计算机应用 ›› 2018, Vol. 38 ›› Issue (5): 1239-1244.DOI: 10.11772/j.issn.1001-9081.2017102557

• 人工智能 • 上一篇    下一篇

基于局部远亲差分增强的扰动粒子群优化算法

王永贵, 胡彩云, 李鑫   

  1. 辽宁工程技术大学 软件学院, 辽宁 葫芦岛 125105
  • 收稿日期:2017-10-30 修回日期:2017-12-18 出版日期:2018-05-10 发布日期:2018-05-24
  • 通讯作者: 胡彩云
  • 作者简介:王永贵(1975-),男,内蒙古宁城人,教授,硕士,CCF会员,主要研究方向:大数据、数据库及数据仓库;胡彩云(1990-),女,安徽芜湖人,硕士研究生,主要研究方向:大数据、智能算法、机器学习;李鑫(1996-),女,辽宁朝阳人,主要研究方向:大数据、数据库及数据仓库。
  • 基金资助:
    国家自然科学基金资助项目(61404069);辽宁省科技厅博士启动基金资助项目(20141140);辽宁省教育厅基金资助项目(L2014128)。

Perturbation particle swarm optimization algorithm based on local far-neighbor differential enhancement

WANG Yonggui, HU Caiyun, LI Xin   

  1. School of Software, Liaoning Technical University, Huludao Liaoning 125105, China
  • Received:2017-10-30 Revised:2017-12-18 Online:2018-05-10 Published:2018-05-24
  • Contact: 胡彩云
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61404069), the PhD Start-up Fundation of Liaoning Province Science and Technology Department (20141140), the Foundation of Liaoning Provincial Education Department (L2014128).

摘要: 针对粒子群优化(PSO)算法在搜索过程中因个体间缺乏交互,使种群逐渐丧失多样性、导致算法陷入局部极值的问题,提出了一种基于局部远亲差分增强的扰动粒子群优化算法(LFDE-PPSO)。首先,为扩大种群搜索空间,在速度更新过程中引入扰动因子,使惯性权重、学习因子在小范围内波动;其次,引入重构概率,选择适应度值低的个体重建中间种群;最后,为增加种群多样性,使较差个体的优秀基因得以保留,引入粒子不相关性及远亲个体,利用不相关性选择与差分个体基因差异性较大的远亲进行差分增强。实验结果表明,所提算法能够使中间种群中适应度值高的个体得以保留,有效增加种群多样性,使种群具备较强的跳脱局部极值能力,加快粒子逼近全局最优,同时具有收敛快、精度高等优点。

关键词: 粒子群优化算法, 扰动, 不相关性, 局部远亲, 差分增强, 种群多样性

Abstract: To solve the problems that Particle Swarm Optimization (PSO) algorithm is easy to fall into the local extremum due to the lack of interaction between individuals in the search process, the diversity of the population is gradually lost, a Perturbation Particle Swarm Optimization algorithm based on Local Far-neighbor Differential Enhancement (LFDE-PPSO) was proposed. Firstly, in order to enlarge the population search space, the disturbance factor was introduced to make inertia weight and learning factor fluctuate within a small range. Secondly, the reconstruction probability was introduced, and the population with low fitness value was selected to reconstruct intermediate population. Finally, in order to increase the population diversity, the excellent individuals of poor individuals were retained, the irrelevant and far-neighbor individuals were introduced. The far-neighbors with large differences from differential individual genes were used for differential enhancement. The experimental results show that the proposed algorithm can preserve individuals with high fitness in the intermediate population, effectively increase the population diversity, make the population have strong ability to jump out of local extremum, speed up the particle approximation to the global aptimum, and have the advantages of fast convergence and high precision.

Key words: Particle Swarm Optimization (PSO) algorithm, disturbance, irrelevance, local far-neighbor, differential enhancement, population diversity

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