doi:10.11772/j.issn.1001-9081.2014.06.1641

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (6): 1641-1644.DOI: 10.11772/j.issn.1001-9081.2014.06.1641

• Artificial intelligence • Previous Articles Next Articles

LI Zuoyong¹,ZHANG Xiaoli¹,WANG Jiayang²,ZHANG Zhengjian¹

1. College of Resources and Environment, Chengdu University of Information Technology, Chengdu Sichuan 610041, China;
2. Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu Sichuan 610041, China

Received:2013-11-12 Revised:2014-01-02 Online:2014-07-02 Published:2014-06-01
Contact: LI Zuoyong

免疫进化混合猴王遗传算法

李祚泳¹,张小丽¹,汪嘉杨²,张正健¹

1. 成都信息工程学院资源环境学院,成都 610041
2. 中国科学院成都山地灾害与环境研究所,成都 610041

通讯作者: 李祚泳
作者简介:李祚泳(1944-),男,四川宜宾人,教授,博士生导师,主要研究方向:优化算法、人工神经网络;张小丽(1990-),女,重庆人,硕士研究生,主要研究方向:优化算法、环境信息分析;张正健(1986-),男,重庆人,硕士,主要研究方向:优化算法、环境信息分析;汪嘉杨（1980-），女，副教授，博士，主要研究方向：环境系统分析。
基金资助:
国家自然科学基金资助项目

Abstract

Abstract:

Aiming at the limitations of easily falling into local minimum and poor stability in simple Monkey-King Genetic Algorithm (MKGA), a MKGA by Immune Evolutionary Hybridized (MKGAIEH) was proposed. MKGAIEH divided the total population into several sub-groups. In order to make full use of the best individual (monkey-king) information of total population, the Immune Evolutionary Algorithm (IEA) was introduced to iterative calculation. In addition, for the other individuals in the sub-groups, the crossover and mutation operations were performed on the monkey-kings of sub-groups and total population. As local searches of all sub-groups were completed, the solutions of sub-groups were mixed again. As the iteration proceeds, this strategy combined the global information exchange with local search is not only to avoid the premature convergence, but also to approximate the global optimal solution with a higher accuracy. Comparison experiments on 6 test functions using MKGAIEH, MKGA, Improved MKGA (IMKGA), Bee Evolutionary Genetic Algorithm (BEGA), Algorithm of Shuffled Frog Leaping based on Immune Evolutionary Particle Swarm Optimization (IEPSOSFLA), and Common climbing Operator Genetic Algorithm (COGA) were given. The results show that the MKGAIEH can find the global optimal solutions for all 6 test functions, and the mean values and standard deviation accuracy of 5 test functions achieve the minimums with improving several orders of magnitude than those of the comparison algorithms. Therefore, MKGAIEH has the optimal searching ability and the stability all the better.

摘要：

针对简单猴王遗传算法（MKGA）存在易陷入局部极值和稳定性较差的缺陷,提出了免疫进化混合猴王遗传算法（MKGAIEH）。MKGAIEH将总群体划分为若干个子群体,为了充分利用总群体中最优个体(总猴王)信息,引入免疫进化算法（IEA）对其进行免疫进化迭代计算;此外,对子群体内的其他个体,同时考虑子群体的子猴王与群体的总猴王对其进行交叉和变异遗传操作。当所有子群体的局部搜索完成后,再将各子群体的解重新混合。这种全局信息交换与子群内局部搜索相结合的策略不仅避免了早熟收敛,而且随着迭代的进行,还能以更高的精度逼近全局最优解。将MKGAIEH、MKGA、改进后的猴王遗传算法（IMKGA）、蜜蜂遗传算法（BEGA）、免疫进化粒子群蛙跳算法（IEPSOSFLA）和普通爬山算子遗传算法（COGA）对6个典型测试函数的计算结果进行了比较，其结果为:MKGAIEH对6个测试函数都能获得全局最优解,有5个测试函数获得的平均值和标准差比其他5种优化算法获得的平均值和标准差精度提高了几个数量级,达到了最小。这表明MKGAIEH具有更佳的寻优能力和更好的稳定性。

CLC Number:

TP18

LI Zuoyong ZHANG Xiaoli WANG Jiayang ZHANG Zhengjian. [J]. Journal of Computer Applications, 2014, 34(6): 1641-1644.

李祚泳张小丽汪嘉杨张正健. 免疫进化混合猴王遗传算法[J]. 计算机应用, 2014, 34(6): 1641-1644.

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免疫进化混合猴王遗传算法

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