Co-evolutionary particle swarm optimization based on population entropy

Previous work presented an approach based on co-evolutionary particle swarm optimization (CPSO) to solve optimization problems. Preliminary results demonstrated that CPSO constitutes a promising approach to solve optimization problems. However how to the particles migrate in the process of collaborative evolution become challenging. In this paper, a modified CPSO based on population entropy is applied in the context of ECPSO. The entropy is used to measure the diversity of the whole population and then guide the particles how to migrate. The ECPSO is tested on some benchmark optimization problems and the results show a superior performance compared to the standard PSO and CPSO.