基于折射原理反向学习模型的改进粒子群算法

doi:10.3969/j.issn.0372-2112.2015.11.001

电子学报 ›› 2015, Vol. 43 ›› Issue (11): 2137-2144.DOI: 10.3969/j.issn.0372-2112.2015.11.001

• 学术论文 • 下一篇

基于折射原理反向学习模型的改进粒子群算法

邵鹏^1,2, 吴志健^1,2, 周炫余², 邓长寿³

1. 武汉大学软件工程国家重点实验室, 湖北武汉 430072;
2. 武汉大学计算机学院, 湖北武汉 430072;
3. 九江学院信息科学与技术学院, 江西九江 332005

收稿日期:2014-06-19 修回日期:2014-09-28 出版日期:2015-11-25
- 通讯作者:
- 吴志健
- 作者简介:
- 邵鹏男,1983年生,武汉大学计算机学院博士研究生,研究方向:智能计算、智能信息处理.E-mail:sp198310@163.com;周炫余 男,1987年生,武汉大学计算机学院博士研究生,研究方向:自然语言处理、智能信息处理;邓长寿 男,1972年生,博士,教授,九江学院信息科学与技术学院副院长,研究方向:智能计算和数据挖掘.
- 基金资助:
- 国家自然科学基金 (No.61070008,No.70971043); 武汉大学软件工程国家重点实验室开放基金项目 (No.SKLSE2012-09-19); 中央高校基本科研业务专项项目 (No.2012211020205); 江西省教育厅科学技术项目 (No.GJJ13729)

Improved Particle Swarm Optimization Algorithm Based on Opposite Learning of Refraction

SHAO Peng^1,2, WU Zhi-jian^1,2, ZHOU Xuan-yu², DENG Chang-shou³

1. State Key Lab of Software Engineering, Wuhan University, Wuhan, Hubei 430072, China;
2. Computer School, Wuhan University, Wuhan, Hubei 430072, China;
3. School of Information Science and Technology, Jiujiang University, Jiujiang, Jiangxi 332005, China

Received:2014-06-19 Revised:2014-09-28 Online:2015-11-25 Published:2015-11-25
- Supported by:
- National Natural Science Foundation of China (No.61070008, No.70971043); Open Fund of State Key Laboratory of Software Engineering, Wuhan University, China (No.SKLSE2012-09-19); Fundamental Research Funds for the Central Universities (No.2012211020205); Science and Technique Research Program of Education Department of Jiangxi Province (No.GJJ13729)

摘要/Abstract

摘要：

对于粒子群优化算法易陷入局部最优的缺陷,反向学习策略对其的改进取得了较好的效果.然而,反向学习策略需要结合其它策略来提高算法后期的全局搜索能力,针对此缺陷,根据光的折射原理对反向学习策略的反向过程进行改进,提出反向学习的统一算法模型及基于折射原理反向学习模型的改进粒子群算法.实验与分析表明,与其它基于反向学习的粒子群算法相比,该模型更有效地改进了所提算法的全局搜索能力,提高了种群的多样性,从而提高了算法的收敛速度以及优化精度.

关键词: 智能优化算法, 粒子群优化算法, 反向学习, 折射原理

Abstract:

One of shortcomings found in the particle swarm optimization algorithm is that it is easy to fall into local optimum,and the opposite learning strategy has a good effect on the improvement of this shortcoming.However,to improve the global search ability by using the opposite learning strategy it is necessary that in the late algorithm other strategies are combined to opposite learning strategy.To overcome this shortcoming,this paper improves the opposite process of the opposite learning strategy according to the refraction principle of light,and proposes the unified model of opposite-based learning(UOBL) and the improved particle swarm optimization algorithm based on the opposite learning model of the principle of refraction(refrPSO).Experiment results and analysis show that the model improves the global search ability of the refrPSO algorithm more effectively compared with other particle swarm algorithm based on opposite learning and the diversity of the population.Because of these improvements,the refrPSO enhances the convergence speed and the accuracy of optimization.

