›› 2016, Vol. 29 ›› Issue (3): 48-.

• 论文 • 上一篇    下一篇



  1. (上海理工大学 光电信息与计算机工程学院,上海 200093)
  • 出版日期:2016-03-15 发布日期:2016-03-18
  • 作者简介:方欣欣(1989—),男,硕士研究生。研究方向:人工智能。龚如宾(1963—),男,教授。研究方向:多媒体视觉等。

Multi-objective Particle Swarm Optimization Algorithm Based on Cosine Distance

FANG Xinxin,GONG Rubin,LI Dawei   

  1. (School of Optica1-Electrical & Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
  • Online:2016-03-15 Published:2016-03-18


针对粒子群优化算法具有的个体分布不均匀以及重复个体较多等缺陷,提出了一种基于余弦距离的多目标粒子群优化算法,该算法根据外部精英存储策略,利用余弦距离排挤机制来选取最分散的粒子,扩大 Pareto最优解集的收敛性和多样性,增强算法的全局寻优能力。通过采用标准多目标优化问题ZDTl~ZDT3进行仿真实验与粒子群算法、混沌粒子群算法、基于拥挤距离的多目标优化算法对比表明,该算法在Pareto前沿的收敛性和多样性方面均优于基于拥挤距离排挤机制,并具有较高的效率

关键词: 余弦距离, 拥挤距离, 多目标优化, 粒子群, 非支配解


A multi-objective particle swarm optimization (PSO) algorithm based on cosine distance is proposed to tackle the drawbacks such as uneven individual distribution redundant overlapping individuals existing in standard particle swarm optimization.Based upon external elite storage strategy,this algorithm utilizes cosine distance crowing mechanism to select the most widely distributed particles.It amplifies the convergence and diversity of best solution set and strengthens the capacity of global optimization.Standard multi-objective optimization ZDTl~ZDT3 are adopted in simulation experiments to compare the proposed algorithm with the particle swarm optimization,chaos particle swarm optimization and multi-objective optimization algorithm based on crowing mechanism.Results show that the proposed algorithm not only outperforms other algorithms in terms of Pareto's frontier convergence and diversity but also obtains preferable efficiency.

Key words: cosine distance;crowding distance;multi objective optimization;particle swarm;non dominated solutions


  • TP301.6