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

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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.

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Acknowledgments

The authors would like to thank Aimin Zhou for his helpful comments and suggestions on the original manuscripts. This work was supported by the National Basic Research Program of China under Grant No. 2012CB821205, Foundation for Creative Research Groups of the National Natural Science Foundation of China under Grant No. 61021002, National Natural Science Foundation of China under Grant No. 61174037, and the Innovation Funds of China Academy of Space Technology under Grant No. CAST20120602.

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Authors and Affiliations

  1. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, 150001, China

    Hu Zhang & Shenming Song

  2. Academy of Fundamental and Interdisciplinary Sciences, Harbin Institute of Technology, Harbin, 150001, China

    Xiujie Zhang

  3. Department of Electrical Engineering and Automation, Aalto University School of Electrical Engineering, Aalto, Finland

    Xiao-Zhi Gao

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  1. Hu Zhang
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  2. Xiujie Zhang
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  4. Xiao-Zhi Gao
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Correspondence to Shenming Song.

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Communicated by V. Loia.

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Zhang, H., Zhang, X., Song, S. et al. An affinity propagation-based multiobjective evolutionary algorithm for selecting optimal aiming points of missiles. Soft Comput 21, 3013–3031 (2017). https://doi.org/10.1007/s00500-015-1986-9

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