Particle swarm optimization with area extension (AEPSO)

Abstract

Particle swarm optimization (PSO) is one of the evolutionary algorithms which proved to be useful in solving multi-robots tasks. PSO outperforms other evolutionary algorithms, such as GA, in this area. In this paper we introduce a new modified version of PSO called area extension PSO (AEPSO). Information about the environment in extended area together with various heuristics improves the performance of each robot and the group. We believe this AEPSO is suitable to solve problems in environments with large area which have more similarity to real world robotic problems. The result of this study shows a magnificent improvement and the potential of AEPSO, especially in dynamic environments.