optimization to support mission planning for constellations of unmanned aerial systems

    Science.gov (United States)

    Tenenbaum, S.; Stouch, D.; McGraw, K.; Fichtl, T.

    2008-04-01

    Unmanned aerial vehicles (UAVs) have proven themselves indispensable in providing intelligence, reconnaissance, and surveillance (ISR). We foresee a future where constellations of multi-purpose UAVs will be tasked to provide ISR in an unpredictable environment. Automated systems will process imagery and other sensor data gathered by the constellations to provide continuous situational awareness for the warfighter on the ground. In this paper, we present a tool that generates coordinated mission plans for constellations of UAVs with multiple goals and objectives. We call this tool Spatially Produced Airspace Routes from Tactical Evolved Networks, or SPARTEN. SPARTEN uses evolutionary algorithm (EA)-based, multi-objective optimization to generate coordinated sortie routes for constellations of UAVs. These sortie routes maximize sensor coverage, avoid conflicts between UAVs, minimize the latency of sensor data, and avoid areas of poor weather to provide valid route solutions. We use an Air Maneuver Network (AMN) based on terrain reasoning to constrain the solution space. We make two contributions to the field of UAV route planning. We develop a tool to optimize planning across multiple objectives for constellations of UAVs, and we explore the performance of this tool on a battlefield scenario.

  15. A Procedure to Perform Multi-Objective Optimization for Sustainable Design of Buildings

    Directory of Open Access Journals (Sweden)

    Cristina Brunelli

    2016-11-01

    Full Text Available When dealing with sustainable design concepts in new construction or in retrofitting existing buildings, it is useful to define both economic and environmental performance indicators, in order to select the optimal technical solutions. In most of the cases, the definition of the optimal strategy is not trivial because it is necessary to solve a multi-objective problem with a high number of the variables subjected to nonlinear constraints. In this study, a powerful multi-objective optimization genetic algorithm, NSGAII (Non-dominated Sorting Genetic Algorithm-II, is used to derive the Pareto optimal solutions, which can illustrate the whole trade-off relationship between objectives. A method is then proposed, to introduce uncertainty evaluation in the optimization procedure. A new university building is taken as a case study to demonstrate how each step of the optimization process should be performed. The results achieved turn out to be reliable and show the suitableness of this procedure to define both economic and environmental performance indicators. Similar analysis on a set of buildings representatives of a specific region might be used to assist local/national administrations in the definition of appropriate legal limits that will permit a strategic optimized extension of renewable energy production. Finally, the proposed approach could be applied to similar optimization models for the optimal planning of sustainable buildings, in order to define the best solutions among non-optimal ones.

  16. A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem

    Directory of Open Access Journals (Sweden)

    J. S. Sadaghiani

    2014-04-01

    Full Text Available Flexible job shop scheduling problem is a key factor of using efficiently in production systems. This paper attempts to simultaneously optimize three objectives including minimization of the make span, total workload and maximum workload of jobs. Since the multi objective flexible job shop scheduling problem is strongly NP-Hard, an integrated heuristic approach has been used to solve it. The proposed approach was based on a floating search procedure that has used some heuristic algorithms. Within floating search procedure utilize local heuristic algorithms; it makes the considered problem into two sections including assigning and sequencing sub problem. First of all search is done upon assignment space achieving an acceptable solution and then search would continue on sequencing space based on a heuristic algorithm. This paper has used a multi-objective approach for producing Pareto solution. Thus proposed approach was adapted on NSGA II algorithm and evaluated Pareto-archives. The elements and parameters of the proposed algorithms were adjusted upon preliminary experiments. Finally, computational results were used to analyze efficiency of the proposed algorithm and this results showed that the proposed algorithm capable to produce efficient solutions.

  17. 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.

  18. Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP

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    Hisham A. Shehadeh

    2017-10-01

    Full Text Available In this paper, we propose an extended multi-objective version of single objective optimization algorithm called sperm swarm optimization algorithm. The proposed multi-objective optimization algorithm based on sperm fertilization procedure (MOSFP operates based on Pareto dominance and a crowding factor, that crowd and filter out the list of the best sperms (global best values. We divide the sperm swarm into three equal parts, after that, different types of turbulence (mutation operators are applied on these parts, such as uniform mutation, non-uniform mutation, and without any mutation. Our algorithm is compared against three well-known algorithms in the field of optimization. These algorithms are NSGA-II, SPEA2, and OMOPSO. These algorithms are compared using a very popular benchmark function suites called Zitzler-Deb-Thiele (ZDT and Walking-Fish-Group (WFG. We also adopt three quality metrics to compare the convergence, accuracy, and diversity of these algorithms, including, inverted generational distance (IGD, spread (SP, and epsilon (∈. The experimental results show that the performance of the proposed MOSFP is highly competitive, which outperformed OMOPSO in solving problems such as ZDT3, WFG5, and WFG8. In addition, the proposed MOSFP outperformed both of NSGA-II or SPEA2 algorithms in solving all the problems.

  19. Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

    Science.gov (United States)

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

    2016-08-15

    Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades

    Directory of Open Access Journals (Sweden)

    Jie Zhu

    2017-01-01

    Full Text Available A procedure based on MATLAB combined with ANSYS is presented and utilized for the multi-objective aerodynamic and structural optimization of horizontal-axis wind turbine (HAWT blades. In order to minimize the cost of energy (COE and improve the overall performance of the blades, materials of carbon fiber reinforced plastic (CFRP combined with glass fiber reinforced plastic (GFRP are applied. The maximum annual energy production (AEP, the minimum blade mass and the minimum blade cost are taken as three objectives. Main aerodynamic and structural characteristics of the blades are employed as design variables. Various design requirements including strain, deflection, vibration and buckling limits are taken into account as constraints. To evaluate the aerodynamic performances and the structural behaviors, the blade element momentum (BEM theory and the finite element method (FEM are applied in the procedure. Moreover, the non-dominated sorting genetic algorithm (NSGA II, which constitutes the core of the procedure, is adapted for the multi-objective optimization of the blades. To prove the efficiency and reliability of the procedure, a commercial 1.5 MW HAWT blade is used as a case study, and a set of trade-off solutions is obtained. Compared with the original scheme, the optimization results show great improvements for the overall performance of the blade.

  1. Remote sensing imagery classification using multi-objective gravitational search algorithm

    Science.gov (United States)

    Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

    2016-10-01

    Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

  2. A sustainable manufacturing system design: A fuzzy multi-objective optimization model.

    Science.gov (United States)

    Nujoom, Reda; Mohammed, Ahmed; Wang, Qian

    2017-08-10

    In the past decade, there has been a growing concern about the environmental protection in public society as governments almost all over the world have initiated certain rules and regulations to promote energy saving and minimize the production of carbon dioxide (CO 2 ) emissions in many manufacturing industries. The development of sustainable manufacturing systems is considered as one of the effective solutions to minimize the environmental impact. Lean approach is also considered as a proper method for achieving sustainability as it can reduce manufacturing wastes and increase the system efficiency and productivity. However, the lean approach does not include environmental waste of such as energy consumption and CO 2 emissions when designing a lean manufacturing system. This paper addresses these issues by evaluating a sustainable manufacturing system design considering a measurement of energy consumption and CO 2 emissions using different sources of energy (oil as direct energy source to generate thermal energy and oil or solar as indirect energy source to generate electricity). To this aim, a multi-objective mathematical model is developed incorporating the economic and ecological constraints aimed for minimization of the total cost, energy consumption, and CO 2 emissions for a manufacturing system design. For the real world scenario, the uncertainty in a number of input parameters was handled through the development of a fuzzy multi-objective model. The study also addresses decision-making in the number of machines, the number of air-conditioning units, and the number of bulbs involved in each process of a manufacturing system in conjunction with a quantity of material flow for processed products. A real case study was used for examining the validation and applicability of the developed sustainable manufacturing system model using the fuzzy multi-objective approach.

