多目标优化的遗传算法及其实现

The Research and Implementation of Genetic Algorithm for Multi-Objective Optimization

遗传算法是一种通过模拟自然进化过程搜索最优解的方法,在优化方法中具有独特的优越性,有着非常重要的理论意义和广泛的应用领域.多目标优化问题求解已成为遗传算法的一个重要研究方向,而基于Pareto最优概念的多目标遗传算法则是当前遗传算法的研究热点.本文对遗传算法的理论基础进行分析,包括模式定理等,讨论用遗传算法来解决多目标优化问题的方法并给出其实现,介绍遗传算法的各种改进措施,并指出遗传算法的发展动向.

Genetic Algorithms （GAs） are stochastical search and optimization techniques which mimic the natural process of evolution. GAs have some advantages over the traditional optimization algorithms, and are of the great importance and have a wide range of applications. Multi - Objective Optimization （MOO） has been an important research area of Genetic Algorithms in recent years, and current research work focuses on the Pareto optimal - based MOO evolutionary approaches. The basic algorithm theory and implementation techniques of genetic algorithms are outlined. The implementation of Multi - Objective optimization problem has been given. This paper has also summed up some kinds of relevant improved methods and some new developmental trends concerning genetic algorithms.