中文摘要：

针对标准粒子群优化算法早熟收敛、易陷入局部最优、收敛精度低等缺点,提出了一种改进的自适应粒子群算法.该算法在每次进化后自适应地更新每个粒子的惯性权重和学习因子,并对粒子进行排序,实现了自适应调整局部搜索和全局搜索的功能.与标准粒子群算法在6个标准测试函数上的实验进行比较并进行了t检验分析.结果表明,该算法具有很好的性能.

英文摘要：

The standard particle swarm optimization（PSO）algorithm has some defects,such as suffering from the premature convergence problem,which is easy to fall into local optima and lead to slow convergence accuracy.According to these problems,an modified adaptive particle swarm optimization（MPSO）is presented.To adaptive balance local search capability and global search capability,MPSO updates each particle＇s inertia weight and acceleration coefficients self-adaptive,and the particles are sorted.The MPSO algorithm is tested on six benchmark functions and t-test analysis is done.The experimental study shows that MPSO has a better performance in comparison with several variant PSO algorithms.