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

Hao Liu (), Yue Wang, Liangping Tu, Guiyan Ding and Yuhan Hu
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Hao Liu: University of Science and Technology Liaoning
Yue Wang: University of Science and Technology Liaoning
Liangping Tu: University of Science and Technology Liaoning
Guiyan Ding: University of Science and Technology Liaoning
Yuhan Hu: University of Science and Technology Liaoning

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 6, No 6, 2407-2433

Abstract: 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.

Keywords: Particle swarm optimization; Disruption operator; Cauchy mutation; Engineering design problems (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10845-018-1403-1

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