Diversity-enhanced particle swarm optimization algorithm based on the group behaviour of social spiders

Abstract

Particle swarm optimization (PSO) is a representative swarm intelligence algorithm, which has the drawback of being restricted by premature convergence. To make PSO less likely to be restricted by premature convergence and to enhance its exploration and exploitation ability, this article proposes a social spider inspired particle swarm optimization (SSI-PSO). Based on PSO, the proposed algorithm divides the swarm into subgroups to mimic different behaviours of a social spider colony. Particles are classified in each iteration and then a variety of search strategies is adopted. Specifically, dominant male particles aim to search the neighbourhood, while negative female particles tend to search in the opposite direction to enhance the diversity. Meanwhile, positive female particles and non-dominant male particles are designed to balance the potential impact. Various commonly used benchmark functions and truss structural designs are tested for comparison. The results indicate that the proposed SSI-PSO is effective and efficient.

KEYWORDS:

• Heuristic algorithms
• particle swarm optimization
• diversity enhanced
• local search
• social spider

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 51705312 and 11772191].