According to the disadvantage of slow convergence rate of the basic differential evolution(DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead(NM) simplex method into the basic DE algorithm i...
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According to the disadvantage of slow convergence rate of the basic differential evolution(DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead(NM) simplex method into the basic DE algorithm is presented in this *** hybrid procedure performed the exploration with DE and the exploitation with the NM simplex *** to the control parameters of the proposed approach is *** computational results on several classical Benchmarks nonlinear complex functions show that the hybrid optimization algorithm is superior to the two original search techniques(*** and DE) in terms of solution quality and convergence *** with other DE variants,the proposed algorithm has better convergence performance and *** Wilcoxon non-parametric statistical tests also confirm the above claims.
According to the disadvantage of slow convergence rate of the basic differential evolution (DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead (NM) simplex method into the basic DE algorithm...
详细信息
According to the disadvantage of slow convergence rate of the basic differential evolution (DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead (NM) simplex method into the basic DE algorithm is presented in this paper. This hybrid procedure performed the exploration with DE and the exploitation with the NM simplex method. Sensitivity to the control parameters of the proposed approach is analyzed. The computational results on several classical Benchmarks nonlinear complex functions show that the hybrid optimization algorithm is superior to the two original search techniques (i.e. NM and DE) in terms of solution quality and convergence rate. Compared with other DE variants, the proposed algorithm has better convergence performance and robustness. The Wilcoxon non-parametric statistical tests also *** the above claims.
In this paper,net present value is taken into account while discussing multiproduct aggregate production planning decision making problems in bifuzzy ***,an expected value programming model with net present value for ...
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In this paper,net present value is taken into account while discussing multiproduct aggregate production planning decision making problems in bifuzzy ***,an expected value programming model with net present value for that problem is *** an hybrid optimization algorithm combining bifuzzy simulation,genetic algorithm,and neural network is proposed to solve the *** the end of this paper,an numerical example is given to illustrate the feasibility of the proposed method.
In this paper,net present value is taken into account while discussing multiproduct aggregate production planning decision making problems in bifuzzy ***,an expected value programming model with net present value for ...
详细信息
In this paper,net present value is taken into account while discussing multiproduct aggregate production planning decision making problems in bifuzzy ***,an expected value programming model with net present value for that problem is *** an hybrid optimization algorithm combining bifuzzy simulation,genetic algorithm,and neural network is proposed to solve the *** the end of this paper,an numerical example is given to illustrate the feasibility of the proposed method.
A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehic...
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A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehicles among the available vehicles, the initial routing of the selected fleet and the routing optimization. Fuzzy C-means (FCM) can group the customers with close Euclidean distance into the same vehicle according to the principle of similar feature partition. Transiently chaotic neural network (TCNN) combines local search and global search, possessing high search efficiency. It will solve the routes to near optimality. A simple tabu search (TS) procedure can improve the routes to more optimality. The computations on benchmark problems and comparisons with other results in literatures show that the proposed algorithm is a viable and effective approach for CVRP.
This paper presents a two-stage hybrid optimization algorithm based on a modified genetic algorithm. In the first stage, a global search is carried out over the design search space using a modified GA. The proposed mo...
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This paper presents a two-stage hybrid optimization algorithm based on a modified genetic algorithm. In the first stage, a global search is carried out over the design search space using a modified GA. The proposed modifications on the basic GA includes dynamically changing the population size throughout the GA process and the use of different forms of the penalty function in constraint handling. In the second stage, a local search based on the genetic algorithm solution is executed using a discretized form of Hooke and Jeeves method. The hybridalgorithm and the modifications to the basic genetic algorithm are examined on the design optimization of reinforced concrete flat slab buildings. The objective function is the total cost of the structure including the cost of concrete, formwork, reinforcement and foundation excavation. The constraints are defined according to the British Standard BS8110 for reinforced concrete structures. Comparative studies are presented to study the effect of different parameters of handling genetic algorithm on the optimized flat slab building. It has been shown that the proposed hybridalgorithm can improve genetic algorithm solutions at the expense of more function evaluations. (c) 2004 Elsevier Ltd. All rights reserved.
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