A fresh Particle Swarm Optimizations: A position paper

This paper contributes a novel Particle Swarm Optimization (PSO) method. The particle is updated not only by the best position in history (pbest) and the best position among all the particles in the swarm (gbest), but also using the position that is nearest neighbor of pbest. Additionally, we introduce a modified PSO algorithm based on the fuzzy clustering of particles to communication with the nearest neighbor for reducing the premature convergence and in sequel enhance the capability of global exploration. We validate our methods by an extensive experimental study on four benchmark test functions and compare the result with basic PSO.

[1]  Ling Li,et al.  Learning and Measuring Specialization in Collaborative Swarm Systems , 2004, Adapt. Behav..

[2]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[3]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[4]  Dongyong Yang,et al.  Self-Adaptive Crossover Particle Swarm Optimizer for Multi-dimension Functions Optimization , 2007, Third International Conference on Natural Computation (ICNC 2007).

[5]  Olga Sourina,et al.  Automatic clustering and boundary detection algorithm based on adaptive influence function , 2008, Pattern Recognit..

[6]  Young-Il Kim,et al.  A cluster validation index for GK cluster analysis based on relative degree of sharing , 2004, Inf. Sci..

[7]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[8]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[9]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[10]  Andries P. Engelbrecht,et al.  Image Classification using Particle Swarm Optimization , 2002, SEAL.

[11]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[12]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[13]  Yu Liu,et al.  Center particle swarm optimization , 2007, Neurocomputing.

[14]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[15]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[16]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[17]  Han Huang,et al.  A Particle Swarm Optimization Algorithm with Crossover Operator , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[18]  Mohammad Hossein Fazel Zarandi,et al.  A type-2 fuzzy rule-based expert system model for stock price analysis , 2009, Expert Syst. Appl..

[19]  Thomas Stützle,et al.  Convergence behavior of the fully informed particle swarm optimization algorithm , 2008, GECCO '08.

[20]  A. Engelbrecht,et al.  A new locally convergent particle swarm optimiser , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[21]  Andries Petrus Engelbrecht,et al.  Using neighbourhoods with the guaranteed convergence PSO , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).