| 快速差分进化算法 |
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| 引用本文： | 安葳鹏,屈星龙. 快速差分进化算法[J]. 吉林大学学报(理学版), 2017, 55(4): 866-873 |
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| 作者姓名： | 安葳鹏 屈星龙 |
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| 作者单位： | 1. 河南理工大学 计算机科学与技术学院, 河南 焦作 454000； 2. 河南理工大学 物理与电子信息学院, 河南 焦作 454000 |
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| 摘 要： | 提出一种快速差分进化(FDE)算法.该算法采用根据上一代最优个体确定下一代搜索区间的技术不断更新和缩小搜索区域,从而加快收敛速率,提高收敛精度和鲁棒性.通过对21个极值函数仿真试验分析表明,该算法在问题维数多时,极值函数的收敛速率、收敛鲁棒性和收敛精度明显优于其他算法,且种群初始化形式不影响算法的收敛性能.
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| 关 键 词： | 快速差分进化(FDE)算法 鲁棒性 收敛速度 收敛精度 |
| 收稿时间： | 2016-11-02 |
Fast Differential Evolution Algorithm |
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| AN Weipeng,QU Xinglong. Fast Differential Evolution Algorithm[J]. Journal of Jilin University: Sci Ed, 2017, 55(4): 866-873 |
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| Authors: | AN Weipeng QU Xinglong |
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| Affiliation: | 1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000,Henan Province, China； 2. School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454000, Henan Province, China |
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| Abstract: | We presented a fast differential evolution (FDE) algorithm. The algorithm used the technique that constantly updated and narrowed the search area to determine the search interval of the next generation accordingto the previous generation optimal individual, so as to speed up the convergence rate and to improve the convergence precision and robustness. Through simulation test analysis of 21 extreme functions about optimization, the results show that the convergence rate, convergence robustness, and convergence precision of the algorithm are significantly superior to the other algorithms for the high dimensions of the problem, and the initialization form of population does not have any effects on the convergence performance of the algorithm. |
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| Keywords: | algorithm； convergence precision； robustness； convergence rate fast differential evolution (FDE) |
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