  3. Multi objectives model to optimise the economical value of agriculture water use in Gaza Strip

    Science.gov (United States)

    Ouda, O.; Bardossy, A.

    2003-04-01

    Multi objectives model to optimise the economical value of agriculture water use in Gaza Strip. O. Ouda (1), A. Bárdossy (1) (1) Institut fuer Wasserbau, Universitaet Stuttgart Fax: +49-(0)711-685-4746/ e-mail: omar.ouda@iws.uni-stuttgart.de Key words: Multi objectives model, agriculture water use, and Gaza Strip. ============================================================================ Abstract The Gaza Strip faces a serious water shortage problem, with a present water shortage of about 61 Mm3/year. The problem is projected to become even larger in the future due to a high population growth of about 3.2%. The water deficit is presently covered by abstraction of the groundwater beyond the sustainable yield, where groundwater is the only natural source in Gaza strip. Irrigated agriculture consumed about 60% (90 Mm3/year) of water in Gaza strip. The economical value of water used for agriculture propose is very low in comparison with water opportunity cost of 1 US/m3 , ( seawater desalination cost). A Multi objective optimisation model (MOM) based on mathematical programming techniques aimed to optimise the economical return value of agriculture water use has been formulated, where 20 crops distributed over 16 zones have been considered. The available agriculture area, Available treated wastewater, Local agriculture products demand were considered as constrains. Irrigation water demand for each crop for three meteorological conditions dry, wet and average year, and Average product prices were considered as variables. A modification of the MOM models has been made toward equitable profit distribution (US/hectare) among the different 16 zones, where additional constrain of minimum profit per hectare in each zone has been implemented. Finally a sensitivity analysis for the effect of water price, crop price and crop products demand on the model output has been made. The MOM presents a good analytical basis for policy makers toward optimising the economical return of

  4. A performance-oriented power transformer design methodology using multi-objective evolutionary optimization.

    Science.gov (United States)

    Adly, Amr A; Abd-El-Hafiz, Salwa K

    2015-05-01

    Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper.

  5. A performance-oriented power transformer design methodology using multi-objective evolutionary optimization

    Directory of Open Access Journals (Sweden)

    Amr A. Adly

    2015-05-01

    Full Text Available Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper.

  6. 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...

  7. Employing multi-objective Genetic Programming to the downscaling of near-surface atmospheric fields

    Science.gov (United States)

    Zerenner, Tanja; Venema, Victor; Friederichs, Petra; Simmer, Clemens

    2015-04-01

    The coupling of models for the different components of the Soil-Vegetation-Atmosphere-System is required to investigate component interactions and feedback processes. However, the component models for atmosphere, land-surface and subsurface are usually operated at different resolutions in space and time owing to the dominant processes. The computationally expensive atmospheric models are typically employed at a coarser resolution than land-surface and subsurface models. Thus up- and downscaling procedures are required at the interface between the atmospheric model and the land-surface/subsurface models. We apply multi-objective Genetic Programming (GP) to a training data set of high-resolution atmospheric model runs to learn downscaling rules, i. e., equations or short programs that reconstruct the fine-scale fields of the near-surface atmospheric state variables from the coarse atmospheric model output. Like artificial neural networks, GP can flexibly incorporate multivariate and nonlinear relations, but offers the advantage that the solutions are human readable and thus can be checked for physical consistency. Further, the Strength Pareto Approach for multi-objective fitness assignment allows to consider multiple characteristics of the fine-scale fields during the learning procedure. We have applied the described machine learning methodology to a training data set of 400 m resolution COSMO model runs to learn downscaling rules which recover realistic fine-scale structures from the coarsened fields at 2.8 km resolution. Hence we are currently downscaling by a factor of 7. The COSMO model is the weather forecast model developed and maintained by the German Weather Service and is contained in the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the atmospheric COSMO model to land-surface model CLM and subsurface hydrological model ParFlow. Finally we aim at implementing the learned downscaling rules in the TerrSysMP to achieve scale

  8. Geometrical exploration of a flux-optimised sodium receiver through multi-objective optimisation

    Science.gov (United States)

    Asselineau, Charles-Alexis; Corsi, Clothilde; Coventry, Joe; Pye, John

    2017-06-01

    A stochastic multi-objective optimisation method is used to determine receiver geometries with maximum second law efficiency, minimal average temperature and minimal surface area. The method is able to identify a set of Pareto optimal candidates that show advantageous geometrical features, mainly in being able to maximise the intercepted flux within the geometrical boundaries set. Receivers with first law thermal efficiencies ranging from 87% to 91% are also evaluated using the second law of thermodynamics and found to have similar efficiencies of over 60%, highlighting the influence that the geometry can play in the maximisation of the work output of receivers by influencing the distribution of the flux from the concentrator.

  9. Critical Comparison of Multi-objective Optimization Methods: Genetic Algorithms versus Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    V. Sedenka

    2010-09-01

    Full Text Available The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II and a novel multi-objective Particle Swarm Optimization (PSO. The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator is modeled using the Finite Element Method (FEM. The hit rate and the quality of the Pareto front distribution are classified.

  10. MOSAIC: A Multi-Object Spectrograph for the E-ELT

    Science.gov (United States)

    Kelz, A.; Hammer, F.; Jagourel, P.; MOSAIC Consortium

    2016-10-01

    The instrumentation plan for the European Extremely Large Telescope foresees a Multi-Object Spectrograph (E-ELT MOS). The MOSAIC project is proposed by a European-Brazilian consortium, to provide a unique MOS facility for astrophysics, studies of the inter-galactic medium and for cosmology. The science cases range from spectroscopy of the most distant galaxies, mass assembly and evolution of galaxies, via resolved stellar populations and galactic archaeology, to planet formation studies. A further strong driver is spectroscopic follow-up observations of targets that will be discovered with the James Webb Space Telescope.

  11. A novel scheme in audio watermarking using multi-objective genetic algorithm

    Science.gov (United States)

    Majid, Abdul; Javed, Tahir; Choi, Tae-Sun

    2010-01-01

    In this paper, multi-objective genetic algorithm (MOGA) based novel watermarking scheme of audio signal is proposed. Using this MOGA scheme small size images are embedded efficiently as a digital watermark in the discrete wavelet domain of audio signal. The main advantage of proposed scheme is that it automatically selects the intensity of watermark for embedding in audio signal. As a result an optimal tradeoffs is obtained between two contradicting properties i.e. robustness and imperceptibility. The results obtained using the proposed MOGA technique show the embedded watermark is more robustness against common cropping and Gaussian noise attacks.

