东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (3): 328-332.DOI: 10.12068/j.issn.1005-3026.2014.03.006

• 信息与控制 • 上一篇    下一篇

求解大规模可靠性问题的改进差分进化算法

孔祥勇1，高立群1，欧阳海滨1，葛延峰1,2   

  1. (1.东北大学 信息科学与工程学院， 辽宁 沈阳110819; 2.辽宁省电力有限公司， 辽宁 沈阳110014)
  • 收稿日期:2013-06-17 修回日期:2013-06-17 出版日期:2014-03-15 发布日期:2013-11-22
  • 通讯作者: 孔祥勇
  • 作者简介:孔祥勇(1988-)，男，山东滕州人，东北大学博士研究生;高立群(1949-)，男，辽宁沈阳人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61273155).

An Improved Differential Evolution Algorithm for Large Scale Reliability Problems

KONG Xiangyong1， GAO Liqun1， OUYANG Haibin1， GE Yanfeng1,2   

  1. 1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China; 2. Liaoning Electric Power Company Limited， Shenyang 110014， China.
  • Received:2013-06-17 Revised:2013-06-17 Online:2014-03-15 Published:2013-11-22
  • Contact: KONG Xiangyong
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摘要： 针对差分进化算法典型变异算子的局限，设计了全局加速的变异算子，进而提出全局加速的自适应改进算法.新变异算子能够均衡全局搜索与局部搜索，提高寻优效率.根据差分向量与整个种群分布范围的关系，有针对性的设定变异率值，减缓搜索范围缩小的趋势，保持较高的种群多样性.采用两区间选择策略，通过学习和比较自适应地调整交叉率，使其满足进化搜索的需要，同时提高算法的通用性.将改进算法应用于大规模可靠性问题中，实验结果表明，改进算法在解决大规模系统可靠性问题时具有更好的寻优效果.

关键词: 全局加速, 差分进化, 大规模可靠性问题, 两区间选择, 参数自适应

Abstract: In order to overcome the limitations of differential evolution algorithm with typical mutation operators， an improved adaptive differential evolution algorithm was proposed with a new mutation operator with global acceleration. The global acceleration operator can balance the global search and local search， such that the algorithm has higher optimization efficiency. According to the difference vector and the population distribution， the value of mutation rate was selected to slow down the trend of search scope narrow to maintain high population diversity. The crossover rate was adaptively chosen from two intervals through learning and comparing to meet the needs of the evolutionary search and improve the versatility of the algorithm. The improved algorithm is applied to the largescale reliability problems and the experimental results show that the improved algorithm achieves better optimization performance in solving largescale reliability problems.

Key words: global acceleration, differential evolution algorithm, large scale reliability problems, twointerval selection strategy, parameter adaptation

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