A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization

Particle swarm optimization (PSO) is characterized by a fast convergence, which can lead the algorithms of this class to stagnate in local optima. In this paper, a variant of the standard PSO algorithm is presented, called PSO-2S, based on several initializations in different zones of the search space, using charged particles. This algorithm uses two kinds of swarms, a main one that gathers the best particles of auxiliary ones, initialized several times. The auxiliary swarms are initialized in different areas, then an electrostatic repulsion heuristic is applied in each area to increase its diversity. We analyse the performance of the proposed approach on a testbed made of unimodal and multimodal test functions with and without coordinate rotation and shift. The Lennard-Jones potential problem is also used. The proposed algorithm is compared to several other PSO algorithms on this benchmark. The obtained results show the efficiency of the proposed algorithm.

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

[2]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[3]  J. Kennedy,et al.  Neighborhood topologies in fully informed and best-of-neighborhood particle swarms , 2003, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[4]  Jian Wang,et al.  An Improved Particle Swarm Optimization Algorithm , 2011 .

[5]  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.

[6]  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).

[7]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[8]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[9]  Michael N. Vrahatis,et al.  Unified Particle Swarm Optimization in Dynamic Environments , 2005, EvoWorkshops.

[10]  M. Hoare.  Structure and Dynamics of Simple Microclusters , 2007 .

[11]  N. J. A. Sloane,et al.  Sphere Packings, Lattices and Groups , 1987, Grundlehren der mathematischen Wissenschaften.

[12]  Haiyan Jiang,et al.  Global Optimization of Lennard-Jones Clusters by a Parallel Fast Annealing Evolutionary Algorithm , 2002, J. Chem. Inf. Comput. Sci..

[13]  Atsushi Ishigame,et al.  Particle swarm optimization based on the concept of tabu search , 2007, 2007 IEEE Congress on Evolutionary Computation.

[14]  Russell C. Eberhart,et al.  Evolutionary computation implementations , 2007 .

[15]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[16]  R. W. Dobbins,et al.  Computational intelligence PC tools , 1996 .

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

[18]  Amir Nakib,et al.  A New Multiagent Algorithm for Dynamic Continuous Optimization , 2010, Int. J. Appl. Metaheuristic Comput..

[19]  T. E. Simos.  Proceedings of the international conference on Computational methods in sciences and engineering , 2003 .

[20]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

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

[22]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[23]  Konstantinos E. Parsopoulos,et al.  UPSO: A Unified Particle Swarm Optimization Scheme , 2019, International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004).

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

[25]  R. Salomon.  Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. , 1996, Bio Systems.

[26]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[27]  Junyan Wang,et al.  Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization , 2011, ICSI.

[28]  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).

[29]  Yann Cooren.  Perfectionnement d'un algorithme adaptatif d'optimisation par essaim particulaire : application en génie médical et en électronique. (Improvement of an adaptive algorithm of Optimization by Swarm Particulaire : application in medical engineering and in electronics) , 2008 .

[30]  Yong Lu,et al.  A robust stochastic genetic algorithm (StGA) for global numerical optimization , 2004, IEEE Transactions on Evolutionary Computation.

[31]  Shang-Jeng Tsai,et al.  Efficient Population Utilization Strategy for Particle Swarm Optimizer , 2009, IEEE Trans. Syst. Man Cybern. Part B.