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A new particle swarm optimization algorithm for fuzzy optimization of armored vehicle scheme design

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Abstract

Armored vehicle design is a complex constrained optimization problem which often involves a number of fuzzy and stochastic parameters. In this paper, a fuzzy optimization problem model of armored vehicle scheme design is presented, and a new particle swarm optimization (PSO) algorithm is proposed for effectively solving the problem. The problem model uses fuzzy variables to evaluate the objective function and constraints of the problem. The algorithm employs multiple ranking criteria to define three global bests of the swarm, makes different quality particles learning from different global bests, and thus search effectively through the solution space by means of multi-criteria optimization. Experiment results show that our approach can achieve good solution quality with low computational costs.

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Acknowledgements

This work was supported in part by grants from National Natural Science Foundation (Grant No. 61105073, 61020106009) of China. The authors are grateful to the data provided by Institute of Armored Forces Equipment and Technology.

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Authors and Affiliations

  1. Department of Command & Control, Academy of Armored Force Engineering, Beijing, 100072, China

    Kan Wang

  2. College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Yu Jun Zheng

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  1. Kan Wang
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  2. Yu Jun Zheng
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Correspondence to Yu Jun Zheng.

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Wang, K., Zheng, Y.J. A new particle swarm optimization algorithm for fuzzy optimization of armored vehicle scheme design. Appl Intell 37, 520–526 (2012). https://doi.org/10.1007/s10489-012-0345-0

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