计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (18): 30-33.

• 博士论坛 • 上一篇    下一篇

单纯形搜索在遗传算法中的融合研究

肖宏峰1,2,谭冠政2   

  1. 1.湖南师范大学 计算机教学部,长沙 410081
    2.中南大学 信息科学与工程学院,长沙 410083
  • 收稿日期:2007-10-10 修回日期:2008-04-24 出版日期:2008-06-21 发布日期:2008-06-21
  • 通讯作者: 肖宏峰

Study on fusing simplex search into genetic algorithm

XIAO Hong-feng1,2,TAN Guan-zheng2   

  1. 1.Department of Computer Education,Hunan Normal University,Changsha 410081,China
    2.College of Information Science & Engineering,Central South University,Changsha 410083,China
  • Received:2007-10-10 Revised:2008-04-24 Online:2008-06-21 Published:2008-06-21
  • Contact: XIAO Hong-feng

摘要: 构造了单纯形混合遗传算法SM-HGA+。分析单纯形搜索算法,提出了单纯形交叉算子和K步随机单纯形搜索算子,并将单纯形搜索算法和这两个算子分别融入到最优微群体μPBt)、最差微群体μPWt)和普通群体PCt),形成SM-HGA+。最优微群体中的单纯搜索算法提高算法的精度;最差微群体中的单纯形交叉算子加速最差个体向优秀个体进化;普通群体中K步随机单纯性搜索提高全局搜索速度,同时在普通群体采用大交叉概率的标准遗传算法,提高全局搜索能力。遗传算法测试函数验证算法SM-HGA+的正确性、效率。

Abstract: This paper describes simplex hybrid genetic algorithm called SM-HGA+.In detail,by analyzing Neld Meld Simplex algorithm,the authors propose simplex crossover operator and K-step random simplex search operator,and respectively fuse Neld Mead Simplex algorithm and above two novel operators into the best population μPBt),the worst population μPWt) and a common population PCt) in a genetic algorithm in order to construct algorithm SM-HGA+.Neld Mead Simplex algorithm in μPBt) raises the computation precision;the simplex crossover operator in μPWt) accelerates the worst individuals evolving towards better individuals;the K-step random simplex search operator enhances the global convergence speed and genetic algorithm with big crossover probability improves global search performance of algorithm SM-HGA+ by population PCt).The standard testing functions test and verify the correctness and efficiency of SM-HGA+.