基于粒子滤波重采样与变异操作的改进粒子群算法

doi:10.11772/j.issn.1001-9081.2016.04.1008

计算机应用 ›› 2016, Vol. 36 ›› Issue (4): 1008-1014.DOI: 10.11772/j.issn.1001-9081.2016.04.1008

基于粒子滤波重采样与变异操作的改进粒子群算法

韩雪^1,2, 程奇峰^1,2, 赵婷婷^1,2, 张利民³

1. 辽宁工程技术大学理学院, 辽宁阜新 123000;
2. 辽宁工程技术大学智能工程与数学研究院, 辽宁阜新 123000;
3. 辽宁工程技术大学机械工程学院, 辽宁阜新 123000

收稿日期:2015-09-15 修回日期:2015-11-05 出版日期:2016-04-10 发布日期:2016-04-08
通讯作者: 程奇峰
作者简介:韩雪(1990-),女,辽宁本溪人,硕士研究生,主要研究方向:智能优化算法、大数据处理; 程奇峰(1985-),男,江西鄱阳人,副教授,博士,主要研究方向:随机模型预测控制、大数据分析与挖掘; 赵婷婷(1989-),女,辽宁沈阳人,硕士,主要研究方向:系统控制;张利民(1982-),男,河南焦作人,讲师,博士,主要研究方向:大数据处理、随机模型预测控制。
基金资助:
国家自然科学基金资助项目(61304090);辽宁省自然科学基金资助项目(2015020570);辽宁省教育厅科学研究项目(L2013132);辽宁工程技术大学生产技术问题创新研究基金资助项目(2013031T)。

Improved particle swarm optimization based on re-sampling of particle filter and mutation

HAN Xue^1,2, CHENG Qifeng^1,2, ZHAO Tingting^1,2, ZHANG Limin³

1. School of Science, Liaoning Technical University, Fuxin Liaoning 123000, China;
2. Institute of Intelligent Engineering and Mathematics, Liaoning Technical University, Fuxin Liaoning 123000, China;
3. School of Mechanical Engineering, Liaoning Technical University, Fuxin Liaoning 123000, China

Received:2015-09-15 Revised:2015-11-05 Online:2016-04-10 Published:2016-04-08
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61304090), the Project of Natural Science Foundation of Liaoning Province (2015020570), the Science Research Fund from Department of Education, Liaoning Province (L2013132), the Innovation Research Fund of Liaoning Technical University (2013031T).

摘要/Abstract

摘要： 针对标准粒子群优化(PSO)算法在求解过程中存在求解精度低、搜索后期收敛速度慢等问题,提出一种基于粒子滤波重采样步骤与变异操作相结合的改进PSO算法——RSPSO。该算法充分利用重采样中具有较大权值的粒子被保留和复制、较小权值的粒子被舍弃的特点,并利用已有的变异操作方法克服粒子匮乏的缺点,大大增强了PSO算法中后期搜索阶段的局部搜索能力。在不同基准函数下对RSPSO算法和标准PSO算法以及文献中其他改进算法进行对比。实验结果表明, RSPSO算法的收敛速度较快,同时其搜索精度和解的稳定性均有所提高,且能够全局地解决多峰问题。

关键词: 粒子群算法, 粒子滤波, 重采样, 变异, 基准函数

Abstract: Concerning the low accuracy and convergence of standard Particle Swarm Optimization (PSO) algorithm, an improved particle swarm optimization based on particle filter re-sampling and mutation named RSPSO was proposed. By using the resampling characteristic of abandoning particles with low weights and duplicating and retaining particles with high weights, an existing method for mutation was adopted to overcome the disadvantage of particle degeneracy, which greatly enhanced the local search capability in the later searching stage of PSO algorithm. RSPSO algorithm was compared with the standard algorithm and some other improved algorithms in the literature under different benchmark functions. The experimental results show that RSPSO has faster convergence, higher accuracy and better stability, and it is able to solve multi-modal problems globally.

Key words: Particle Swarm Optimization (PSO) algorithm, particle filter, re-sampling, mutation, benchmark function

中图分类号:

TP18

韩雪, 程奇峰, 赵婷婷, 张利民. 基于粒子滤波重采样与变异操作的改进粒子群算法[J]. 计算机应用, 2016, 36(4): 1008-1014.

HAN Xue, CHENG Qifeng, ZHAO Tingting, ZHANG Limin. Improved particle swarm optimization based on re-sampling of particle filter and mutation[J]. Journal of Computer Applications, 2016, 36(4): 1008-1014.

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基于粒子滤波重采样与变异操作的改进粒子群算法

Improved particle swarm optimization based on re-sampling of particle filter and mutation

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