Abstract
Particle swarm optimization (PSO) has shown its good performance in many optimization problems. This paper proposes a Cooperative Velocity Updating algorithm based Particle Swarm Optimization (CVUPSO), which is inspired by the competition and cooperation methods of different populations in natural swarm living, such as bees, ants, birds, fish, etc. In this algorithm, before an elite is introduced from other sub-swarms or a new particle is randomly born, the weak particle will be eliminated out of its sub-swarm. In each iteration process, every sub-swarm abandons a least potential particle. The CVUPSO recorded four special positions: pbest, lbest, gbest and lworst. The pbest represents the current particle’s best position while lbest represents the current sub swarm’s best position, and gbest is the best position among the whole swarm, lworst is the position of the particle with the worst performance. A new update method is adopted in CVUPSO, where the particles are more likely to follow lbest than follow gbest in the early stage of iteration, but opposite in the later stage. In this paper, two variants of CVUPSO are proposed, one variant is CVUPSO with Random inertia weight (for short CVUPSO-R), and another is CVUPSO with Exponential decline inertia weight (for short CVUPSO-E). By making comparative experiments on several widely used benchmark functions, analysis results show that the performance of these two improved variants are more promising than the recently developed PSO algorithms for searching multiple peak values of multiple objects optimization problem.
Similar content being viewed by others
References
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proc of the IEEE international conference on neural networks, pp 1942–1948
Mostaghim S, Teich JR (2003) Strategies for finding local guides in multi-objective particle swarm optimization (MOPSO). In: Proceedings of the IEEE Swarm Intelligence Symposium, pp 26–33
Liang JJ, Qin AK, Suganthan PN, Baskar S (2004) Particle swarm optimization algorithms with novel learning strategies. In: IEEE international conference on systems, man and cybernetics, vol 4, pp 3659–3664
Zou D, Wu L, Gao L, Wang X (2010) A modified particle swarm optimization algorithm for reliability problems. In: 2010 IEEE fifth international conference: bio-inspired computing: theories and applications (BIC-TA), pp 1098–1105
Navalertporn T, Afzulpurkar NV (2011) Optimization of tile manufacturing process using particle swarm optimization. Swarm Evol Comput 1(2):97–109
Poli R, Kennedy J, Blackwell T (2007) Hybrid particle swarm optimizer with breeding and subpopulations. Swarm Intell 1:33–57
Suganthan PN (1999) Particle swarm optimizer with neighbor-hood operator. In: CEC’99, pp 1958–1962
Kaveh A, Talatahari S (2011) Hybrid charged system search and particle swarm optimization for engineering design problems. Eng Comput 28(4):423–440
Mousa AA, El-Shorbagy MA, Abd-El-Wahed WF (2012) Local search based hybrid particle swarm optimization algorithm for multiobjective optimization. Swarm Evol Comput 3:1–14
Liu L, Yang S, Wang D (2012) Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima. Inf Sci 182(1):139–155
Huang H, Qin H, Hao Z, Lim A (2012) Example-based learning particle swarm optimization for continuous optimization. Inf Sci 182(1):125–138
Kiranyaz S, Pulkkinen J, Gabbouj M (2011) Multi-dimensional particle swarm optimization in dynamic environments. Expert Syst Appl 38(3):2212–2223
Chen C-C (2011) Two-layer particle swarm optimization for unconstrained optimization problems. Appl Soft Comput 11(1):295–304
Peng H, Li R, Cao L-l, Li L-x (2011) Multiple swarms multi-objective particle swarm optimization based on decomposition. Proc Eng 15:3371–3375
Zou D, Wu L, Gao L, Wang X (2010) A modified particle swarm optimization algorithm for reliability problems. In: 2010 IEEE fifth international conference on bio-inspired computing: theories and applications (BIC-TA), pp 1098–1105
Parsopoulos KE, Vrahatis MN (2004) UPSO: a unified particle swarm optimization scheme. In: Proc int conf computational methods in sciences and engineering (ICCMSE). Lecture series on computer and computational sciences, vol 1. VSP International Science Publishers, Zeist, pp 868–873
Juanga Y-T, Tung S-L, Chiu H-C (2011) Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions. Inf Sci 181(20):4539–4549
Liang JJ, Qin AK (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295
Niu B, Zhu Y, He X, Wu H (2007) MCPSO: a multi-swarm cooperative particle swarm optimizer. Appl Math Comput 185:1050–1062
Norouzzadeh MS, Ahmadzadeh MR, Palhang M (2012) LADPSO: using fuzzy logic to conduct PSO algorithm. Appl Intell 37:290–304
Khan SA, Engelbrecht AP (2012) A fuzzy particle swarm optimization algorithm for computer communication network topology design. Appl Intell 36:161–177
Mohamed Ben Ali Y (2012) Psychological model of particle swarm optimization based multiple emotions. Appl Intell 36:649–663
Shuang B, Chen J, Li Z (2011) Study on hybrid PS-ACO algorithm. Appl Intell 34:64–73
Acknowledgements
Financial supports from the National Natural Science Foundation of China (No. 61072039), the National High-Tech Research and Development Program of China (No. 2009AA01Z119), the 2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science (No. Z121101002812005) the Beijing Municipal Natural Science Foundation (No. 4102040) are highly appreciated.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, H., Zhao, X., Wang, K. et al. Cooperative Velocity Updating model based Particle Swarm Optimization. Appl Intell 40, 322–342 (2014). https://doi.org/10.1007/s10489-013-0459-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-013-0459-z