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μPB(t)、最差微群体μPW(t)和普通群体PC(t),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。
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 μPB（t）,the worst population μPw（t） and a coin mon population Pc（t） in a genetic algorithm in order to construct algorithm SM-HGA＋.Neld Mead Simplex algorithm in μPB（t） raises the computation precision;the simplex crossover operator in μPW （t） accelerates the worst individuals evolving towards better indlviduals;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 Pc （t）.The standard testing functions test and verify the correctness and efficiency of SM-HGA .
出处
《计算机工程与应用》
CSCD
北大核心
2008年第18期30-33,共4页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.5027150)
湖南省教育厅一般项目(the Common Project of Bureau of Education of Hunan Province under Grant No.05C410)
