中文摘要：

对基本粒子群优化算法作了一些改进：通过去掉速度因子简化算法结构,引入指数下降形式的惯性权重,对全局极值进行自适应的变异操作,进而提出一种简化的带变异算子的自适应粒子群优化算法。通过与其他改进的粒子群算法的数值实验对比分析,表明提出的新算法能够有效地避免早熟收敛问题,并能较大幅度地提高收敛速度和收敛精度。

英文摘要：

A new particle swarm optimization（PSO） algorithm is presented based on three methods of improvement in original PSO.First,the iteration formula of PSO is changed and simplified by removal of velocity parameter that is unnecessary during the course of evolution.Second,the dynamically decreasing inertia weight is employed to enhance the balance of global and local search of algorithm.Finally,the mutation operator is introduced to improve the search performance of algorithm.Experimental results show that the new algorithm not only outperforms standard PSO in terms of accuracy and convergence rate but also avoids effectively being trapped in local minima.