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.
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.
出处
《软件学报》
EI
CSCD
北大核心
2010年第6期1296-1307,共12页
Journal of Software
基金
国家自然科学基金Nos.90820302
60805027
高等学校博士学科点专项科研基金No.200805330005
湖南省研究生创新基金No.CX2009B039
中南大学研究生学位论文创新基金No.1373-74334000016~~
关键词
正交遗传算法
局部搜索
全局优化
正交实验设计
orthogonal genetic algorithm
local search
global optimization
orthogonal experimental design
