The Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation
Keywords:Particle Swarm Optimization, inertia weight, population diversity, expected velocity, chaos perturbation
Aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. This paper uses the good uniformity of Anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population’s fitness to avoid the untimely aggregation of particle swarm. The numerical simulations of five test functions are performed and the results are compared with several swarm intelligence heuristic algorithms. The results shows that the modified algorithm can keep the population diversity well in the middle stage of the iterative process and it can improve the mean best of the algorithm and the success rate of search.
Kennedy J, Eberhart R,(1995); Particle swarm optimization. IEEE Int. Conf. on Neural Networks. Piscataway, NJ. IEEE Service Center, 1942-1948. http://dx.doi.org/10.1109/icnn.1995.488968
Shi Y, Eberhart R C,(1998); A modified particle swarm optimizer. IEEE Int. Conf. on Evolutionary Computation. Piscataway, NJ. IEEE Service Center, 69-73. http://dx.doi.org/10.1109/icec.1998.699146
Naveed Iqbal, Azzedine Zerguine,Naofal Al-Dhahir,(2014); Decision Feedback Equalization using Particle Swarm Optimization. Signal Processing, 108:1-12. http://dx.doi.org/10.1016/j.sigpro.2014.07.030
Manish Mandloi, Vimal Bhatia,(2016); A low-complexity hybrid algorithm based on particle swarm and ant colony optimization for large-MIMO detection. Signal Processing, 50:66-74. http://dx.doi.org/10.1016/j.eswa.2015.12.008
Md Ashiqur Rahmana,Sohel Anwara,Afshin Izadian,(2016); Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method.Journal of Power Sources.307, 86-97. http://dx.doi.org/10.1016/j.jpowsour.2015.12.083
Razieh Sheikhpour,Mehdi Agha Sarrama,Robab Sheikhpour,(2016); Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer.Applied Soft Computing, 40113-131. http://dx.doi.org/10.1016/j.asoc.2015.10.005
Yu-Shan Cheng,Man-Tsai Chuang,Yi-Hua Liu,Shun-Chung Wang,Zong-Zhen Yang,(2016); A particle swarm optimization based power dispatch algorithm with roulette wheel re-distribution mechanism for equality constraint.Renewable Energ, 88: 58-72. http://dx.doi.org/10.1016/j.renene.2015.11.023
Migdat Hodzic, Li-Chou Tai (2016); Grey Predictor reference model for assisting particle swarm optimization for wind turbine contro.Renewable Energ, 86: 251-256 http://dx.doi.org/10.1016/j.renene.2015.08.001
Tao Lin, Peng Wu, Fengmei Gao, Yi Yu, Linhong Wang (2016); Study on SVM temperature compensation of liquid ammonia volumetric flowmeter based on variable weight PSO, International Journal of Heat and Technology , 33(2):151-156. http://dx.doi.org/10.18280/ijht.330224
Shi Y. and Eberhart, R.C. (2001); Fuzzy adaptive particle swarm optimization, IEEE Int. Conf. on Evolutionary Computation.Seoul, Korea, 101-106.
Zhang L., Yu H., Hu S. (2003); A new approach to improve particle swarm optimization, Genetic and Evolutionary Computation Conference 2003. Chicago, IL, USA, 134-142.
Jiang C. W., Etorre B. (2005); A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization, Mathematics and Computers in Simulation, 68:57-65. http://dx.doi.org/10.1016/j.matcom.2004.10.003
Chen G.M., Huang X.B.,Jia J Y, Min Z.F.(2006); Natural exponential inertia weight strategy in particle swarm optimization.World Congress on Intelligent Control & Automation, 1: 3672-3675. http://dx.doi.org/10.1109/WCICA.2006.1713055
Jiao B., Lian Z.G.,Gu X.S.(2008); A dynamic inertia weight particle swarm optimization algorithm.Chaos Solitons & Fractals, 37(3): 698-705. http://dx.doi.org/10.1016/j.chaos.2006.09.063
Zhang Dingxue, Liao Ruiquan,(2009); Adaptive particle swarm optimization algorithm based on population velocity.Control and Decision, 24(8): 1257-1265.
Lv Zhensu, Hou Zhirong (2004); Particle Swarm Optimization with Adaptive Mutation.Acta Electronica sinica, 32(3):416-420.
Zeng Jianchao, Cui Zhihua (2012); Nature Inspired Computation, Beijing, National Defense Industry Press, 252-256.
Kazem A., Sharifi E., Hussain F.K., Saberi M., Hussain O.K. (2013); Support vector regression with chaos-based firefly algorithm for stock market price forecasting, Applied Soft Computing, 13: 947-958. http://dx.doi.org/10.1016/j.asoc.2012.09.024
LIU Huaying, LIN Yue (2006); A Hybrid Particle Swarm Optimization Based on Chaos Strategy to Handle Local Convergence, Computer Engineering and Applications, 42(13):77- 79.
Yang X.S.(2011); Chaos-enhanced firefly algorithm with automatic parameter tuning, International Journal of Swarm Intelligence Research, 2: 1-11. http://dx.doi.org/10.4018/jsir.2011100101
Lu Y., Liu X.(2011); A new population migration algorithm based on the chaos theory, IEEE 2nd International Symposium on Intelligence Information Processing and Trusted Computing, 147-150. http://dx.doi.org/10.1109/IPTC.2011.44
LV Jinhu, LU Junan, Chen Shihua,(2002); Chaotic time series analysis and its application, Wuhan: Wuhan University press, 87-89.
R. Anderson (1996); Industrial Cryptography, IEEE REV., 118-120.
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