  12. Galaxy and Mass Assembly (GAMA): Optimal Tiling of Dense Surveys with a Multi-Object Spectrograph

    Science.gov (United States)

    Robotham, A.; Driver, S. P.; Norberg, P.; Baldry, I. K.; Bamford, S. P.; Hopkins, A. M.; Liske, J.; Loveday, J.; Peacock, J. A.; Cameron, E.; Croom, S. M.; Doyle, I. F.; Frenk, C. S.; Hill, D. T.; Jones, D. H.; van Kampen, E.; Kelvin, L. S.; Kuijken, K.; Nichol, R. C.; Parkinson, H. R.; Popescu, C. C.; Prescott, M.; Sharp, R. G.; Sutherland, W. J.; Thomas, D.; Tuffs, R. J.

    2010-03-01

    A heuristic greedy algorithm is developed for efficiently tiling spatially dense redshift surveys. In its first application to the Galaxy and Mass Assembly (GAMA) redshift survey we find it rapidly improves the spatial uniformity of our data, and naturally corrects for any spatial bias introduced by the 2dF multi-object spectrograph. We make conservative predictions for the final state of the GAMA redshift survey after our final allocation of time, and can be confident that even if worse than typical weather affects our observations, all of our main survey requirements will be met.

  13. Multi-objective experimental design for (13)C-based metabolic flux analysis.

    Science.gov (United States)

    Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel

    2015-10-01

    (13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective

  14. Modelling and multi objective optimization of laser peening process using Taguchi utility concept

    Science.gov (United States)

    Ranjith Kumar, G.; Rajyalakshmi, G.

    2017-11-01

    Laser peening is considered as one of the innovative surface treatment technique. This work focuses on determining the optimal peening parameters for finding optimal responses like residual stresses and deformation. The modelling was done using ANSYS and values are optimised using Taguchi Utility concept for simultaneous optimization of responses. Three parameters viz. overlap; Pulse duration and Pulse density are considered as process parameters for modelling and optimization. Through Multi objective optimization, it is showing that Overlap is showing maximum influence on Stress and deformation followed by Power density and pulse duration.

  15. Multi-objective Emergency Facility Location Problem Based on Genetic Algorithm

    Science.gov (United States)

    Zhao, Dan; Zhao, Yunsheng; Li, Zhenhua; Chen, Jin

    Recent years, emergent disasters have occurred frequently. This has attracted more attention on emergency management, especially the multi-objective emergency facility location problem (EFLP), a NP problem. However, few algorithms are efficient to solve the probleme and so the application of genetic algorithm (GA) can be a good choice. This paper first introduces the mathematical models for this problem and transforms it from complex constraints into simple constraints by punishment function. The solutions to the experiments are obtained by applying GA. The experiment results show that GA could solve the problems effectively.

  16. Multi-loop control strategy of a solid oxide fuel cell and micro gas turbine hybrid system

    Science.gov (United States)

    Wu, Xiao-Juan; Zhu, Xin-Jian

    2011-10-01

    Solid oxide fuel cell and micro gas turbine (SOFC/MGT) hybrid system is a promising distributed power technology. In order to ensure the system safe operation as well as long lifetime of the fuel cell, an effective control manner is expected to regulate the temperature and fuel utilization at the desired level, and track the desired power output. Thus, a multi-loop control strategy for the hybrid system is investigated in this paper. A mathematical model for the SOFC/MGT hybrid system is built firstly. Based on the mathematical model, control cycles are introduced and their design is discussed. Part load operation condition is employed to investigate the control strategies for the system. The dynamic modeling and control implementation are realized in the MATLAB/SIMULINK environment, and the simulation results show that it is feasible to build the multi-loop control methods for the SOFC/MGT hybrid system with regard to load disturbances.

  17. High-Capacitance Hybrid Supercapacitor Based on Multi-Colored Fluorescent Carbon-Dots.

    Science.gov (United States)

    Genc, Rukan; Alas, Melis Ozge; Harputlu, Ersan; Repp, Sergej; Kremer, Nora; Castellano, Mike; Colak, Suleyman Gokhan; Ocakoglu, Kasim; Erdem, Emre

    2017-09-11

    Multi-colored, water soluble fluorescent carbon nanodots (C-Dots) with quantum yield changing from 4.6 to 18.3% were synthesized in multi-gram using dated cola beverage through a simple thermal synthesis method and implemented as conductive and ion donating supercapacitor component. Various properties of C-Dots, including size, crystal structure, morphology and surface properties along with their Raman and electron paramagnetic resonance spectra were analyzed and compared by means of their fluorescence and electronic properties. α-Manganese Oxide-Polypyrrole (PPy) nanorods decorated with C-Dots were further conducted as anode materials in a supercapacitor. Reduced graphene oxide was used as cathode along with the dicationic bis-imidazolium based ionic liquid in order to enhance the charge transfer and wetting capacity of electrode surfaces. For this purpose, we used octyl-bis(3-methylimidazolium)diiodide (C8H16BImI) synthesized by N-alkylation reaction as liquid ionic membrane electrolyte. Paramagnetic resonance and impedance spectroscopy have been undertaken in order to understand the origin of the performance of hybrid capacitor in more depth. In particular, we obtained high capacitance value (C = 17.3 μF/cm2) which is exceptionally related not only the quality of synthesis but also the choice of electrode and electrolyte materials. Moreover, each component used in the construction of the hybrid supercapacitor is also played a key role to achieve high capacitance value.

  18. Ground-based multi-color photometry of the γ Doradus-δ Scuti hybrid star KIC 6761539

    Science.gov (United States)

    Herzberg, W.; Uytterhoeven, K.; Roth, M.

    2012-12-01

    We present a preliminary analysis of the first three nights of multi-color photometric data for a γ Doradus-δ Scuti hybrid star (KIC 6761539) that is also being observed with the Kepler space telescope. We find that up to four (depending on the filter) of the highest amplitude modes, whose frequencies could be determined from Kepler data, are visible from the ground. Our goal is to use the multi-color information for mode identification, but this will only be possible with a longer time series. A multi-color photometric multi-site campaign is currently ongoing for this purpose.

  19. A versatile nanotechnology to connect individual nano-objects for the fabrication of hybrid single-electron devices

    Science.gov (United States)

    Bernand-Mantel, A.; Bouzehouane, K.; Seneor, P.; Fusil, S.; Deranlot, C.; Brenac, A.; Notin, L.; Morel, R.; Petroff, F.; Fert, A.

    2010-11-01

    We report on the high yield connection of single nano-objects as small as a few nanometres in diameter to separately elaborated metallic electrodes, using a 'table-top' nanotechnology. Single-electron transport measurements validate that transport occurs through a single nano-object. The vertical geometry of the device natively allows an independent choice of materials for each electrode and the nano-object. In addition ferromagnetic materials can be used without encountering oxidation problems. The possibility of elaborating such hybrid nanodevices opens new routes for the democratization of spintronic studies in low dimensions.