Key words: intelligent optimization, particle swarm optimization, opposite-based learning, refraction principle

中图分类号:

TP18

邵鹏, 吴志健, 周炫余, 等. 基于折射原理反向学习模型的改进粒子群算法[J]. 电子学报, 2015, 43(11): 2137-2144.

SHAO Peng, WU Zhi-jian, ZHOU Xuan-yu, et al. Improved Particle Swarm Optimization Algorithm Based on Opposite Learning of Refraction[J]. Acta Electronica Sinica, 2015, 43(11): 2137-2144.

参考文献

[1] J.Kennedy,R C Eberhart.Particle swarm optimization[A]. Proceedings of the IEEE International Joint Conference on Neural Networks[C]. IEEE,1995.1942-1948.
[2] 胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J]. 软件学报,2007,18(4):861-868. Hu Wang,Li Zhi-shu.A simpler and more effective particle swarm optimization algorithm[J]. Journal of Software,2007,18(4):861-868.(in Chinese)
[3] Gandomi A H,Yun G J,Yang X S,et al.Chaos-enhanced accelerated particle swarm optimization[J]. Communications in Nonlinear Science and Numerical Simulation,2013,18(2):327-340.
[4] Tizhoosh H.Opposition-based learning:A new scheme for machine intelligence[A]. Proceedings of International Conference on Intelligent Agent,Web Technologies and Internet Commerce[C]. Vienna:IEEE,2005.695-701.
[5] S Rahnamayan,H R Tizhoosh,M M A Salama.Opposition-based differential evolution algorithm[A]. IEEE Congress on Evolutionary Computation[C]. Canada:IEEE,2006.2010-2017.
[6] Wang H,Li H,Liu Y,et al.Opposition-based particle swarm algorithm with cauchy mutation[A]. IEEE Congress on Evolutionary Computation[C]. Singapore:IEEE,2007.4750-4756.
[7] Wang H,Wu Z J,Rahnamayan.Enhancing particle swarm optimization using generalized opposition-based learning[J]. Information Sciences,2011,181(20):4699-4714.
[8] 喻飞,李元香,等.透镜成像反向学习策略在粒子群算法中的应用[J]. 电子学报,2014,42(2):230-235. Yu Fei,Li Yuan-xiang,et al.The application of a novel OBL based on lens imaging principle in PSO[J]. Acta Electronica Sinica,2014,42(2):230-235.(in Chinese)
[9] Griffiths,David J.Introduction to Electrodynamics(3rd Ed)[M]. Prentice Hall,1998.386-389.
[10] 周新宇,吴志健,等.一种精英反向学习的粒子群优化算法[J]. 电子学报,2013,41(8):1647-1652. Zhou Xin-yu,Wu Zhi-jian,et al.Elite opposition-based particle swarm optimization[J]. Acta Electronica Sinica,2013,41(8):1647-1652.(in Chinese)
[11] Rosenbrock H H.An automatic method for finding the greatest or least value of a function[J]. Computer Journal,1960,3(3):175-184.
[12] 梁艳春,周春光,等.基于遗传算法的Rosenbrock函数优化问题的研究[J]. 软件学报,1997,8(9):701-708. Liang Yan-chun,Zhou Chun-guang,et al.Optimization of Rosenbrock's function based on genetic algorithms[J]. Journal of Software,1997,8(9):701-708.(in Chinese)
[13] He S,Wu Q H,Saunders J R.Group search optimizer:an optimization algorithm inspired by animal searching behavior[J]. IEEE Transactions on Evolutionary Computation,2009,13(5):973-990.
[14] Changyong Lee,Xin Yao.Evolutionary programming using mutations based on the levy probability distribution[J]. IEEE Transactions on Evolutionary Computation,2004,2(8):1-13.
[15] 王晖.区域变换搜索的智能算法研究[D]. 武汉:武汉大学,2011.17-19. WangHui.Research on intelligent algorithms based on region transformation search and its applications[D]. Wuhan:Wuhan University,2011.17-19.(in Chinese)

基于折射原理反向学习模型的改进粒子群算法

Improved Particle Swarm Optimization Algorithm Based on Opposite Learning of Refraction

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