Heterogeneous Bare-Bones Particle Swarm Optimization for Dynamic Environments

Particle swarm optimization is an effective technique to track and find optimum in dynamic environments. In order to improve convergence accuracy of solutions, a heterogeneous bare-bones particle swarm optimization (HBPSO) is proposed in which several master swarms and a slaver swarm are employed to exploration search and exploitation search, respectively. When detecting environments change, a new strategy is used to update the position of particles for keeping swarm diversity. If the search areas of two swarms are overlapped, the worse swarm will be initialized. Experimental results on moving peaks benchmark (MPB) functions show that the proposed algorithm is effective and easy to implement.