The existing algorithms for solving multi-objectiveoptimization problems fall into three main categories:Decomposition-based,dominance-based,and *** multi-objectiveoptimization problemsmainly focus on objectives,tre...
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The existing algorithms for solving multi-objectiveoptimization problems fall into three main categories:Decomposition-based,dominance-based,and *** multi-objectiveoptimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective *** seen,a variety of decision variable groupingalgorithms have been ***,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto *** solvethese problems,a multi-objectiveoptimizationalgorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is *** algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision *** the later stages of algorithmoptimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the *** validation of the test functions shows that this algorithmcan solve the multi-objectiveoptimization problem more efficiently.
In this paper, an improved selection method is proposed and integrated with summation of normalized objectives based multi-objective differential evolution to solve multi-objectiveoptimization problems. The summation...
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ISBN:
(纸本)9781612840727
In this paper, an improved selection method is proposed and integrated with summation of normalized objectives based multi-objective differential evolution to solve multi-objectiveoptimization problems. The summation of normalized objectives and diversified selection is used to replace the non-domination sorting and reduce the simulation time of the multi-objectiveevolutionaryalgorithm. However, the diversified selection may keep some bad individuals as parents which lead to poor performance. With the proposed method, a pre-selection is applied to filter the bad solutions and improve the convergence. The algorithm is tested on 15 commonly used benchmark problems and compared with a number of multi-objectiveevolutionaryalgorithms in literature. The results show that the proposed algorithm is effective and efficient.
Most multi-objectiveevolutionaryalgorithms (MOEAs) use the concept of dominance in the search process to select the top solutions as parents in an elitist manner. However, as MOEAs are probabilistic search methods, ...
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Most multi-objectiveevolutionaryalgorithms (MOEAs) use the concept of dominance in the search process to select the top solutions as parents in an elitist manner. However, as MOEAs are probabilistic search methods, some useful information may be wasted, if the dominated solutions are completely disregarded. In addition, the diversity may be lost during the early stages of the search process leading to a locally optimal or partial Pareto-front. Beside this, the non-domination sorting process is complex and time consuming. To overcome these problems, this paper proposes multi-objectiveevolutionaryalgorithms based on Summation of normalized objective values and diversified selection (SNOV-DS). The performance of this algorithm is tested on a set of benchmark problems using both multi-objectiveevolutionary programming (MOEP) and multi-objective differential evolution (MODE). With the proposed method, the performance metric has improved significantly and the speed of the parent selection process has also increased when compared with the non-domination sorting. In addition, the proposed algorithm also outperforms ten other algorithms. (C) 2010 Elsevier Inc. All rights reserved.
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