Particle swarm optimizer with diversity measure based on swarm representation in complex network

Abstract

In this paper a alternative approach to the diversity guided particle swarm optimization (PSO) is investigated. The PSO shows acceptable performance on well-known test problems, however tends to suffer from premature convergence on multi-modal test problems. This premature convergence can be avoided by increasing diversity in search space. In this paper we introduce diversity measure based on graph representation of swam evolution and we discuss possibilities of graph representation of swarm population in adaptive control of PSO algorithm. Based on our findings we concluded, that network representation of evolution population and its subsequent analysis can be used in adaptive control, in various degrees of success.