A Self-Organizing Multiobjective Evolutionary Algorithm

Hu Zhang*, Aimin Zhou, Shenmin Song, Qingfu Zhang, Xiaozhi Gao, Jun Zhang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

68 Citations (Scopus)

Abstract

Under mild conditions, the Pareto front (Pareto set) of a continuous m-objective optimization problem forms an (m - 1)-dimensional piecewise continuous manifold. Based on this property, this paper proposes a self-organizing multiobjective evolutionary algorithm. At each generation, a self-organizing mapping method with (m - 1) latent variables is applied to establish the neighborhood relationship among current solutions. A solution is only allowed to mate with its neighboring solutions to generate a new solution. To reduce the computational overhead, the self-organizing training step and the evolution step are conducted in an alternative manner. In other words, the self-organizing training is performed only one single step at each generation. The proposed algorithm has been applied to a number of test instances and compared with some state-of-the-art multiobjective evolutionary methods. The results have demonstrated its advantages over other approaches.

Original languageEnglish
Pages (from-to)792-806
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Volume20
Issue number5
DOIs
Publication statusPublished - Oct 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Clustering algorithm
  • evolutionary algorithms
  • multiobjective optimization
  • self-organizing map (SOM)
  • GENETIC ALGORITHM
  • OBJECTIVE OPTIMIZATION
  • MAP
  • HYPERVOLUME
  • SELECTION
  • MOEA/D

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