Multiobjective evolutionary algorithm based on mixture Gaussian models

被引：0
作者：
Zhou, Ai-Min [1 ]
Zhang, Qing-Fu [2 ]
Zhang, Gui-Xu [1 ]
机构：
[1] Department of Computer Science and Technology, East China Normal University
[2] School of Computer Science and Electronic Engineering, University of Essex
来源：
Ruan Jian Xue Bao/Journal of Software | 2014年 / 25卷 / 05期
关键词：
Evolutionary algorithm; Mixture Gaussian probability model; MOEA/D; Multiobjective optimization;
D O I：
10.13328/j.cnki.jos.004514
中图分类号：
学科分类号：
摘要：
Recombination operators used in most current multiobjective evolutionary algorithms (MOEAs) were originally designed for single objective optimization. This paper demonstrates that some widely used recombination operators may not work well for multiobjective optimization problems (MOPs), and proposes a multiobjective evolutionary algorithm based on decomposition and mixture Gaussian models (MOEA/D-MG). In the algorithm, a reproduction operator based on mixture Gaussian models is used to model the population distribution and sample new trails solutions, and a greedy replacement scheme is then applied to update the population by the new trial solutions. MOEA/D-MG is applied to a variety of test instances with complicated Pareto fronts. The extensive experimental results indicate that MOEA/D-MG is promising for dealing with these continuous MOPs. © Copyright 2014, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
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页码：913 / 928
页数：15
相关论文
共 44 条
  • [41] Zhou A.M., Zhang Q.F., Jin Y.C., Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm, IEEE Trans. on Evolutionary Computation, 13, 5, pp. 1167-1189, (2009)
  • [42] Zhou A.M., Zhang Q.F., Zhang G.X., A multiobjective evolutionary algorithm based on decomposition and probability model, Proc. of the IEEE Congress of Evolutionary Computation (CEC 2012), pp. 1-8, (2012)
  • [43] Li H., Zhang Q.F., Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II, IEEE Trans. on Evolutionary Computation, 13, 2, pp. 284-302, (2009)
  • [44] Larranaga P., Lozano J., Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, (2002)
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