Differential Opposition-Based Particle Swarm

Particle Swarm Optimization (PSO) is slow but steady learner although it exhibits strong competence in solving complicated problems. However, during the course of searching process, the particles gradually gather into the vicinity of the best particle found so far. Furthermore, some evidences show that the unreasonable setting of its inertial term in the kinetic equations may lead to slow convergence of PSO. Thus, a differential opposition-based particle swarm optimization with adaptive elite mutation (DOPSO) is presented to overcome these drawbacks in this paper. There are two strategies are introduced into DOPSO to balance the contradiction between exploration and exploitation during its searching process: (1) Firstly a new particle’s position update rule in which differential term replaces the inertia term is designed to accelerate its convergence; (2) Secondly an adaptive elite mutation strategy (AEM) is included to avoid trapping into local optimum. Experimental results show that the proposed method has a significant improvement in performance compared with some state-of-art PSOs.