一种求解约束优化问题的遗传算法

A genetic algorithm for solving constrained optimization problems

约束优化问题是工程领域中常见的数学模型,求解难度主要来自变量的约束和目标函数的复杂性.本文针对约束优化问题,提出了一种基于双向信息搜索的遗传算法.首先,通过启发式信息在种群中产生至少一个可行个体;其次,对于每一个杂交父代个体,基于概率分布选择一个可行解和一个目标函数值好的个体,杂交后代由这三个点的矢量和产生.最后,仿真实验及比较结果表明,提出的遗传算法是可行有效的.

Constrained optimization problems are common mathematical models in the engineering field,and the difficulty of solving mainly comes from the constraints of variables and the complexity of the objective function.This paper proposes a genetic algorithm based on bidirectional information search for constrained optimization problems.Firstly,heuristic information is used to generate at least one feasible individual in the population.Secondly,for each hybrid parent individual,based on the probability distribution,select a feasible solution and an individual with a good objective function value.The hybrid offspring is generated by the vector sum of these three points.Finally,simulation experiments and comparison results show that the proposed genetic algorithm is feasible and effective.