Particle swarm optimizer enhanced with local genetic operator

Han Zhou; Yinghong Ma

doi:10.1117/12.2638694

17 May 2022 Particle swarm optimizer enhanced with local genetic operator

Han Zhou, Yinghong Ma

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122594D (2022) https://doi.org/10.1117/12.2638694
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

As is known, effective learning exemplar and proper topology structure play important roles for improving the performance of Particle swarm optimization. Traditional learning exemplars are often criticized due to the loss of population diversity and easy falls to local optima. Aiming for constructing diversified learning exemplar, in this paper, particle swarm optimizer with local genetic operator (PSOLG) is introduced. PSOLG uses genetic operation to generate promising learning exemplars for particles. In addition, ring topology is utilized for maintaining population diversity. The proposed PSOLG is against with 7 PSO and non-PSO methods. The experiment shows that the PSOLG performs better than peer algorithms for solving optimization problems.

Citation Download Citation

Han Zhou and Yinghong Ma "Particle swarm optimizer enhanced with local genetic operator", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122594D (17 May 2022); https://doi.org/10.1117/12.2638694

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