Abstract
The particle swarm optimization (PSO) algorithm is designed to find a single
optimal solution and needs some modifications to be able to locate multiple optima on a multimodal function. In parallel with evolutionary computation algorithms, these modifications can be grouped in the framework
of niching. In this work, we present a new approach to niching in PSO based on
clustering particles to identify niches. The neighborhood structure, on which particles rely
for communication, is exploited together with the niche information to locate multiple optima
in parallel. Our approach was implemented in the