[1]黄昆鸟,陈森发,孙燕,等.一种小生境正交遗传算法研究[J].东南大学学报(自然科学版),2004,34(1):135-137.[doi:10.3969/j.issn.1001-0505.2004.01.032]
 Huang Kun,Chen Senfa,Sun Yan,et al.Research on a niche orthogonal genetic algorithm[J].Journal of Southeast University (Natural Science Edition),2004,34(1):135-137.[doi:10.3969/j.issn.1001-0505.2004.01.032]
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一种小生境正交遗传算法研究()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
34
期数:
2004年第1期
页码:
135-137
栏目:
数学、物理学、力学
出版日期:
2004-01-20

文章信息/Info

Title:
Research on a niche orthogonal genetic algorithm
作者:
黄昆鸟1 陈森发1 孙燕2 郜振华1
1 东南大学系统工程研究所, 南京 210096; 2 中国海洋大学工程学院, 青岛 266003
Author(s):
Huang Kun1 Chen Senfa1 Sun Yan2 Gao Zhenhua1
1 Institute of Systems Engineering, Southeast University, Nanjing 210096, China
2 College of Engineering, Ocean University of China, Qingdao 266003, China
关键词:
遗传算法 全局优化 小生境 正交设计
Keywords:
genetic algorithm global optimization niche orthogonal design
分类号:
O221
DOI:
10.3969/j.issn.1001-0505.2004.01.032
摘要:
针对标准遗传算法的不足,借助正交试验法的全局均衡设计思想和二元变异操作对初始种群产生方式、交叉算子和变异算子进行了改进,提高了种群的多样性; 借助最优保留策略和自然界的小生境思想,对选择算子进行了改进,提高了算法的全局收敛性能; 另外还通过引入加速正交搜索操作,提高了算法的收敛速度.在此基础上,提出了一种小生境正交遗传算法,并进行了实例研究.研究结果表明,该算法不但可以有效地克服标准遗传算法的缺陷,而且计算速度、计算精度和算法稳定性也得到了显著提高.
Abstract:
Targeting the insufficiencies of the standard genetic algorithm(SGA), improvements are made to SGA from three aspects in this paper. Firstly, for the purpose of enhancing diversity of population, production mode of initial population, crossover operator and mutation operator are improved by using the global equilibrium design ideology of orthogonal experiment method and dual mutation operation; then for the purpose of enhancing global convergence performance of algorithm, the selection operator is improved by using elitist preservation strategy and natural niche ideology; finally, for the purpose of enhancing convergence speed of the algorithm, an accelerated orthogonal search operation is imported. On this basis, a niche orthogonal genetic algorithm(NOGA)is proposed in this paper, and case research is also done. The research results show that NOGA can not only overcome the shortcomings of SGA effectively, but also evidently improve the computing speed, computing precision and stability of the algorithm.

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备注/Memo

备注/Memo:
作者简介: 黄昆鸟(1973—),男,博士生; 陈森发(联系人),男,教授,博士生导师,chensenfa@163.com.
更新日期/Last Update: 2004-01-20