Individual and Social Behaviour in Particle Swarm Optimizers
This work addresses parameter tuning and topology design for researchers and practitioners using particle swarm optimization, but it is incremental as it builds on existing models without introducing new paradigms.
The paper investigates how inertia, individuality, and sociality weights, along with social network topologies, affect the convergence speed and form in particle swarm optimizers, finding that specific settings can optimize performance.
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.