| 差分进化算法综述 |
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| 引用本文： | 丁青锋,尹晓宇.差分进化算法综述[J].智能系统学报,2017,12(4):431-442. |
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| 作者姓名： | 丁青锋 尹晓宇 |
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| 作者单位： | 1. 华东交通大学 电气与自动化工程学院, 江西 南昌 330013;2. 上海大学 特种光纤与光接入重点实验室, 上海 200072 |
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| 摘 要： | 差分进化算法由于算法结构简单易于执行，并且具有优化效率高、参数设置简单、鲁棒性好等优点，因此差分进化算法吸引了越来越多研究者的关注。本文概述了差分进化算法的基本概念以及存在的问题，综述了差分进化算法的控制参数、差分策略、种群结构以及与其他最优化算法混合等4个方面改进策略并讨论它们各自的优缺点，为差分进化算法下一步的改进提出了参考方向。
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| 关 键 词： | 差分进化 启发式并行搜索 差分策略 控制参数 种群结构 混合优化 收敛速度 优化效率 |
Research survey of differential evolution algorithms |
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| DING Qingfeng,YIN Xiaoyu.Research survey of differential evolution algorithms[J].CAAL Transactions on Intelligent Systems,2017,12(4):431-442. |
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| Authors: | DING Qingfeng YIN Xiaoyu |
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| Affiliation: | 1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China;2. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200072, China |
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| Abstract: | Due to its simple algorithm structure, ease of performance, high optimization efficiency, simple parameter setting, and excellent robustness, the differential evolution (DE) algorithm has attracted increasing attention from researchers. In this paper, we outline the basic concepts of the DE algorithm as well as its limitations, and review four improvement strategies, including a control parameter, differential strategy, population structure, and mixing it with other optimization algorithms. We discuss the advantages and disadvantages of these strategies and suggest directions for future improvements to the DE algorithm. |
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| Keywords: | differential evolution algorithm heuristic parallel search differential strategies control parameter population structure mixed optimization convergence rate optimization efficiency |
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