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Diversity enhanced particle swarm optimization algorithm and its application in vehicle lightweight design

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Abstract

Particle swarm optimization, a widely used metaheuristic algorithm, mimics the cooperation behavior among species. The PSO algorithm has become a new trend owing to its simplicity and strong optimization capacity. However, premature convergence problem is also a serious issue for PSO comparable with other evolutionary algorithms. Diversity loss is generally known as one of the major causes. For enhancing the diversity of swarms during optimization procedure, an improved PSO algorithm named OLAR-PSO-d is proposed, which incorporates design of experiment technique as well as adaptive reset operator into standard PSO. The OLAR-PSO-d algorithm is compared with other 10 heuristic algorithms. The numerical experiments’ results demonstrate the priority of OLAR-PSO-d both in optimization ability and algorithm stability. The proposed algorithm is also used in a vehicle lightweight design problem. The auto-body achieves 20.25 kg weight reduction with meeting all the performance requirements of crashworthiness.

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Authors and Affiliations

  1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China

    Zhao Liu, Han Li & Ping Zhu

Authors
  1. Zhao Liu
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  2. Han Li
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  3. Ping Zhu
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Corresponding author

Correspondence to Ping Zhu.

Additional information

Recommended by Associate Editor Gang-Won Jang

Zhao Liu is a post-doctor of Shanghai Jiao Tong University. He got his Ph.D. degree from Shanghai Jiao Tong University in 2016. Zhao’s research areas include: a) Intelligent optimization algorithm; b) Multidisciplinary optimization design; c) Lightweight design of Autobody; d) Multi-scale optimization design. He is responsible for two national foundation projects of China which are related to the improvement of PSO algorithm.

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Liu, Z., Li, H. & Zhu, P. Diversity enhanced particle swarm optimization algorithm and its application in vehicle lightweight design. J Mech Sci Technol 33, 695–709 (2019). https://doi.org/10.1007/s12206-019-0124-5

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  • DOI: https://doi.org/10.1007/s12206-019-0124-5

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