作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程 ›› 2010, Vol. 36 ›› Issue (14): 147-149. doi: 10.3969/j.issn.1000-3428.2010.14.053

• 人工智能及识别技术 • 上一篇    下一篇

一种求解约束优化问题的遗传算法

梁昔明,秦浩宇,龙 文   

  1. (中南大学信息科学与工程学院,长沙 410083)
  • 出版日期:2010-07-20 发布日期:2010-07-20
  • 作者简介:梁昔明(1967-),男,教授、博士、博士生导师,主研方向:过程控制及系统优化,进化计算;秦浩宇,硕士研究生;龙 文,博士研究生
  • 基金资助:
    国家自然科学基金资助项目“过程控制系统的一类设定点优化方法研究”(60874070);高校博士点基金资助项目“生产过程操作优化全局寻优方法研究”(20070533131)

Genetic Algorithm for Solving Constrained Optimization Problem

LIANG Xi-ming, QIN Hao-yu, LONG Wen   

  1. (School of Information Science and Engineering, Central South University, Changsha 410083)
  • Online:2010-07-20 Published:2010-07-20

摘要: 提出一种求解约束优化问题的遗传算法。通过可行解与不可行解算术交叉的方法对问题的决策空间进行搜索,对可行种群和不可行种群分别按照适应度和约束违反度进行选择。传统变异操作使得解往往偏离了约束区域,因此引入对可行解的边界变异和对不可行解的非均匀变异,并通过维变异方法保持种群的多样性。数值实验结果说明该算法的有效性。

关键词: 约束优化问题, 可行解, 不可行解, 遗传算法

Abstract: A genetic algorithm to handle constrained optimization problem is proposed. This method searches the decision space of a problem through the arithmetic crossover of feasible and infeasible solutions, and performs a selection on feasible and infeasible populations respectively according to fitness and constraint violation. It uses the boundary mutation on feasible solutions and the non-uniform mutation on infeasible solutions because the solutions usually deviate from the constraint domain after the traditional mutation operation. It maintains the population diversity through dimension mutation. Numerical results show that it is an effective algorithm.

Key words: constrained optimization problem, feasible solution, infeasible solution, genetic algorithm

中图分类号: