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聚类佳点集交叉的约束优化混合进化算法
引用本文:龙文,梁昔明,徐松金,陈富.聚类佳点集交叉的约束优化混合进化算法[J].计算机研究与发展,2012,49(8):1753-1761.
作者姓名:龙文  梁昔明  徐松金  陈富
作者单位:1. 贵州财经大学贵州省经济系统仿真重点实验室 贵阳 550004;中南大学信息科学与工程学院 长沙 410083
2. 中南大学信息科学与工程学院 长沙 410083
3. 铜仁学院数学与计算机科学系 贵州铜仁 554300
基金项目:国家自然科学基金项目,湖南省研究生科研创新项目,贵州财经大学引进人才科研启动项目
摘    要:提出一种基于聚类佳点集多父代交叉和自适应约束处理技术的混合进化算法用于求解约束优化问题.新算法的主要特点是:在搜索机制方面,利用佳点集方法构造初始化种群,使个体能够均匀地分布在整个搜索空间.然后根据父代个体的相似度将种群个体进行聚类分析,从聚类中随机选择个体进行佳点集多父代交叉操作,利用多个父代个体所携带的信息产生新的具有代表性的子代个体,能够维持和增加种群的多样性.另外,引入局部搜索策略以提高算法局部搜索能力和收敛速度.在约束处理技术上,新算法引入了一个自适应约束处理技术,即根据当前种群中可行解的比例自适应选择不同的个体比较准则.通过15个标准测试函数验证了新算法的有效性.

关 键 词:约束优化  进化算法  聚类  自适应  佳点集

A Hybrid Evolutionary Algorithm Based on Clustering Good-Point Set Crossover for Constrained Optimization
Long Wen , Liang Ximing , Xu Songjin , Chen Fu.A Hybrid Evolutionary Algorithm Based on Clustering Good-Point Set Crossover for Constrained Optimization[J].Journal of Computer Research and Development,2012,49(8):1753-1761.
Authors:Long Wen  Liang Ximing  Xu Songjin  Chen Fu
Institution:1(Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang 550004)2(School of Information Science and Engineering, Central South University, Changsha 410083)3(Department of Mathematics and Computer Science, Tongren University, Tongren, Guizhou 554300)
Abstract:A hybrid evolutionary algorithm based on multi-parent crossover of clustering good-point set and adaptive constraint-handling technique is proposed in this paper for solving constrained optimization problems. As for search mechanism, it utilizes good-point set to construct the initialization population that is scattered uniformly over the entire search space in order to maintain the diversity. The individuals of population are divided into several sub-populations according to the similarity of the two parents. The parents are selected randomly from the several sub-populations to arrange the crossover operation. The crossover operator can effectively make use of the information carried by the parents and generate representation offspring in order to maintain and increase the diversity of population. In addition, a local search scheme is introduced to enhance the local search ability and speed up the convergence of the proposed algorithm. As for constraint-handling technique, a new individual comparison criterion is proposed, which can adaptively select different individual comparison criterion according to the proportion of feasible solution in current population. The proposed algorithm is tested on 15 well-known benchmark functions, and the empirical evidence shows its effectivity.
Keywords:constrained optimization  evolutionary algorithm  clustering  adaptive  good point set
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