Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (10): 2812-2821.DOI: 10.11772/j.issn.1001-9081.2018030684

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Adaptive differential evolution algorithm based on multiple mutation strategies

ZHANG Qiang1,2, ZOU Dexuan1, GENG Na1, SHEN Xin1   

  1. 1. College of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou Jiangsu 221116, China;
    2. College of Information and Electrical Engineering, Xuzhou Open University, Xuzhou Jiangsu 221000, China
  • Received:2018-04-03 Revised:2018-06-04 Online:2018-10-10 Published:2018-10-13
  • Supported by:
    This work is partially supported by the National Natural Science Foundation Projects of China (61703188), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_1575).


张强1,2, 邹德旋1, 耿娜1, 沈鑫1   

  1. 1. 江苏师范大学 电气工程及自动化学院, 江苏 徐州 221116;
    2. 徐州开放大学 信息技术与电气工程学院, 江苏 徐州 221000
  • 通讯作者: 张强
  • 作者简介:张强(1980-),男,江苏宜兴人,讲师,硕士研究生,主要研究方向:群智能优化算法、电力系统经济调度;邹德旋(1982-),男,辽宁大连人,副教授,博士,主要研究方向:群智能优化算法、电力系统经济调度;耿娜(1985-),女,江苏铜山人,讲师,博士,主要研究方向:机器人路径规划、任务分配、微粒群算法;沈鑫(1994-),男,江苏盐城人,硕士研究生,主要研究方向:群智能优化算法、电力系统经济调度。
  • 基金资助:

Abstract: In order to overcome the disadvantages of Differential Evolution (DE) algorithm such as low optimization accuracy, slow convergence and poor stability, an Adaptive Differential Evolution algorithm based on Multi-Mutation strategy (ADE-MM) was proposed. Firstly, two disturbance thresholds with learning functions were used in the selection of three mutation strategies to increase the diversity of the population and expand the search scope. Then, according to the successful parameters of the last iteration, the current parameters were adjusted adaptively to improve the search accuracy and speed. Finally, vector particle pool method and central particle method were used to generate new vector particles to further improve the search effect. Tests were performed on 8 functions for 5 comparison algorithms (Random Mutation Differential Evolution (RMDE), Cross-Population Differential Evolution algorithm based on Opposition-based Learning (OLCPDE), Adaptive Differential Evolution with Optional External Archive (JADE), Self-adaptive Differential Evolution (SaDE), Modified Differential Evolution with p-best Crossover (MDE_pBX)), and each example was independently performed 30 times. The ADE-MM algorithm achieves a complete victory in the comparison of mean and variance, 5 independent wins and 3 tie wins are achieved in the 30-dimensional case; 6 independent wins and 2 tie wins are obtained in the 50-dimensional case; in 100-dimensional case, all are won independently. At the same time, in the Wilcoxon rank sum test, winning rate and time-consuming analysis, the ADE-MM algorithm also achieves excellent performance. The results show that ADE-MM algorithm has stronger global search ability, convergence and stability than other five comparison algorithms.

Key words: Differential Evolution (DE), adaptive disturbance, strategy pool, function optimization, central particle

摘要: 为了克服差分进化算法寻优精度低、收敛速度慢、稳定性差等不足,提出一种基于多变异策略的自适应差分进化算法(ADE-MM)。首先,在3个变异策略的选择过程中添加2个具有学习功能的扰动阈值,以提高种群多样性,扩大搜索范围;然后,根据上次迭代的成功参数自适应调整当前参数,提高寻优精度和寻优速度;最后,利用向量粒子池法和中心粒子法产生新的向量粒子,进一步提高寻优效果。使用8个函数、5种对比算法(RMDE、OLCPDE、JADE、SaDE、MDE_pBX)进行测试,且每种例子都独立执行30次。ADE-MM算法在均值和方差的比较中取得了全胜,其中在30维的情况下取得了5个独立胜利,3个并列胜利;在50维的情况下取得了6个独立胜利,2个并列胜利;在100维的情况下全部为独立胜利。同时在Wilcoxon rank sum test、胜率和算法耗时分析中,ADE-MM算法也取得优异的表现。实验结果表明,相对于其他5种对比算法,ADE-MM算法具有更强的全局寻优能力、收敛性和稳定性。

关键词: 差分进化, 自适应扰动, 策略池法, 函数优化, 中心粒子

CLC Number: