Abstract
A novel method to improve the global performance of particle swarm optimization (PSO) is proposed, which extends the exploring domain of the optimal position in the current generation and the optimal position thus achieved by every particle. In each generation, the best two positions are modified according to their searching radii and directions. If the new solutions improve the old ones, the optimal positions in the updating equations of the conventional PSO algorithm will be replaced by the new solutions. Using this operator, the swarm diverges from local optimization easily. Moreover, the algorithm is easily implemented, and because the basic structure of PSO is not altered, the algorithm can be easily combined with different PSO methods to improve the performance. Some benchmark functions and chaotic systems are evaluated via simulations, showing that the proposed algorithm exceeds existing methods such as BPSO, LDWPSO, DNLPSO to some extent.
Similar content being viewed by others
References
Akat SB, Gazi V (2008) Particle swarm optimization with dynamic neighborhood topology: three neighborhood strategies and preliminary results. In: IEEE swarm intelligence symposium, St. Louis, 21–23 September 2008
Barnard CJ, Sibly RM (1981) Producers and scroungers: a general model and its application to captive flocks of house sparrows. Anim Behav 29:543–550
Bergh FVD, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci 176(8):937–971
Bilal A, Erhan A, Ozer AB (2009) Chaos embedded particle swarm optimization algorithms. Chaos Solitons Fractals 40:1715–1734
Box GEP, Jenkins GM (1970) Time series analysis, forecasting and control. Holden Day, San Francisco
Cai X, Zhang G, Venayagamoorthy G, II DW (2007) Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm. Neurocomputing 70:2342–2353
Chen DB, Zhao CX (2009) Particle swarm optimization with adaptive population size and its application. Appl Soft Comput 9:39–48
Chen YH, Yang B, Dong JW, Abraham A (2005) Time-series forecasting using flexible neural tree model. Inf Sci 174:219–235
Chen WN, Zhang J et al (2013) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241–258
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73
Couzin ID, Krause J, Franks NR, Levin SA (2005) Effective leadership and decision-making in animal groups on the move. Nature 434:513–516
Deep K, Thakur M (2007) A new crossover operator for real coded genetic algorithms. Appl Math Comput 188:895–911
Guo-Shao SU (2008) A new intelligent model for nonlinear time series prediction, In: 2008 international conference on computer science and software engineering, pp 435–438
Gromov VA, Shulga AN (2012) Chaotic time series prediction with employment of ant colony optimization. Expert Syst Appl 39:8474–8478
Han M, Fan J, Wang J (2011) A dynamic feedforward neural network based on Gaussian particle swarm optimization and its application for predictive control. IEEE Trans Evol Comput 22(9):1457–1468
He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13:973–990
Higashi N, Iba H (2003) Particle swarm optimization with Gaussian mutation. In: Proceedings of the swarm intelligence symposium, SIS, pp 72–79
Janson S, Middendorf M (2005) A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans Syst Man Cybern B 35:1272–1282
Kaewkamnerdpong B, Peter JB (2005) Perceptive particle swarm optimisation: an investigation, swarm intelligence symosium, vol 8–10, pp 169–176
Kennedy J, Eberhart R (1995) Particle swarm optimisation. In: Proceedings of the IEEE international conference on neural network, pp 1942–1948
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the congress on evolutionary computation, pp 1671–1676
Luitel B, Venayagamoorthy GK (2010) Particle swarm optimization with quantum infusion for system identification. Eng Appl Artif Intell 23(5):635–649
Mackey M, Glass L (1977) Oscillation and chaos in a physiological control system. Science 197–287
Millie P, Radha T, Ajith A (2008) Particle swarm optimization using adaptive mutation. In: Proceedings of the 19th international conference on database and expert systems application, pp 519–523
Nasir Md, Das S, Maity D et al (2012) A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Inf Sci 209:16–36
Parsopoulos KE, Vrahatis MN (2004) UPSO: a unified particle swarm optimization scheme. In: Proceedings of the international conference of computational methods in sciences and engineering, ICCMSE 2004. Lecture series on computer and computational sciences, vol 1. VSP International Science Publishers, Zeist, pp 868–873
Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the IEEE swarm lnlelligence symposium, SIS 2003, Indianapolis, pp 174–181
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE conference on evolutionary computation, pp 69–73
Zheng YL, Ma LH, Zhang LY, Qian JX (2003) On the convergence analysis and parameter selection in particle swarm optimization. In: Proceedings of the 2003 IEEE international conference on machine learning and cybernetics, pp 1802–1807
Zheng YL, Ma LH, Zhang LY et al (2003) Empirical study of particle swarm optimizer with an increasing inertia weight. In: Proceedings of the IEEE congress on evolutionary computation, Piscataway, pp 221–226
Zhao L, Yang YP (2009) PSO-based single multiplicative neuron model for time series prediction. Expert Syst Appl 36:2805–2812
Acknowledgments
This research was partially supported by National Natural Science Foundation of China (No. 61304082) and Natural Science Foundation of Anhui Province, China (Grants No. 1308085MF82).
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Chen, D., Chen, J., Jiang, H. et al. An improved PSO algorithm based on particle exploration for function optimization and the modeling of chaotic systems. Soft Comput 19, 3071–3081 (2015). https://doi.org/10.1007/s00500-014-1469-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-014-1469-4