种群熵竞争粒子群算法OA北大核心CSTPCD

To further improve the convergence and solution accuracy of competitive swarm optimizer,a variety of popula-tion entropy competitive particle swarm optimization algorithm(CSOPE)is proposed.Firstly,a nonlinear inertia weight adjustment strategy is proposed to balance the global exploration ability and local exploitation ability of particles.Secondly,a population state detection strategy based on entropy model is proposed,which calculates the population entropy by the standardized quartile difference and standardized median difference of the population.The population state is monitored by the difference in entropy values between adjacent generations of the population.When the population is in a conver-gence state,it uses gray wolf search to exploit winner particle locally to improve the convergence accuracy of the algo-rithm.The proposed CSOPE algorithm is compared with other 8 optimization algorithms on 21 test functions in CEC2008 and CEC2013,and the experimental results show that the solving accuracy and convergence of the CSOPE algorithm are significantly improved.The CSOPE algorithm is applied to the node localization problem in wireless sensor networks,and the results show that the CSOPE algorithm has high localization accuracy.