改进的模糊优选多目标优化遗传算法及其应用  

An improved genetic algorithm based on fuzzy evaluation for multiobjective optimization and its application

作　　者：张翔[1] 陈建能[1] 耿玉磊[2] 

机构地区：[1]福建农林大学机电工程学院,福建福州350002 [2]浙江大学机械与能源学院,浙江杭州310013

出　　处：《福建农林大学学报（自然科学版）》2007年第5期550-552,共3页Journal of Fujian Agriculture and Forestry University:Natural Science Edition

基　　金：福建省自然科学基金资助项目(Z0511031)

摘　　要：提出一种改进的模糊优选多目标优化遗传算法.算法采用个体在总群体中的相对优属度作为适应度值,将总群体中的全部个体按子目标函数的数量平均划分为子群体,对每个子群体分配1个子目标函数,以子目标函数值计算子群体中个体的适应度值.2次选择后满足了整体最优的要求,又尽可能地逼近各子目标最优值.经实例计算,效果显著.An improved genetic algorithm based on fuzzy evaluation for muhiobjective optimization was proposed. The traditional fitness function has been replaced by fuzzy evaluation function, and the population was subdivided averagely according to the number of sub-object functions, then traditional selection operation was done in subdivision population. After two time selection, the algorithm could obtain optimal multiobject value and sub-object value. Some evaluative examples showed that the results were better.

关 键 词：多目标优化 遗传算法 模糊优选 

分 类 号：TH122[机械工程—机械设计及理论]