Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight - Wen-yong DONG; Lan-lan KANG; Yu-hang LIU; Kang-shun LI (Tongxin xuebao)
An opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight (OPSO-AEM＆NIW) was proposed to overcome the drawbacks, such as falling into local optimization, slow convergence speed of opposition-based particle swarm optimization. Two strategies were introduced to balance the contradiction be-tween exploration and exploitation during its iterations process. The first one was nonlinear adaptive inertia weight (NIW), which aim to accelerate the process of convergence of the algorithm by adjusting the active degree of each parti-cle using relative information such as particle fitness proportion. The second one was adaptive elite mutation strategy (AEM), which aim to avoid algorithm trap into local optimum by trigging particle's activity. Experimental results show OPSO-AEM＆NIW algorithm has stronger competitive ability compared with opposition-based particle swarm optimiza-tions and its varieties in both calculation accuracy and computation cost.