A new dynamic probabilistic Particle Swarm Optimization with dynamic random population topology

Population topologies of Particle Swarm Optimization algorithm (PSO) have direct impacts on the information sharing amony particles during the evolution, and will influence the PSO algorithms´ performance obviously. The canonical PSO algorithms usually use static population topologies, and the majority are the classic population topologies (such as fully connected topology and ring topology). In this paper, we present the strategies of dynamic random topology based on the random generation of population topologies. The basic idea is as follows: various random topologies are used at different stages of evolution in the population, and the solving performance of PSO algorithms is enhanced by improving the information exchange of population in different evolutionary stages. This provides a new way of thinking for the improvement of the PSO algorithm. Experimental results on a relatively new variant of dynamic probabilistic particle swarm optimization show that our strategies can achieve better performance compared with traditional static population topologies. Experimental data are analyzed and discussed in the paper, and the useful conclusions will provide a basis for further research.