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Received:September 25, 2013 Revised:October 18, 2013
Received:September 25, 2013 Revised:October 18, 2013
中文摘要: 为了解决粒子群算法存在“早熟”现象和收敛速度慢的问题，本文提出一种改进的均值粒子群算法. 该算法采用非线性惯性权重，同时在每个迭代步，将粒子历史最优和种群全局最优取均值再乘以一个非线性权重的方法，以提高算法的全局搜索能力和收敛速度. 通过4个标准函数的测试，实验结果表明该算法的有效性.
Abstract:In order to solve premature phenomenon and slow convergence problems in particle swarm algorithm, an improved mean particle swarm algorithm is provided. The algorithm apply to the nonlinear weight. At the same time in each iteration step, the history optimal particle and the global optimal population were averaged and multiplied a nonlinear weight so that it improve the global search capacity and convergence speed. Through four standard function test, The results show that the effectiveness of the proposed algorithm.
keywords: particle swarm algorithm nonlinear inertia weight mean weight mean particle swarm algorithm optimization algorithms
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张亮,谢富强,陈立.一种改进的均值粒子群算法.计算机系统应用,2014,23(5):134-139
ZHANG Liang,XIE Fu-Qiang,CHEN Li.Improved Mean Particle Swarm Optimization Algorithm.COMPUTER SYSTEMS APPLICATIONS,2014,23(5):134-139
张亮,谢富强,陈立.一种改进的均值粒子群算法.计算机系统应用,2014,23(5):134-139
ZHANG Liang,XIE Fu-Qiang,CHEN Li.Improved Mean Particle Swarm Optimization Algorithm.COMPUTER SYSTEMS APPLICATIONS,2014,23(5):134-139