  20. A versatile nanotechnology to connect individual nano-objects for the fabrication of hybrid single-electron devices

    Energy Technology Data Exchange (ETDEWEB)

    Bernand-Mantel, A; Bouzehouane, K; Seneor, P; Fusil, S; Deranlot, C; Petroff, F; Fert, A [Unite Mixte de Physique CNRS/Thales and Universite Paris-Sud, 1, Avenue Auguste Fresnel, F-91767 Palaiseau (France); Brenac, A; Notin, L; Morel, R, E-mail: anne.bernand-mantel@grenoble.cnrs.fr, E-mail: karim.bouzehouane@thalesgroup.fr [INAC/SP2M CEA Grenoble, 17 rue des Martyrs, F-38054 Grenoble Cedex 9 (France)

    2010-11-05

    We report on the high yield connection of single nano-objects as small as a few nanometres in diameter to separately elaborated metallic electrodes, using a 'table-top' nanotechnology. Single-electron transport measurements validate that transport occurs through a single nano-object. The vertical geometry of the device natively allows an independent choice of materials for each electrode and the nano-object. In addition ferromagnetic materials can be used without encountering oxidation problems. The possibility of elaborating such hybrid nanodevices opens new routes for the democratization of spintronic studies in low dimensions.

  1. Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller

    Directory of Open Access Journals (Sweden)

    Ting-Chia Ou

    2017-04-01

    Full Text Available This paper endeavors to apply a novel intelligent damping controller (NIDC for the static synchronous compensator (STATCOM to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm (OWF and a seashore wave power farm (SWPF via a high-voltage, alternating current (HVAC electric power transmission line that connects the STATCOM and the 12-bus hybrid power multi-system. The hybrid multi-system consists of a battery energy storage system (BESS and a micro-turbine generation (MTG. The proposed NIDC consists of a designed proportional–integral–derivative (PID linear controller, an adaptive critic network and a proposed functional link-based novel recurrent fuzzy neural network (FLNRFNN. Test results show that the proposed controller can achieve better damping characteristics and effectively stabilize the network under unstable conditions.

  2. Solving advanced multi-objective robust designs by means of multiple objective evolutionary algorithms (MOEA): A reliability application

    Energy Technology Data Exchange (ETDEWEB)

    Salazar A, Daniel E. [Division de Computacion Evolutiva (CEANI), Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Universidad de Las Palmas de Gran Canaria. Canary Islands (Spain)]. E-mail: danielsalazaraponte@gmail.com; Rocco S, Claudio M. [Universidad Central de Venezuela, Facultad de Ingenieria, Caracas (Venezuela)]. E-mail: crocco@reacciun.ve

    2007-06-15

    This paper extends the approach proposed by the second author in [Rocco et al. Robust design using a hybrid-cellular-evolutionary and interval-arithmetic approach: a reliability application. In: Tarantola S, Saltelli A, editors. SAMO 2001: Methodological advances and useful applications of sensitivity analysis. Reliab Eng Syst Saf 2003;79(2):149-59 [special issue

  3. Multi effect desalination and adsorption desalination (MEDAD): A hybrid desalination method

    KAUST Repository

    Shahzad, Muhammad Wakil

    2014-11-01

    This paper presents an advanced desalination cycle that hybridizes a conventional multi-effect distillation (MED) and an emerging yet low-energy adsorption cycle (AD). The hybridization of these cycles, known as MED + AD or MEDAD in short, extends the limited temperature range of the MED, typically from 65 °C at top-brine temperature (TBT) to a low-brine temperature (LBT) of 40 °C to a lower LBT of 5 °C, whilst the TBT remains the same. The integration of cycles is achieved by having vapor uptake by the adsorbent in AD cycle, extracting from the vapor emanating from last effect of MED. By increasing the range of temperature difference (DT) of a MEDAD, its design can accommodate additional condensation-evaporation stages that capitalize further the energy transfer potential of expanding steam. Numerical model for the proposed MEDAD cycle is presented and compared with the water production rates of conventional and hybridized MEDs. The improved MEDAD design permits the latter stages of MED to operate below the ambient temperature, scavenging heat from the ambient air. The increase recovery of water from the seawater feed may lead to higher solution concentration within the latter stages, but the lower saturation temperatures of these stages mitigate the scaling and fouling effects. © 2014 Elsevier Ltd. All rights reserved.

  4. Multi-component hybrid soft ionogels for photoluminescence tuning and sensing organic solvent vapors.

    Science.gov (United States)

    Ma, Jing; Yan, Bing

    2017-11-07

    This paper tries to prepare soft ionogels through the carboxyl ion liquid (IM + Br - ) as double chemical linker connecting both Bio-MOF-1 (Zn 8 (ad) 4 (BPDC) 6 O·2Me 2 NH 2 , BPDC=biphenyl-4,4'-dicarboxylate, Ad = adeninate) and lanthanide complexes. Among anionic Bio-MOF-1 interacts with IM + Br - through cation exchange (IM + ) to form BMOF-IM and lanthanide ions are further introduced through the coordination to the carboxylic group of IM + together with Phen (1,10-phenanthroline) as assistant ligand for Ln 3+ (Ln = Eu, Tb or Eu/Tb). The resulting multi-component hybrid ionogels (Phen-Ln-IM@BMOF) are prepared and characterized by PXRD, FTIR, TGA and mechanical properties by compression experiment, respectively. The photophysical properties of these hybrid systems are studied in details. By controlling the composition of different Ln 3+ cations in IM@BMOF, the luminescent color of them can be tuned and the white light output can be realized. Furthermore, with careful adjustment of the excitation wavelength, the color of the luminescence can be modulated. Eventually we obtain luminescence trichromatic (Phen-Eu/Tb-IM@BMOF) white-light-emitting materials. Moreover, we try to choose Phen-Eu-IM@BMOF hybrid system for the detection of organic volatile substances, which shows the apparent luminescence quenching effect on ammonia for high sensitivity of sensing. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. An Integrated Method for Interval Multi-Objective Planning of a Water Resource System in the Eastern Part of Handan

    Directory of Open Access Journals (Sweden)

    Meiqin Suo

    2017-07-01

    Full Text Available In this study, an integrated solving method is proposed for interval multi-objective planning. The proposed method is based on fuzzy linear programming and an interactive two-step method. It cannot only provide objectively optimal values for multiple objectives at the same time, but also effectively offer a globally optimal interval solution. Meanwhile, the degree of satisfaction related to different objective functions would be obtained. Then, the integrated solving method for interval multi-objective planning is applied to a case study of planning multi-water resources joint scheduling under uncertainty in the eastern part of Handan, China. The solutions obtained are useful for decision makers in easing the contradiction between supply of multi-water resources and demand from different water users. Moreover, it can provide the optimal comprehensive benefits of economy, society, and the environment.

  6. PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem

    OpenAIRE

    Sanhueza, Claudio; Jimenez, Francia; Berretta, Regina; Moscato, Pablo

    2017-01-01

    Multi-Objective Optimization Problems (MOPs) have attracted growing attention during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have been extensively used to address MOPs because are able to approximate a set of non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP which has been extensively studied, and used in several real-life applications. The mQAP is defined as havin...

