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
出处
《中南民族大学学报（自然科学版）》
CAS
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)
关键词
演化算法
多目标优化
粒子群优化
evolutionary algorithm
multi-objective optimization
particle swarm optimization
