An affinity propagation-based multiobjective evolutionary algorithm for selecting optimal aiming points of missiles

Hu Zhang, Xiujie Zhang, Shenming Song*, Xiaozhi Gao

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)

Abstract

When missiles attack the group targets, the selection of their optimal aiming points is a nonlinear, multi-dimensional and multimodal multiobjective optimization problem. To effectively address this problem, an affinity propagation-based multiobjective evolutionary algorithm called APMO is proposed in this article by introducing an affinity propagation and reproduction utility-based adaptive mating selection strategy named as AMS. In AMS, at each generation, an affinity propagation approach is firstly utilized to discover the neighborhood relationship of solutions. Afterward, parent selections for recombination are conducted on the neighborhoods or the whole population based on a mating restriction probability. Moreover, the mating restriction probability is updated at each generation according to the reproduction utility of the neighborhoods and the whole population over the last certain generations. Comprehensive experiments on benchmark instances denote that the proposed APMO significantly outperforms five popular multiobjective evolutionary algorithms, MOEA/D-DE, RM-MEDA, NSGA-II, SPEA2 and MOEA/D-STM. Practical application proves that APMO is promising to select the better optimal aiming points for missiles.

Original languageEnglish
Pages (from-to)3013–3031
Number of pages19
JournalSoft Computing
Volume21
Early online date29 Dec 2015
DOIs
Publication statusPublished - Jun 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Affinity propagation
  • Aiming point optimization of missiles
  • Evolutionary algorithm
  • Mating restriction
  • Multiobjective optimization

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