Improved Particle Swarm Optimization Algorithm Based on Opposite Learning of Refraction
SHAO Peng1,2, WU Zhi-jian1,2, 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
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.
[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)
2015, Vol. 43 
