Improved Particle Swarm Optimization Based on Self-adaptive Neighborhood Explored
In global optimization,particle swarm optimization(PSO) is often trapped in local optima and low accuracy in convergence.Following an analysis of the cause of the premature convergence,a novel particle swarm optimization based on self-adaptive neighborhood explored is proposed,which is called ANE-PSO.During evolution,every particle can explore the best position's neighborhood in a descend probability,the neighborhood radius can be self-adaptive to reduce,and also the velocity of mutation operator is added in.This method can break away from local optimization and enhances the global search ability.The ANE-PSO is guaranteed to converge the global optimization solution with probability one.Compared with other three improved algorithms on accuracy and convergence speed,and also on time complexity,it shows that the ANE-PSO converges faster,results in better optima,is more robust and the time complexity is not added,and prevents more effectively the premature convergence problem.