摘要
提出一种改进的多目标粒子群优化算法,该算法采用精英归档策略,由档案库中的非劣解提供粒子速度更新时的全局最优位置,根据Pareto支配关系来更新粒子的个体最优位置。使用非劣解目标的线密度度量非劣解前端的均匀性,通过删除小密度的非劣解提高非劣解前端的均匀性。针对多目标进化算法理论型指标的不足,设计了应用型评价指标。标准函数的仿真实验结果表明,所提算法能够获得大量的非劣解,快速地收敛于Pareto最优解前端,且分布比较均匀。
An Improved Multi-Objective Particle Swarm Optimization（IMOPSO） algorithm is proposed,in which elitism archived strategy is used,global best position is provided by non-dominated solutions in the archive and individual best position is updated based on Pareto dominance.The algorithm uses objective solutions linear density to measure non-dominated solutions quality and employs the strategy of deleting low density non-dominated solutions to enhance non-dominated solutions uniformity.To overcome the shortcoming of theoretical index in multi-objective evolution algorithm,a practical index is developed.Simulation results on benchmark functions show the proposed method can obtain a lot of non-dominated solutions,rapidly converge to the Pareto front and uniformly spread along the front.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第33期38-41,共4页
Computer Engineering and Applications
基金
江苏省自然科学基金No.06KJB510040~~
关键词
粒子群
多目标进化算法
PARETO最优
精英策略
归档技术
particle swarm
multi-objective evolutionary algorithm
Pareto optimal
elitism strategy
archive technique
