Scalability of niche PSO

Abstract

In contrast to optimization techniques intended to find a single, global solution in a problem domain, niching (speciation) techniques have the ability to locate multiple solutions in multimodal domains. Numerous niching techniques have been proposed, broadly classified as temporal (locating solutions sequentially) and parallel (multiple solutions are found concurrently) techniques. Most research efforts to date have considered niching solutions through the eyes of genetic algorithms (GA), studying simple multimodal problems. Little attention has been given to the possibilities associated with emergent swarm intelligence techniques. Particle swarm optimization (PSO) utilizes properties of swarm behaviour not present in evolutionary algorithms such as GA, to rapidly solve optimization problems. This paper investigates the ability of two genetic algorithm niching techniques, sequential niching and deterministic crowding, to scale to higher dimensional domains with large numbers of solutions, and compare their performance to a PSO-based niching technique, Niche PSO.