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.
引用
收藏
页码：913 / 928
页数：15
相关论文
共 44 条
  • [11] Zhang Q.F., Li H., MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Trans. on Evolutionary Computation, 11, 6, pp. 712-731, (2007)
  • [12] Zheng J.H., Multiobjective Optimization Algorithms and Their Applications, (2007)
  • [13] Lei D.M., Yan X.P., Multiobjective Intelligent Optimization Algorithms and their Applications, (2009)
  • [14] Jiao L.C., Shang R.H., Ma W.P., Multiobjective Immune Algorithms, Theory and Applications, (2010)
  • [15] Leung Y., Wang Y., Multi-Objective programming using uniform design and genetic algorithm, IEEE Trans. on Systems, Man, and Cybernetics, Part C, 30, 3, pp. 293-304, (2000)
  • [16] Zeng S.Y., Kang L.S., Ding L.X., An orthogonal multi-objective evolutionary algorithm for multi-objective optimization problems with constraints, Evolutionary Computation, 12, 1, pp. 77-98, (2004)
  • [17] Wang Y.N., Wu L.H., Yuan X.F., Multi-Objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure, Soft Computing, 14, 3, pp. 193-209, (2010)
  • [18] Gong W.Y., Cai Z.H., An improved multiobjective differential evolution based on Pareto-adaptive epsilon-dominance and orthogonal design, European Journal of Operational Research, 198, 2, pp. 576-601, (2009)
  • [19] Gong D.W., Zhang Y., Zhang J.H., Multi-Objective particle swarm optimization based on minimal particle angle, Proc. of the Int'l Conf. on Intelligent Computing, pp. 571-580, (2005)
  • [20] Zhan Z.H., Zhang J., A parallel particle swarm optimization approach for multiobjective optimization problems, Proc. of the Genetic and Evolutionary Computation Conf, pp. 81-82, (2010)
12345