  7. Objectivity

    CERN Document Server

    Daston, Lorraine

    2010-01-01

    Objectivity has a history, and it is full of surprises. In Objectivity, Lorraine Daston and Peter Galison chart the emergence of objectivity in the mid-nineteenth-century sciences--and show how the concept differs from its alternatives, truth-to-nature and trained judgment. This is a story of lofty epistemic ideals fused with workaday practices in the making of scientific images. From the eighteenth through the early twenty-first centuries, the images that reveal the deepest commitments of the empirical sciences--from anatomy to crystallography--are those featured in scientific atlases, the compendia that teach practitioners what is worth looking at and how to look at it. Galison and Daston use atlas images to uncover a hidden history of scientific objectivity and its rivals. Whether an atlas maker idealizes an image to capture the essentials in the name of truth-to-nature or refuses to erase even the most incidental detail in the name of objectivity or highlights patterns in the name of trained judgment is a...

  8. A multi-objective optimization approach of housing in Algeria. A step towards sustainability

    Directory of Open Access Journals (Sweden)

    Khadidja El-Bahdja Djebbar

    2018-06-01

    Full Text Available The present study focuses on the evaluation of the energy and environmental performance (EEP of a typical multi-storey building in Tlemcen, Algeria. It aims to contribute to the development of Algeria’s thermal regulation by establishing a list of actions to be taken in short, medium and long term, in terms of thermal rehabilitation in existing housing areas as well as in new ones, in urban regions with a similar climate. An appropriate multi-criteria methodology is developed by determining its thermal performance using a static method as well as establishing a multi- objective strategy of EEP optimization. The assessment of: the potential of primary-energy-saving and that of the CO2-emissions’ reduction and indoor-environmental-quality of a real state of construction by simulation using DesignBuilder software, together with investment-cost are reported. Therefore, this study is conducted using the potential of parametric evaluation methodology to investigate the impact of passive energy-efficiency-measures on the building envelope from energy, environmental, economic and thermal-comfort points of view. Insulation and ventilation reduction are the main measures saving more than 75% of energy and 44% of CO2-emissions. Besides, the winter comfort is significantly improved. But from the economic standpoint, policy measures must be taken; namely tariffs reforms and energy control law enforcement.

  9. Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas

    Science.gov (United States)

    Wiecha, Peter R.; Arbouet, Arnaud; Girard, Christian; Lecestre, Aurélie; Larrieu, Guilhem; Paillard, Vincent

    2017-02-01

    The rational design of photonic nanostructures consists of anticipating their optical response from systematic variations of simple models. This strategy, however, has limited success when multiple objectives are simultaneously targeted, because it requires demanding computational schemes. To this end, evolutionary algorithms can drive the morphology of a nano-object towards an optimum through several cycles of selection, mutation and cross-over, mimicking the process of natural selection. Here, we present a numerical technique that can allow the design of photonic nanostructures with optical properties optimized along several arbitrary objectives. In particular, we combine evolutionary multi-objective algorithms with frequency-domain electrodynamical simulations to optimize the design of colour pixels based on silicon nanostructures that resonate at two user-defined, polarization-dependent wavelengths. The scattering spectra of optimized pixels fabricated by electron-beam lithography show excellent agreement with the targeted objectives. The method is self-adaptive to arbitrary constraints and therefore particularly apt for the design of complex structures within predefined technological limits.

  10. A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation

    Science.gov (United States)

    Botter Martins, Samuel; Vallin Spina, Thiago; Yasuda, Clarissa; Falcão, Alexandre X.

    2017-02-01

    Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

  11. Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

    DEFF Research Database (Denmark)

    Cao, Bin; Zhao, Jianwei; Yang, Po

    2018-01-01

    -objective evolutionary algorithms the Cooperative Coevolutionary Generalized Differential Evolution 3, the Cooperative Multi-objective Differential Evolution and the Nondominated Sorting Genetic Algorithm III, the proposed algorithm addresses the deployment optimization problem efficiently and effectively.......Using immune algorithms is generally a time-intensive process especially for problems with a large number of variables. In this paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm that is implemented using the message passing interface...... (MPI). The proposed algorithm is composed of three layers: objective, group and individual layers. First, for each objective in the multi-objective problem to be addressed, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives. Second, the large...

  12. A hybrid multi-compartment model of granuloma formation and T cell priming in Tuberculosis

    Science.gov (United States)

    Marino, Simeone; El-Kebir, Mohammed; Kirschner, Denise

    2013-01-01

    Tuberculosis is a worldwide health problem with 2 billion people infected with Mycobacterium tuberculosis (Mtb, the bacteria causing TB). The hallmark of infection is the emergence of organized structures of immune cells forming primarily in the lung in response to infection. Granulomas physically contain and immunologically restrain bacteria that cannot be cleared. We have developed several models that spatially characterize the dynamics of the host–mycobacterial interaction, and identified mechanisms that control granuloma formation and development. In particular, we published several agent-based models (ABMs) of granuloma formation in TB that include many subtypes of T cell populations, macrophages as well as key cytokine and chemokine effector molecules. These ABM studies emphasize the important role of T-cell related mechanisms in infection progression, such as magnitude and timing of T cell recruitment, and macrophage activation. In these models, the priming and recruitment of T cells from the lung draining lymph node (LN) was captured phenomenologically. In addition to these ABM studies, we have also developed several multi-organ models using ODEs to examine trafficking of cells between, for example, the lung and LN. While we can predict temporal dynamic behaviors, those models are not coupled to the spatial aspects of granuloma. To this end, we have developed a multi-organ model that is hybrid: an ABM for the lung compartment and a non-linear system of ODE representing the lymph node compartment. This hybrid multi-organ approach to study TB granuloma formation in the lung and immune priming in the LN allows us to dissect protective mechanisms that cannot be achieved using the single compartment or multi-compartment ODE system. The main finding of this work is that trafficking of important cells known as antigen presenting cells from the lung to the lymph node is a key control mechanism for protective immunity: the entire spectrum of infection outcomes can

  13. Multi-Objective Optimization Algorithm to Discover Condition-Specific Modules in Multiple Networks

    Directory of Open Access Journals (Sweden)

    Xiaoke Ma

    2017-12-01

    Full Text Available The advances in biological technologies make it possible to generate data for multiple conditions simultaneously. Discovering the condition-specific modules in multiple networks has great merit in understanding the underlying molecular mechanisms of cells. The available algorithms transform the multiple networks into a single objective optimization problem, which is criticized for its low accuracy. To address this issue, a multi-objective genetic algorithm for condition-specific modules in multiple networks (MOGA-CSM is developed to discover the condition-specific modules. By using the artificial networks, we demonstrate that the MOGA-CSM outperforms state-of-the-art methods in terms of accuracy. Furthermore, MOGA-CSM discovers stage-specific modules in breast cancer networks based on The Cancer Genome Atlas (TCGA data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm provide an effective way to analyze multiple networks.

  14. Robust Multi-Objective PQ Scheduling for Electric Vehicles in Flexible Unbalanced Distribution Grids

    DEFF Research Database (Denmark)

    Knezovic, Katarina; Soroudi, Alireza; Marinelli, Mattia

    2017-01-01

    . The robust formulation effectively considers the errors in the electricity price forecast and its influence on the EV schedule. Moreover, the impact of EV reactive power support on objective values and technical parameters is analysed both when EVs are the only flexible resources and when linked with other......With increased penetration of distributed energy resources and electric vehicles (EVs), different EV management strategies can be used for mitigating adverse effects and supporting the distribution grid. This paper proposes a robust multi-objective methodology for determining the optimal day...... demand response programs. The method is tested on a real Danish unbalanced distribution grid with 35% EV penetration to demonstrate the effectiveness of the proposed approach. It is shown that the proposed formulation guarantees an optimal EV cost as long as the price uncertainties are lower than...

