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动态分级的并行约束优化进化算法
引用本文:邹木春. 动态分级的并行约束优化进化算法[J]. 计算机应用研究, 2011, 28(11): 4150-4152. DOI: 10.3969/j.issn.1001-3695.2011.11.040
作者姓名:邹木春
作者单位:宜春学院数学与计算机学院,江西宜春,336000
摘    要:提出一种动态分级的并行进化算法用于求解约束优化问题。该算法首先利用佳点集方法初始化种群。在进化过程中,将种群个体分为两个子种群,分别用于全局和局部搜索,并根据不同的搜索阶段动态调整各种级别中并行变量的数目。标准测试问题的实验结果表明了该算法的可行性和有效性。

关 键 词:约束优化; 进化算法; 动态分级; 并行

Dynamic hierarchical parallel constrained optimization evolutionary algorithm
ZOU Mu-chun. Dynamic hierarchical parallel constrained optimization evolutionary algorithm[J]. Application Research of Computers, 2011, 28(11): 4150-4152. DOI: 10.3969/j.issn.1001-3695.2011.11.040
Authors:ZOU Mu-chun
Affiliation:(School of Computer & Mathematics, Yichun College, Yichun Jiangxi 336000, China)
Abstract:This paper proposed dynamic hierarchical parallel evolutionary algorithm for solving constrained optimization problems. In this approach, introduced the individuals generation based on good-point-set method into the evolutionary algorithm initial step. During the evolution process, divided the initial population into two subpopulations, which were employed for global and local search respectively. Hierarchical ways of parallel variables were dynamically adapted according to the different search phases. The proposed algorithm was tested on six well-known constrained optimization problems, and the experiment results show the feasibility and effectiveness of the method.
Keywords:constrained optimization   evolutionary algorithm   dynamic hierarchical   parallel
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