Abstract: 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.

Key words: particle swarm, multi-objective evolutionary algorithm, Pareto optimal, elitism strategy, archive technique

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。

关键词: 粒子群, 多目标进化算法, Pareto最优, 精英策略, 归档技术