Multi-population particle swarm optimization concerning the worst information

Haiyan Li; Yanbo Chen; Jiulin Sun; Mi Li

doi:10.1117/12.2684316

1 August 2023 Multi-population particle swarm optimization concerning the worst information

Haiyan Li, Yanbo Chen, Jiulin Sun, Mi Li

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127542P (2023) https://doi.org/10.1117/12.2684316
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Currently, the majority of multi-population algorithms ignore the worst particles and instead learn from the gbest or lbest. Hence, it is simple for these strategies to result in inadequate population variety, which causes early convergence. A multi-population particle swarm optimization considering the worst particles into account (CWMPSO) is suggested to demonstrate the importance of the worst particle information. In addition to considering the best particle, the CWMPSO algorithm concentrates on the worst particle globally. The algorithm divides the population into three subgroups, which learns from the gbest, gworst, and gbest + gworst, respectively. The development of the global worst particle information can be achieved by group learning, allowing for a more thorough exploration of potential optimum solutions and an avoidance of local functional ones. 30 complex CEC2017 benchmark functions were used in this work to test a range of algorithms including CWMPSO with various dimensions. The outcomes demonstrate that, in every dimension, the CWMPSO algorithm is better than other advanced PSOs.

Citation Download Citation

Haiyan Li, Yanbo Chen, Jiulin Sun, and Mi Li "Multi-population particle swarm optimization concerning the worst information", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127542P (1 August 2023); https://doi.org/10.1117/12.2684316

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