基于爆炸冲击波模型的粒子群优化算法

doi:10.11772/j.issn.1001-9081.2014.07.2085

计算机应用 ›› 2014, Vol. 34 ›› Issue (7): 2085-2089.DOI: 10.11772/j.issn.1001-9081.2014.07.2085

基于爆炸冲击波模型的粒子群优化算法

闫涛,古乐野,阮波

中国科学院成都计算机应用研究所，成都 610041

收稿日期:2013-12-30 修回日期:2014-01-29 出版日期:2014-07-01 发布日期:2014-08-01
通讯作者: 闫涛
作者简介:闫涛(1987-)，男，山西忻州人，硕士研究生，主要研究方向：图像处理；古乐野(1960-)，男，重庆人，研究员,博士生导师，主要研究方向：嵌入式系统、图像处理；阮波(1971-)，男，重庆人，副教授, 主要研究方向：图像处理。

New particle swarm optimization based on blast wave model

YAN Tao,GU Leye,Ruanbo

Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu Sichuan 610041, China

Received:2013-12-30 Revised:2014-01-29 Online:2014-07-01 Published:2014-08-01
Contact: YAN Tao

摘要/Abstract

摘要：

针对基本粒子群优化(PSO)算法在解决复杂多峰问题时易于陷入局部最优解的问题，提出一种基于爆炸冲击波模型的PSO算法(简称BW-PSO算法)。该算法通过加入种群多样性监督条件，使得当种群数量缩小至给定阈值时，触发粒子冲击波过程：最优粒子与次优粒子进行交叉变异，处于爆炸半径内的粒子受到牵引力，加速收敛至当前极值；处于爆炸半径外的粒子受到冲击力向外扩散，增加了找到全局最优值的可能性。BW-PSO算法不仅能够通过最优粒子变异操作提升当前解的精度，而且通过粒子冲击波过程，增加了种群多样性，提升了粒子对全局空间开发的能力。实验结果表明，基于爆炸冲击波模型的PSO算法在求解多峰问题表现优于变异PSO算法与带电PSO算法。

Abstract:

A new Particle Swarm Optimization (PSO) algorithm based on the blast wave model (referred to as BW-PSO algorithm) was proposed aiming at the problem that the basic PSO algorithm when solving complex multimodal problems is easy to fall into local optimal solution. The supervision conditions of population diversity were added to the basic PSO algorithm so that the process of particle shock was triggered when the population decreased to a given threshold value. Crossover and mutation occurred between optimal and suboptimal particles so that the particles within the blast radius by the traction were subjected to accelerate convergence to the current extreme and the particles outside the blast radius were subjected to spread out, which increased the possibility of finding the global optimum value. BW-PSO algorithm not only improved the accuracy of the current solution by the mutation between optimal and suboptimal particles, but also increased the population diversity with the shock wave process of the particles and enhances the ability of the global space development of the particles. Compared with the mutative PSO and charged PSO, the results indicate that the BW-PSO algorithm has a better performance to solve multi-modal optimization problem.

中图分类号:

TP18

闫涛古乐野阮波. 基于爆炸冲击波模型的粒子群优化算法[J]. 计算机应用, 2014, 34(7): 2085-2089.

YAN Tao GU Leye Ruanbo. New particle swarm optimization based on blast wave model[J]. Journal of Computer Applications, 2014, 34(7): 2085-2089.

参考文献

［1］KENNEDY J, EBERHART C. Particle swarm optimization［C］// Proceedings of the 1995 IEEE International Conference on Neural Networks. Piscataway: IEEE, 1995: 1942-1948.
［2］LIU Y, NIU B. New particle swarm optimization theory and practice［M］.Beijing: Science Press,2013:1-3.(刘衍民，牛奔.新型粒子群算法理论与实践［M］.北京：科学出版社，2013:1-3.)
［3］van den BERGH F. An analysis of particle swarm optimizers［D］. Pretoria: University of Pretoria, 2002.
［4］QIN J, YIN Y, BAN X. A hybrid of particle swarm optimization and local search for multimodal functions［C］// Advances in Swarm Intelligence-First International Conference. Berlin: Springer, 2010: 589-596.
［5］JI C, LIU F, ZHANG X. Particle swarm optimization based on catfish effect for flood optimal operation of reservoir［C］// Proceedings of the 7th International Conference on Natural Computation. Piscataway: IEEE, 2011: 1197-1201.
［6］ROBINSON J, SINTON S, RAHMAT-SAMII Y. Particle swarms,genetic algorithm, and their hybrids; optimization of a profiled corrugated horn antenna［C］// Proceedings of the 2002 IEEE International Symposium on Antennas and Propagation Society. Piscataway: IEEE,2002: 314-317.
［7］LOVBERG M, RASMUSSEN T K, KRINK T. Hybrid particle swarm optimizer with breeding and subpopulations［C］// Proceedings of the Genetic and Evolutionary Computation Conference. New York:ACM,2001:469-476.
［8］BLACKWELL T M, BENTLEY P J. Dynamic search with charged swarms［C］// Proceedings of the 2002 Genetic and Evolutionary Computation Conference.New York: Elsevier Science and Technology Books, 2002: 19-26.
［9］QIAO D. Explosion and shock waves in the air(I): the basic theory［J］.Explosion and Shock Waves,1985,10(5):79-80.(乔登江.空中爆炸冲击波(I):基本理论［J］.爆炸与冲击,1985,10(5):79-80.)
［10］VESTEROM J S, RIGET J, KRINK T. Division of labor in particle swarm optimization［C］// Proceedings of the 2002 IEEE Congress on Evolutionary Computation. Piscataway: IEEE,2002: 1570-1575.
［11］RIGET J, VESTERSTROM J S. A diversity-guided particle swarm optimizer-the ARPSO technical report［D］. Aarhus: University of Aarhus,2002.
［12］CLERC M,KENNEDY J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space［J］.IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.

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基于爆炸冲击波模型的粒子群优化算法

New particle swarm optimization based on blast wave model

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