| 一种改进的均值粒子群算法 |
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| 引用本文： | 张亮,谢富强,陈立. 一种改进的均值粒子群算法[J]. 计算机系统应用, 2014, 23(5): 134-139 |
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| 作者姓名： | 张亮 谢富强 陈立 |
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| 作者单位： | 南华大学 电气工程学院, 衡阳 421001;南华大学 电气工程学院, 衡阳 421001;南华大学 电气工程学院, 衡阳 421001 |
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| 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.
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| 关 键 词： | 粒子群算法 非线性惯性权重 权重均值 均值粒子群算法 优化算法 |
| 收稿时间： | 2013-09-25 |
| 修稿时间： | 2013-10-18 |
Improved Mean Particle Swarm Optimization Algorithm |
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| ZHANG Liang,XIE Fu-Qiang and CHEN Li. Improved Mean Particle Swarm Optimization Algorithm[J]. Computer Systems& Applications, 2014, 23(5): 134-139 |
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| Authors: | ZHANG Liang XIE Fu-Qiang CHEN Li |
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| Affiliation: | University of South China, College of Electric Engineering, Hengyang 421001, China;University of South China, College of Electric Engineering, Hengyang 421001, China;University of South China, College of Electric Engineering, Hengyang 421001, China |
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| 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. |
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| Keywords: | particle swarm algorithm nonlinear inertia weight mean weight mean particle swarm algorithm optimization algorithms |
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