计算机工程 ›› 2008, Vol. 34 ›› Issue (4): 231-232.doi: 10.3969/j.issn.1000-3428.2008.04.082

• 人工智能及识别技术 • 上一篇    下一篇

基于改进郭涛算法的CCEA函数优化问题

张 萍,李 涛,李振华   

  1. (中国地质大学计算机学院,武汉 430074)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-02-20 发布日期:2008-02-20

Improved-GT-Operator-Based Cooperative Co-evolutionary lgorithm to Function Optimization

ZHANG Ping, LI Tao, LI Zhen-hua   

  1. (Department of Computer, China University of Geosciences, Wuhan 430074)

  • Received:1900-01-01 Revised:1900-01-01 Online:2008-02-20 Published:2008-02-20

摘要: 郭涛算法在求解函数优化问题方面具有独特的优势,其核心在于多父体杂交。鉴于郭涛算法只有杂交操作而没有变异操作,该文引入高斯正态分布变异算子,提高了对复杂问题的求解效率。分析合作式协同演化算法(CCEA),采用多种群相互作用协同进化的策略求解复杂问题。同时在合作式协同演化模型中引入了郭涛算法,求解复杂高维的函数优化问题。实验结果表明,该模型的效率优于其他模型。

关键词: 郭涛算法, 高斯变异算子, 合作式协同演化算法

Abstract: Guo Tao(GT) algorithm is highly effective in solving function optimization problems. The core of the algorithm is multi-parent recombination, but it only has crossover operator, no mutation. Gauss mutation operator of Evolution Strategies(ES) is introduced. Cooperative Co-Evolution Algorithm(CCEA) uses multi-population strategy, which means that the fitness of an individual depends on the relationship between that individual and other individuals. CCEA is high-efficient in solving complicated problem. A cooperative co-evolution model to function optimization is proposed, based on an improved Guo Tao operator, which employs a Gauss mutation operator to enhance its exploring ability. Experimental results show that the model is more effective than others.

Key words: Guo Tao algorithm, Gauss mutation operator, cooperative co-evolution algorithm

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