遗传算法在多目标优化应用中的对比研究

多目标优化应用研究在过程工程领域越来越受重视。本文首先给出了多目标优化问题的一般形式，指出多目标问题求解任务：引导搜索向整个的Pareto优化范围；Pareto优化前沿上保持解集的多样性。在简要论述遗传算法求解多目标技术的基础上，对应用了遗传算法求解多目标的两种方法进行了对比研究，并给出了线性加权遗传算法和一种多目标遗传算法的计算框图。指出线性加权法求解Pareto最优解时不能不能很好地处理非凸区域、均匀分布的权重值不能生成均匀分布的Pareto前沿等局限性，以及多目标遗传算法生成种群多样性及Pareto最优解均匀分布的优点，并用实例进行了验证说明。

Genetic algorithm applied to Multi-objective optimization:A comparative study

The problem statement of multi-objective optimization problem ( MOP) is firstly presented.The goals of problem solving are to guide the search process towards the global Pareto-optimal region and maintain population diversity in the Pareto-optimal front.After reviewing MOP techniques using genetic algorithm (GA) ,two methods are presented and compared.The frameworks of these methods are introduced.And lastly,two practical examples are carried out.The results show that: (1) the drawbacks of weighted sum approach using GA for Pareto set generation is observed,and (2) Multi-objective genetic algorithm have the advantages of the diversity of population and the uniform spread of Pareto-optimal solutions.