  15. An Extensible Component-Based Multi-Objective Evolutionary Algorithm Framework

    DEFF Research Database (Denmark)

    Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

    2017-01-01

    The ability to easily modify the problem definition is currently missing in Multi-Objective Evolutionary Algorithms (MOEA). Existing MOEA frameworks do not support dynamic addition and extension of the problem formulation. The existing frameworks require a re-specification of the problem definition...... and recompilation of source code implementing the problem specification. The presented, Controleum framework is based on Dynamic Links and a component-based system to support dynamic reconfiguration of the problem formulation without any need for recompilation of source code. Four different experiments...... with different compositions of objectives from the horticulture domain are formulated based on a state of the art micro-climate simulator, electricity prices and weather forecasts. The experimental results demonstrate that the Controleum framework support dynamic reconfiguration of the problem formulation...

  16. Fuzzy Logic Approaches to Multi-Objective Decision-Making in Aerospace Applications

    Science.gov (United States)

    Hardy, Terry L.

    1994-01-01

    Fuzzy logic allows for the quantitative representation of multi-objective decision-making problems which have vague or fuzzy objectives and parameters. As such, fuzzy logic approaches are well-suited to situations where alternatives must be assessed by using criteria that are subjective and of unequal importance. This paper presents an overview of fuzzy logic and provides sample applications from the aerospace industry. Applications include an evaluation of vendor proposals, an analysis of future space vehicle options, and the selection of a future space propulsion system. On the basis of the results provided in this study, fuzzy logic provides a unique perspective on the decision-making process, allowing the evaluator to assess the degree to which each option meets the evaluation criteria. Future decision-making should take full advantage of fuzzy logic methods to complement existing approaches in the selection of alternatives.

  17. Developing a Novel Multi-objective Programming Model for Personnel Assignment Problem

    Directory of Open Access Journals (Sweden)

    Mehdi Seifbarghy

    2014-05-01

    Full Text Available The assignment of personnel to the right positions in order to increase organization's performance is one of the most crucial tasks in human resource management. In this paper, personnel assignment problem is formulated as a multi-objective binary integer programming model in which skills, level of satisfaction and training cost of personnel are considered simultaneously in productive company. The purpose of this model is to obtain the best matching between candidates and positions. In this model, a set of methods such as a group analytic hierarchy process (GAHP, Shannon entropy, coefficient of variation (CV and fuzzy logic are used to calculate the weights of evaluation criteria, weights of position and coefficient of objective functions. This proposed model can rationalize the subjective judgments of decision makers with mathematic models.

  18. Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

    Directory of Open Access Journals (Sweden)

    Man Ding

    2017-03-01

    Full Text Available This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP, which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII and multi-objective ABC (MOABC, are presented to illustrate the effectiveness and robustness of the proposed method.

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-06-15

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

  1. 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.

  2. GaudiMM: A modular multi-objective platform for molecular modeling.

    Science.gov (United States)

    Rodríguez-Guerra Pedregal, Jaime; Sciortino, Giuseppe; Guasp, Jordi; Municoy, Martí; Maréchal, Jean-Didier

    2017-09-15

    GaudiMM (for Genetic Algorithms with Unrestricted Descriptors for Intuitive Molecular Modeling) is here presented as a modular platform for rapid 3D sketching of molecular systems. It combines a Multi-Objective Genetic Algorithm with diverse molecular descriptors to overcome the difficulty of generating candidate models for systems with scarce structural data. Its grounds consist in transforming any molecular descriptor (i.e. those generally used for analysis of data) as a guiding objective for PES explorations. The platform is written in Python with flexibility in mind: the user can choose which descriptors to use for each problem and is even encouraged to code custom ones. Illustrative cases of its potential applications are included to demonstrate the flexibility of this approach, including metal coordination of multidentate ligands, peptide folding, and protein-ligand docking. GaudiMM is available free of charge from https://github.com/insilichem/gaudi. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  3. Dynamic population artificial bee colony algorithm for multi-objective optimal power flow.

    Science.gov (United States)

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

    2017-03-01

    This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.

  4. Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems.

    Science.gov (United States)

    Cilfone, Nicholas A; Kirschner, Denise E; Linderman, Jennifer J

    2015-03-01

    Biologically related processes operate across multiple spatiotemporal scales. For computational modeling methodologies to mimic this biological complexity, individual scale models must be linked in ways that allow for dynamic exchange of information across scales. A powerful methodology is to combine a discrete modeling approach, agent-based models (ABMs), with continuum models to form hybrid models. Hybrid multi-scale ABMs have been used to simulate emergent responses of biological systems. Here, we review two aspects of hybrid multi-scale ABMs: linking individual scale models and efficiently solving the resulting model. We discuss the computational choices associated with aspects of linking individual scale models while simultaneously maintaining model tractability. We demonstrate implementations of existing numerical methods in the context of hybrid multi-scale ABMs. Using an example model describing Mycobacterium tuberculosis infection, we show relative computational speeds of various combinations of numerical methods. Efficient linking and solution of hybrid multi-scale ABMs is key to model portability, modularity, and their use in understanding biological phenomena at a systems level.

  5. Multi-objective optimization for deformable image registration: proof of concept

    Science.gov (United States)

    Alderliesten, Tanja; Sonke, Jan-Jakob; Bosman, Peter A. N.

    2012-02-01

    In this work we develop and study a methodology for deformable image registration that overcomes a drawback of optimization procedures in common deformable image registration approaches: the use of a single combination of different objectives. Because selecting the best combination is well-known to be non-trivial, we use a multi-objective optimization approach that computes and presents multiple outcomes (a so-called Pareto front) at once. The approach is inherently more powerful because not all Pareto-optimal outcomes are necessarily obtainable by running existing approaches multiple times, for different combinations. Furthermore, expert knowledge can be easily incorporated in making the final best-possible decision by simply looking at (a diverse selection of) the outcomes illustrating both the transformed image and the associated deformation vector field. At the basis of the optimization methodology lies an advanced, model-based evolutionary algorithm that aims to exploit features of a problem's structure in a principled manner via probabilistic modeling. Two objectives are defined: 1) maximization of intensity similarity (normalized mutual information) and 2) minimization of energy required to accomplish the transformation (a model based on Hooke's law that incorporates elasticity characteristics associated with different tissue types). A regular grid of points forms the basis of the transformation model. Interpolation extends the correspondence as found for the grid to the rest of the volume. As a proof of concept we performed tests on a 2D axial slice of a CT scan of a breast. Results indicate plausible behavior of the proposed methodology that innovatively combines intensity-based and model-based registration criteria with state-of-the-art adaptive computation techniques for multi-objective optimization in deformable image registration.

  6. Multi-wave and hybrid imaging techniques: a new direction for nondestructive testing and structural health monitoring.

