一种多项指标全提升的多目标优化演化算法  

A Multi-Objective Optimization Evolutionary Algorithm with Multiple Indicators Enhanced

作　　者：宋中山[1] 陈建国[1] 郑波尽[1] 

机构地区：[1]中南民族大学计算机科学学院,武汉430074

出　　处：《中南民族大学学报（自然科学版）》2011年第3期89-93,共5页Journal of South-Central University for Nationalities：Natural Science Edition

基　　金：国家自然科学基金资助项目(60803095)

摘　　要：针对当前大部分多目标优化演化算法设计复杂、耗时巨大,以及取得的近似Pareto前沿点不够多、分布不均匀、覆盖不完整等问题,提出了一种新的基于粒子群和几何Pareto选择算法的多目标优化PSGPS算法.经过5个测试问题的实验结果表明:该算法使用较低的时间消耗,就能在前沿点个数、前沿点分布均匀性、覆盖完整度等性能指标上都优于当前流行的NSGA2,SPEA2和PESA等多目标优化演化算法.Currently,most of multi-objective optimization evolutionary algorithms are complex and time-consuming.At the same time,the approximate Pareto fronts of these algorithms may not have enough points,with uneven in distribution and incomplete coverage.This paper presents a new multi-objective optimization evolutionary algorithm,which is based on Particle Swarm Optimization algorithm and Geometric Pareto selection algorithm.The experimental results on five widely used test-problems show that the performance indicators,including the numbers of front points,the uniformity,the complete of coverage and so on,are better than the compared popular multi-objective optimization algorithms： NSGA2,SPEA2,PESA etc.with satisfactory time consuming

关 键 词：演化算法 多目标优化 粒子群优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]