An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

Fanrong Kong, Jianhui Jiang and Yan Huang
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Fanrong Kong: School of Software Engineering, Tongji University, Shanghai 201804, China
Jianhui Jiang: School of Software Engineering, Tongji University, Shanghai 201804, China
Yan Huang: Shanghai Development Center of Computer Software Technology, Shanghai 201112, China

Mathematics, 2019, vol. 7, issue 6, 1-13

Abstract: As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swarm preserve the exploitation ability. On the other hand, the number of sub-swarms adaptively changes from a large value to a small value, which helps the algorithm make a suitable balance between exploitation and exploration. By employing several peer algorithms, we conducted comparisons to validate the proposed algorithm on a large-scale optimization benchmark suite of CEC 2013. The experiments results demonstrate the proposed algorithm is effective and competitive to address large-scale optimization problems.

Keywords: particle swarm optimization; large-scale optimization; adaptive multi-swarm; diversity maintenance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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