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A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems

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Abstract

Particle swarm optimization (PSO) has attracted the attention of many researchers because of its simple concept and easy implementation. However, it suffers from premature convergence due to quick loss of population diversity. Meanwhile, real-world engineering design problems are generally nonlinear or large-scale or constrained optimization problems. To enhance the performance of PSO for solving large-scale numerical optimizations and engineering design problems, an adaptive disruption strategy which originates from the disruption phenomenon of astrophysics, is proposed to shift the abilities between global exploration and local exploitation. Meanwhile, a Cauchy mutation is utilized to a certain dimension of the best particle to help particle jump out the local optima. Nine well-known large-scale unconstrained problems, ten complicated shifted and/or rotated functions and four famous constrained engineering problems are utilized to validate the performance of the proposed algorithm compared against those of state-of-the-art algorithms. Experimental results and statistic analysis confirm effectiveness and promising performance of the proposed algorithm.

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Acknowledgements

The authors wish to acknowledge the National Natural Science Foundation of China (Grant No. U1731128); the Doctoral Research Starting Funds of Liaoning Province (Grant No. 201601292); the Youth Science Funds of USTL (Grant No. 2014QN16); the Talent Development Program of USTL (Grant No. 2015RC04) for the financial support.

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  1. School of Science, University of Science and Technology Liaoning, Anshan, 114051, China

    Hao Liu, Yue Wang, Liangping Tu, Guiyan Ding & Yuhan Hu

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  1. Hao Liu
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Correspondence to Hao Liu.

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Liu, H., Wang, Y., Tu, L. et al. A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems. J Intell Manuf 30, 2407–2433 (2019). https://doi.org/10.1007/s10845-018-1403-1

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