    Science.gov (United States)

    Cheng, Yuhua; Deng, Yiming; Cao, Jing; Xiong, Xin; Bai, Libing; Li, Zhaojun

    2013-11-27

    In this article, the state-of-the-art multi-wave and hybrid imaging techniques in the field of nondestructive evaluation and structural health monitoring were comprehensively reviewed. A new direction for assessment and health monitoring of various structures by capitalizing the advantages of those imaging methods was discussed. Although sharing similar system configurations, the imaging physics and principles of multi-wave phenomena and hybrid imaging methods are inherently different. After a brief introduction of nondestructive evaluation (NDE) , structure health monitoring (SHM) and their related challenges, several recent advances that have significantly extended imaging methods from laboratory development into practical applications were summarized, followed by conclusions and discussion on future directions.

  7. Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models.

    Science.gov (United States)

    Barzegar, Rahim; Fijani, Elham; Asghari Moghaddam, Asghar; Tziritis, Evangelos

    2017-12-01

    Accurate prediction of groundwater level (GWL) fluctuations can play an important role in water resources management. The aims of the research are to evaluate the performance of different hybrid wavelet-group method of data handling (WA-GMDH) and wavelet-extreme learning machine (WA-ELM) models and to combine different wavelet based models for forecasting the GWL for one, two and three months step-ahead in the Maragheh-Bonab plain, NW Iran, as a case study. The research used totally 367 monthly GWLs (m) datasets (Sep 1985-Mar 2016) which were split into two subsets; the first 312 datasets (85% of total) were used for model development (training) and the remaining 55 ones (15% of total) for model evaluation (testing). The stepwise selection was used to select appropriate lag times as the inputs of the proposed models. The performance criteria such as coefficient of determination (R2), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were used for assessing the efficiency of the models. The results indicated that the ELM models outperformed GMDH models. To construct the hybrid wavelet based models, the inputs and outputs were decomposed into sub-time series employing different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Dmeyer of different orders at level two. Subsequently, these sub-time series were served in the GMDH and ELM models as an input dataset to forecast the multi-step-ahead GWL. The wavelet based models improved the performances of GMDH and ELM models for multi-step-ahead GWL forecasting. To combine the advantages of different wavelets, a least squares boosting (LSBoost) algorithm was applied. The use of the boosting multi-WA-neural network models provided the best performances for GWL forecasts in comparison with single WA-neural network-based models. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Model-based framework for multi-axial real-time hybrid simulation testing

    Science.gov (United States)

    Fermandois, Gaston A.; Spencer, Billie F.

    2017-10-01

    Real-time hybrid simulation is an efficient and cost-effective dynamic testing technique for performance evaluation of structural systems subjected to earthquake loading with rate-dependent behavior. A loading assembly with multiple actuators is required to impose realistic boundary conditions on physical specimens. However, such a testing system is expected to exhibit significant dynamic coupling of the actuators and suffer from time lags that are associated with the dynamics of the servo-hydraulic system, as well as control-structure interaction (CSI). One approach to reducing experimental errors considers a multi-input, multi-output (MIMO) controller design, yielding accurate reference tracking and noise rejection. In this paper, a framework for multi-axial real-time hybrid simulation (maRTHS) testing is presented. The methodology employs a real-time feedback-feedforward controller for multiple actuators commanded in Cartesian coordinates. Kinematic transformations between actuator space and Cartesian space are derived for all six-degrees-offreedom of the moving platform. Then, a frequency domain identification technique is used to develop an accurate MIMO transfer function of the system. Further, a Cartesian-domain model-based feedforward-feedback controller is implemented for time lag compensation and to increase the robustness of the reference tracking for given model uncertainty. The framework is implemented using the 1/5th-scale Load and Boundary Condition Box (LBCB) located at the University of Illinois at Urbana- Champaign. To demonstrate the efficacy of the proposed methodology, a single-story frame subjected to earthquake loading is tested. One of the columns in the frame is represented physically in the laboratory as a cantilevered steel column. For realtime execution, the numerical substructure, kinematic transformations, and controllers are implemented on a digital signal processor. Results show excellent performance of the maRTHS framework when six

  9. Multi-objective optimisation in carbon monoxide gas management at TRONOX KXN Sands

    Directory of Open Access Journals (Sweden)

    Stadler, Johan

    2014-08-01

    Full Text Available Carbon monoxide (CO is a by-product of the ilmenite smelting process from which titania slag and pig iron are produced. Prior to this project, the CO at Tronox KZN Sands in South Africa was burnt to get rid of it, producing carbon dioxide (CO2. At this plant, unprocessed materials are pre-heated using methane gas from an external supplier. The price of methane gas has increased significantly; and so this research considers the possibility of recycling CO gas and using it as an energy source to reduce methane gas demand. It is not possible to eliminate the methane gas consumption completely due to the energy demand fluctuation, and sub-plants have been assigned either CO gas or methane gas over time. Switching the gas supply between CO and methane gas involves production downtime to purge supply lines. Minimising the loss of production time while maximising the use of CO arose as a multi-objective optimisation problem (MOP with seven decision variables, and computer simulation was used to evaluate scenarios. We applied computer simulation and the multi-objective optimisation cross-entropy method (MOO CEM to find good solutions while evaluating the minimum number of scenarios. The proposals in this paper, which are in the process of being implemented, could save the company operational expenditure while reducing the carbon footprint of the smelter.

  10. Multi-objective simultaneous placement of DG and DSTATCOM using novel lightning search algorithm

    Directory of Open Access Journals (Sweden)

    Yuvaraj Thangaraj

    2017-10-01

    Full Text Available In this proposed study, a new long term scheduling is proposed for simultaneous placement of Distributed Generation (DG and Distribution STATic COMpensator (DSTATCOM in the radial distribution networks. The proposed work has a unique multi-objective function which consists of minimizing power loss, and total voltage deviation (TVD, as well as maximizing the voltage stability index (VSI subject to equality and inequality system constraints. The multi-objective problem has been solved by a novel metaheuristic optimization algorithm called as lightning search algorithm (LSA. In the proposed approach, the feeder loads are varied linearly from light load (0.5 to peak load (1.6 with a step size of 1%. In each load step, the optimal sizing for DG and DSTATCOM are calculated by LSA. Through curve fitting technique (CFT, the optimal sizing for both DG and DSTATCOM per load level is formulated in the form of generalized equation. The proposed generalized equation will help the distribution network operators (DNOs to select the DG and DSTATCOM sizes according to the load changes. The proposed method is tested on two test systems of 33-bus and 69-bus in different cases. Keywords: Distributed Generation (DG, Distribution STATic COMpensator (DSTATCOM, Lightning search algorithm (LSA, Voltage stability index (VSI, Curve fitting technique (CFT, Distribution network operators (DNOs

  11. Benchmarking and performance enhancement framework for multi-staging object-oriented languages

    Directory of Open Access Journals (Sweden)

    Ahmed H. Yousef

    2013-06-01

    Full Text Available This paper focuses on verifying the readiness, feasibility, generality and usefulness of multi-staging programming in software applications. We present a benchmark designed to evaluate the performance gain of different multi-staging programming (MSP languages implementations of object oriented languages. The benchmarks in this suite cover different tests that range from classic simple examples (like matrix algebra to advanced examples (like encryption and image processing. The benchmark is applied to compare the performance gain of two different MSP implementations (Mint and Metaphor that are built on object oriented languages (Java and C# respectively. The results concerning the application of this benchmark on these languages are presented and analysed. The measurement technique used in benchmarking leads to the development of a language independent performance enhancement framework that allows the programmer to select which code segments need staging. The framework also enables the programmer to verify the effectiveness of staging on the application performance. The framework is applied to a real case study. The case study results showed the effectiveness of the framework to achieve significant performance enhancement.

