PSO2: Particle swarm optimization with PSO-based local search

Several attempts have been made to enhance PSO performance by combining it with a local search method. Following the same track, we present in this paper local search in PSO performed by smaller independent swarms of PSO producing PSO2. Different modifications are made to help basic PSO2 enhance performance. PSO2-RS and PSO2-SA are 2 modified versions of PSO2 that targeted to increase the swarm diversity. Increasing the local search swarms sizes as the search progresses is another modification made to basic PSO2 in order to change the algorithm behavior to be more exploitive. The final algorithm is examined against 4 functions of the CEC-2005 benchmark suite and results are reported.

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

[2]  C. Mohan,et al.  Multi-phase generalization of the particle swarm optimization algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Jing J. Liang,et al.  Problem Definitions for Performance Assessment of Multi-objective Optimization Algorithms , 2007 .

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

[5]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search , 2005, 2005 IEEE Congress on Evolutionary Computation.

[6]  Mehmet Fatih Tasgetiren,et al.  Dynamic multi-swarm particle swarm optimizer with harmony search , 2011, Expert Syst. Appl..

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

[8]  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).

[9]  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.

[10]  Xi-Huai Wang,et al.  Hybrid particle swarm optimization with simulated annealing , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[11]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[12]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).