PSO with Coupled Map Lattice and Worker Ant’s Law

摘要

In this study, we present two types of approaches for developing of particle swarm optimization (PSO). Firstly, we introduce coupled map lattice (CML) that is used a logistic map, to the moving equation of PSO. We consider that the PSO obtains two parts which are moving according to random vector and moving according similar vector by the CML phenomena. Secondly, we introduce Worker ant’s law to the moving equation of PSO. The Worker ant’s law is a variant of the Pareto’s law. The ratio of ants is 2:6:2 that include hard working ants, normal ants, and not working ants, respectively. We give the different momentum term for each swarm that is divided according to Worker ant’s law. It is considered that the search range can be expanded by the different momentum term without the divergence of solution search. We confirm that the CML and Worker ant’s law improve the solution searching ability of PSO by comparison with the general PSOs.