  12. Multi-Objective Low-Carbon Economic Dispatch Considering Demand Response with Wind Power Integrated Systems

    Directory of Open Access Journals (Sweden)

    Liu Wenjuan

    2017-01-01

    Full Text Available The generation cost, carbon emissions and customers’ satisfaction are considered in this paper. On the basis of this, the multi-objective and low-carbon economic dispatch model with wind farm, this considers demand response, is established. The model user stochastic programming theory to describe the uncertainty of the wind power and converts it into an equivalent deterministic model by using distribution function of wind power output, optimizes demand side resources to adjust the next day load curve and to improve load rate and absorptive capacity of wind power, introduce customers’ satisfaction to ensure that the scheduling scheme satisfies customer and integrate the resources of source and load to unify coordination wind farm access to network and to meet the requirements of energy saving and emission reduction. The search process of artificial fish school algorithm introducing Tabu search and more targeted search mechanism, an multi-objective improved artificial fish school algorithm is proposed to solve this model. Using the technique for order preference by similarity to ideal solution (TOPSIS to sort the Pareto frontier, the optimal scheduling scheme is determined. Simulation results verify the rationality and validity of the proposed model and algorithm.

  13. 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.

  14. Identification of mutated driver pathways in cancer using a multi-objective optimization model.

    Science.gov (United States)

    Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

    2016-05-01

    New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Monte Carlo modelling of multi-object adaptive optics performance on the European Extremely Large Telescope

    Science.gov (United States)

    Basden, A. G.; Morris, T. J.

    2016-12-01

    The performance of a wide-field adaptive optics (AO) system depends on input design parameters. Here we investigate the performance of a multi-object AO system design for the European Extremely Large Telescope, using an end-to-end Monte Carlo AO simulation tool, Durham adaptive optics simulation platform, with relevance for proposed instruments such as MOSAIC. We consider parameters such as the number of laser guide stars, sodium layer depth, wavefront sensor pixel scale, actuator pitch and natural guide star availability. We provide potential areas where costs savings can be made, and investigate trade-offs between performance and cost, and provide solutions that would enable such an instrument to be built with currently available technology. Our key recommendations include a trade-off for laser guide star wavefront sensor pixel scale of about 0.7 arcsec per pixel, and a field of view of at least 7 arcsec, that electron multiplying CCD technology should be used for natural guide star wavefront sensors even if reduced frame rate is necessary, and that sky coverage can be improved by a slight reduction in natural guide star sub-aperture count without significantly affecting tomographic performance. We find that AO correction can be maintained across a wide field of view, up to 7 arcmin in diameter. We also recommend the use of at least four laser guide stars, and include ground-layer and multi-object AO performance estimates.

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

    Science.gov (United States)

    Kim, Tae Yong; Park, Jong Myoung; Kim, Hyun Uk; Cho, Kwang Myung; Lee, Sang Yup

    2015-03-01

    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 acids, while maintaining sufficiently high growth rate and minimizing the secretion of undesired byproducts. Homo-productions of acetic, lactic and succinic acids were targeted as examples. Engineered E. coli strains capable of producing homo-acetic and homo-lactic acids could be developed by taking this systems approach for the minimal identification of gene knockout targets. Also, failure to predict effective gene knockout targets for the homo-succinic acid production suggests that the multi-objective optimization is useful in assessing the suitability of a microorganism as a host strain for the production of a homo-organic acid. The systems metabolic engineering-based approach reported here should be applicable to the production of other industrially important organic acids. Copyright © 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  17. A Cognitive Skill Classification Based on Multi Objective Optimization Using Learning Vector Quantization for Serious Games

    Directory of Open Access Journals (Sweden)

    Moh. Aries Syufagi

    2013-09-01

    Full Text Available Nowadays, serious games and game technology are poised to transform the way of educating and training students at all levels. However, pedagogical value in games do not help novice students learn, too many memorizing and reduce learning process due to no information of player’s ability. To asses the cognitive level of player ability, we propose a Cognitive Skill Game (CSG. CSG improves this cognitive concept to monitor how players interact with the game. This game employs Learning Vector Quantization (LVQ for optimizing the cognitive skill input classification of the player. CSG is using teacher’s data to obtain the neuron vector of cognitive skill pattern supervise. Three clusters multi objective XE "multi objective"  target will be classified as; trial and error, carefully and, expert cognitive skill. In the game play experiments employ 33 respondent players demonstrates that 61% of players have high trial and error, 21% have high carefully, and 18% have high expert cognitive skill. CSG may provide information to game engine when a player needs help or when wanting a formidable challenge. The game engine will provide the appropriate tasks according to players’ ability. CSG will help balance the emotions of players, so players do not get bored and frustrated. 

  18. A Multi-Objective Optimization Model for Planning Unmanned Aerial Vehicle Cruise Route

    Directory of Open Access Journals (Sweden)

    Xiaofeng Liu

    2016-06-01

    Full Text Available The use of unmanned aerial vehicles (UAVs was introduced to monitor a traffic situation and the respective cruise route optimization problem was given. Firstly, a multi-objective optimization model was proposed, which considered two scenarios: the first scenario was that there were enough UAVs to monitor all the targets, while the second scenario was that only some targets could be monitored due to a lack of UAVs. A multi-objective evolutionary algorithm was subsequently proposed to plan the UAV cruise route. Next, a route planning experiment, using the Microdrones md4-1000 UAV, was conducted and a UAV route planning case was studied. The experiment showed that the UAV actual flight route was almost consistent with the planned route. The case study showed that, compared with the initial optimal solutions, the optimal total UAV cruise distance and the number of UAVs used in scenario 1 decreased by 41.65% and 40.00%, respectively. Meanwhile, the total UAV cruise distance and the number of targets monitored in scenario 2 reduced by 15.75% and increased by 27.27%, respectively. In addition, a comparison study with other algorithms was conducted, while the optimization results were also improved. This demonstrated that the proposed UAV cruise route planning model was effective.

  19. Near-Earth object hazardous impact: A Multi-Criteria Decision Making approach

    Science.gov (United States)

    Sánchez-Lozano, J. M.; Fernández-Martínez, M.

    2016-11-01

    The impact of a near-Earth object (NEO) may release large amounts of energy and cause serious damage. Several NEO hazard studies conducted over the past few years provide forecasts, impact probabilities and assessment ratings, such as the Torino and Palermo scales. These high-risk NEO assessments involve several criteria, including impact energy, mass, and absolute magnitude. The main objective of this paper is to provide the first Multi-Criteria Decision Making (MCDM) approach to classify hazardous NEOs. Our approach applies a combination of two methods from a widely utilized decision making theory. Specifically, the Analytic Hierarchy Process (AHP) methodology is employed to determine the criteria weights, which influence the decision making, and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain a ranking of alternatives (potentially hazardous NEOs). In addition, NEO datasets provided by the NASA Near-Earth Object Program are utilized. This approach allows the classification of NEOs by descending order of their TOPSIS ratio, a single quantity that contains all of the relevant information for each object.

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

    Science.gov (United States)

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

    2017-11-01

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