多策略粒子群优化算法

为了克服粒子群优化算法易早熟、局部搜索能力弱的问题，提出了一种改进的粒子群优化算法--多策略粒子群优化算法。在群体寻优过程中，各粒子根据搜索到的最优位置的变动情况，从几种备选的策略中抉择出当代的最优搜索策略。其中，最优粒子有最速下降策略、矫正下降策略和随机移动策略可以选择，非最优粒子有聚集策略和扩散策略可以选择。四个典型测试函数的数值实验结果表明，新提出的算法比标准粒子群优化算法具有更强和更稳定的全局搜索能力。

Multi-strategy particle swarm optimization algorithm

Like many other intelligent optimization algorithms,Particle Swarm Optimization (PSO) algorithm often suffers from premature convergence,especially in multi peak problems.In addition,the convergence accuracy of its local search is unsatisfactory.To overcome these problems,a novel improved PSO,named Multi strategy Particle Swarm Optimization (MPSO) algorithm,is proposed.In the evolution of particle swarms,each particle chooses its own contemporary optimal search strategy from multiple alternative strategies according to the changes of optimal position it finds.Among these strategies,the steepest descent strategy,the corrective decline strategy and the random mobile strategy are able to be chosen by the optimal particle,while the aggregation strategy and the diffusion strategy are available for non optimal particles.In the end,the performance of MPSO is tested with four typical test functions and numerical results indicate that the proposed MPSO algorithm has a stronger and more stable global search ability than the standard PSO algorithm.