| 一种改进的微分进化算法 |
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| 引用本文： | 王创业,高浩.一种改进的微分进化算法[J].电脑与信息技术,2011,19(6):50-53. |
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| 作者姓名： | 王创业 高浩 |
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| 作者单位： | 1. 华北电力大学,北京,102206
2. 清华大学自动化系,北京,100084 |
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| 摘 要： | 相对于其他优化算法来说，微分进化算法具有控制参数少、易于使用以及鲁棒性强等特点，但在搜索过程中存在着局部搜索能力弱的缺点。针对微分进化算法局部搜索能力弱的缺点，提出了一种基于局部变异的微分进化算法，该算法使个体具有良好快速收敛能力。使用典型优化函数对比较算法进行了测试，算法分析和仿真结果表明，改进以后的算法具有寻优能力...
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| 关 键 词： | 微分进化算法 变异 局部搜索 收敛速度 |
An improving Differential Evolution Algorithm |
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| WANG Chuang-ye,GAO Hao.An improving Differential Evolution Algorithm[J].Computer and Information Technology,2011,19(6):50-53. |
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| Authors: | WANG Chuang-ye GAO Hao |
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| Affiliation: | 1.North China Electric Power University,Beijing,102206,China;2.Fengyang Power Supply Company,Fengyang 233100,China; 3.Automation Department,Tsinghua University,Beijing 100084,China) |
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| Abstract: | Differential evolution algorithm （DE） has better search performance for many optimization problems with few control parameters, easy to use and robust when it is compared with other evolutionary algorithms. But the local search ability of DE is weak. For conquering this drawback of DE, a differential evolution algorithm based local mutation （LMDE） is proposed in this paper. Then the improved algorithm shows fist convergence rate. Numerical study is carried by using benchmark functions. Experiment simulations show that the proposed algorithm has powerful optimizing ability, good stability and higher optimizing precision, so it can be applied in optimization problems. |
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| Keywords: | differential evolution algorithm mutation local search convergence rate |
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