计算机应用 ›› 2015, Vol. 35 ›› Issue (10): 2933-2938.DOI: 10.11772/j.issn.1001-9081.2015.10.2933

• 人工智能 • 上一篇    下一篇

混合多约束处理技术的并行约束差分进化算法

魏文红   

  1. 东莞理工学院 计算机学院, 广东 东莞 523808
  • 收稿日期:2015-04-17 修回日期:2015-06-11 出版日期:2015-10-10 发布日期:2015-10-14
  • 通讯作者: 魏文红(1977-),男,江西南昌人,副教授,博士,CCF会员,主要研究方向:网络与并行分布计算、智能优化处理,weiwh@dgut.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61103037,61300198);广东省自然科学基金资助项目(S2013010011858);广东省高校科技创新项目(2013KJCX0178)。

Parallel constrained differential evolution algorithm merging with multi-constraint handling techniques

WEI Wenhong   

  1. School of Computer, Dongguan University of Technology, Dongguan Guangdong 523808, China
  • Received:2015-04-17 Revised:2015-06-11 Online:2015-10-10 Published:2015-10-14

摘要: 针对约束差分进化算法中单一约束处理技术无法适合所有优化问题的情况,提出了一种混合多种约束处理技术的并行约束差分进化算法。该算法将种群分成多个子种群,各子种群采用不同的约束处理技术并行地独立进化,在适应值评价时进行种群间的通信交流。通过混合4种约束处理技术,使得算法对于所有测试函数都能成功地寻找到最优解,而且运算时间是串行算法的1/4。实验结果表明:与相应的串行算法及采用单一约束处理技术的算法比较,所提算法具有更高的求解精度、更少的计算时间和更快的收敛速度。

关键词: 约束处理技术, 差分进化, 约束优化, 并行, 收敛性

Abstract: Aiming at the problem that constrained differential evolution with single constraint handing technique is not suitable for all constrained optimization problems, a parallel constrained differential evolution algorithm using multi-constraint handing techniques was proposed. The algorithm divided an initial population into several sub-populations, and then the sub-populations evolved with different constraint handing techniques in parallel, they communicated with each other at fitness evaluation. By using four constraint handing techniques, the algorithm can find the best known optimization solution and compared with serial algorithm, the computation time is 1/4 while solving all benchmark functions. The experimental results show that the propsed algorithm is able to decrease computation time, and improve solution accuracy and convergence speed in the majority of test cases compared with corresponding serial algorithm and those algorithms which only use one constraint handing technique.

Key words: constraint handing technique, Differential Evolution (DE), constrained optimization, parallel, convergence

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