Uniform Opposition-Based Particle Swarm

Uniform opposition-based particle swarm optimization (NOPSO) is proposed to overcome the drawbacks, such as, slow convergence speed, falling into local optimization, of opposition-based particle swarm optimization. Two mechanisms are introduced to balance the contradiction between exploration and exploitation during searching process. 1) Firstly, a new particle's position update rule in which uniform 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.