Fully Learned Multi-swarm Particle Swarm Optimization

This paper presents a new variant of PSO, called fully learned multi-swarm particle swarm optimization (FLMPSO) for global optimization. In FLMPSO, the whole population is divided into a number of sub-swarms, in which the learning probability is employed to influence the exemplar of each individual and the center position of the best experience found so far by all the sub-swarms is also used to balance exploration and exploitation. Each particle updates its velocity based on its own historical experience or others relying on the learning probability, and the center position is also applied to adjust its flying. The experimental study on a set of six test functions demonstrates that FLMPSO outperform the others in terms of the convergence efficiency and the accuracy.

[1]  Zhu Zhu,et al.  Hybridization of particle swarm optimization with the K-Means algorithm for clustering analysis , 2010, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA).

[2]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[3]  Hanning Chen,et al.  PS2O: A multi-swarm optimizer for discrete optimization , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[4]  Yi Zhuo Guo,et al.  Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means , 2013 .

[5]  Qian Wang,et al.  A hybrid search strategy based particle swarm optimization algorithm , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).

[6]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  Ben Niu,et al.  An Improved MCPSO with Center Communication , 2008, 2008 International Conference on Computational Intelligence and Security.

[10]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[11]  Jing J. Liang,et al.  Multi-swarm Particle Swarm Optimization with a Center Learning Strategy , 2013, ICSI.

[12]  Ying Tan,et al.  Advances in Swarm Intelligence , 2016, Lecture Notes in Computer Science.

[13]  Konstantinos E. Parsopoulos UPSO : A Unified Particle Swarm Optimization Scheme , 2004 .

[14]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[15]  Q. Henry Wu,et al.  MCPSO: A multi-swarm cooperative particle swarm optimizer , 2007, Appl. Math. Comput..

[16]  Kalyan Veeramachaneni,et al.  Fitness-distance-ratio based particle swarm optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).