Diversity-based Information Exchange among Multiple Swarms in Particle Swarm Optimization

This paper proposes a method to exchange information among multiple swarms in particle swarm optimization. The provided algorithm is developed to solve problems that have landscapes with a high number of local optima. Each swarm provides two sets of particles; one set is the particles to be sent to another swarm, while the other set is the particles to be replaced by individuals from other swarms. Proposed algorithm also provides a new paradigm to search for neighboring swarms in order to share common interests in the swarm's neighborhood. The particle's movement is according to one variation of PSO with three basic terms, each one to lead the particles toward the best particle in the swarm, in the neighborhood, and in the whole population. Demonstrated through a suite of benchmark test functions, the proposed algorithm is shown competitive performance with improved convergence speed.