Modeling and Analysis of Indirect Communication in Particle Swarm Optimization

Abstract

Particle swarm optimization (PSO) has successfully been applied to many optimization problems. One particularly interesting aspect of these algorithms is to study the communication behavior of the particles. Often, a neighborhood topology is defined a priori and used throughout the optimization run. However, the cost of communication between particles has not been analyzed up to now. In this paper, we will propose a novel algorithm called DAPSO (distributed archives PSO) that makes use of stationary archives to establish indirect communication architecture in the swarms. Moreover, we provide analytical results of the required communication energy in such a scenario. This might be especially important in robot swarms and sensor networks. The applicability of our new methodology will be shown on some selected test cases.