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中国科学院软件研究所
  
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江中央,蔡自兴,王 勇.求解全局优化问题的混合自适应正交遗传算法.软件学报,2010,21(6):1296-1307
求解全局优化问题的混合自适应正交遗传算法
Hybrid Self-Adaptive Orthogonal Genetic Algorithm for Solving Global Optimization Problems
  修订日期:2009-02-16
DOI:
中文关键词:  正交遗传算法  局部搜索  全局优化  正交实验设计
英文关键词:orthogonal genetic algorithm  local search  global optimization  orthogonal experimental design
基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.90820302, 60805027 (国家自然科学基金); the Specialized Research Foud for the Doctoral Program of Higher Education of China under Grant No.200805330005 (高等学校博士学科点专项科研基金); the Graduate Innovation Fund of Hu’nan Province of China under Grant No.CX2009B039 (湖南省研究生创新基金); the Graduate Degree Thesis Innovation Foundation of Central South University of China under Grant No.1373-74334000016 (中南大学研究生学位论文创新基金)
作者单位
江中央 中南大学 信息科学与工程学院,湖南 长沙 410083 
蔡自兴  
王 勇  
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全文下载次数: 3505
中文摘要:
      提出了一种基于正交实验设计的混合自适应正交遗传算法(hybrid self-adaptive orthogonal genetic algorithm,简称HSOGA)以求解全局优化问题,此算法利用正交实验设计方法设计交叉算子,并提出一种自适应正交交叉算子.该自适应正交交叉算子根据父代个体的相似度自适应地调整正交表的因素个数和对父代个体进行因素分割的位置,生成具有代表性的子代个体,以更好地搜索空间.此外,新算法利用自适应正交交叉算子生成均匀分布的初始种群,以保证初始群体的多样性.同时引入了局部搜索策略以提高算法局部搜索能力和收敛速度.通过14个高维的Benchmark函数验证了算法的通用性和有效性.
英文摘要:
      This paper presents a hybrid self-adaptive orthogonal genetic algorithm (HSOGA) based on orthogonal experimental design method for solving global optimization problems. In HSOGA, the orthogonal experimental design method is utilized to design crossover operator, and as a result, a self-adaptive orthogonal crossover operator is proposed. The self-adaptive orthogonal crossover operator self-adaptively adjusts the number of orthogonal array’s factors and the location for dividing the parents into several sub-vectors according to the similarity of the two parents, in order to produce a small but representative set of points as the potential offspring. In addition, in HSOGA the self-adaptive orthogonal crossover operator is also adopted to generate an initial population that is scattered uniformly over the feasible solution space in order to maintain the diversity. Moreover, a local search scheme is incorporated into HSOGA in the purpose of enhancing the local search ability and speeding up the convergence of HSOGA. HSOGA is tested with fourteen benchmark functions. The experimental results suggest that HSOGA is generic and effective.
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