一种基于新的模型的多目标存档遗传算法  

A Multi-objective Archive Genetic Algorithm Based on New Model

作　　者：刘淳安[1] 王宇平[1] 

机构地区：[1]西安电子科技大学理学院

出　　处：《计算机工程与应用》2005年第4期43-45,共3页Computer Engineering and Applications

基　　金：国家自然科学基金资助项目(编号:60374063);陕西省自然科学研究计划项目(编号:2001SL06)

摘　　要：在多目标优化中,如何在最优解集中获得一组分布均匀且质量较好的代表解是十分重要的。文中给出了种群个体的序和解的均匀性分布定义,在此基础上又给出了解的序值方差和U-度量方差,然后把对任意多个目标函数的优化问题转化成对两个目标函数的优化问题,并对转化后的优化问题提出了一种新的多目标存档遗传算法,并证明了其全局收敛性。数据实验比较表明该算法能找到问题的数量更多、分布更广、更均匀的Pareto最优解。In multi-objective optimization,it is very important for the decision maker to find a sufficient number of uniformly distributed and representative Pareto optimal solutions.The rank and uniform distribution of the population are firstly defined and then the rank variance and U-measure variance are introduced.As a result,the multi-objective optimization problem is finally converted into a two objective optimization problem.For the transformed problem,a novel multi-objective archive genetic algorithm is proposed and its global convergence is proved.The numerical experiment shows that this algorithm can find more and wider Pareto-optimal solutions than the original one.

关 键 词：新模型 存档遗传算法 全局收敛性 

分 类 号：O224[理学—运筹学与控制论] TP18[理学—数学]