Individual and Social Behaviour in Particle Swarm Optimizers

TL;DR

This paper investigates how individual and social factors, including coefficients and network topology, influence the convergence behavior of particle swarm optimizers.

Contribution

It provides a comprehensive analysis of how different coefficient settings and social network structures affect PSO performance and convergence patterns.

Findings

Different coefficient settings significantly impact convergence speed.

Network topology influences the exploration and exploitation balance.

Optimal configurations depend on problem characteristics.

Abstract

Three basic factors govern the individual behaviour of a particle: the inertia from its previous displacement; the attraction to its own best experience; and the attraction to a given neighbour's best experience. The importance awarded to each factor is controlled by three coefficients: the inertia; the individuality; and the sociality weights. The social behaviour is ruled by the structure of the social network, which defines the neighbours that are to inform of their experiences to a given particle. This paper presents a study of the influence of different settings of the coefficients as well as of the combined effect of different settings and different neighbourhood topologies on the speed and form of convergence.