A Simple and Fast Particle Swarm Optimization

Particle Swarm Optimization (PSO) has shown its good performance on well-known numerical function problems. However, on some multimodal functions the PSO easily suffers from premature convergence because of the rapid decline in diversity. Some diversity-guided PSO algorithms have been proposed to maintain diversity, while these techniques cost much computation time on the calculation of diversity. In this paper, a simple and fast PSO (hybrid PSO, namely HPSO) is proposed, which indirectly maintains the diversity of swarm but not compute it. Experimental studies on 16 well-known benchmark functions show that the HPSO not only obtains better performance than the standard PSO and other two diversityguided PSO algorithms, but almost cost the same computation time with the standard PSO. In addition, a comprehensive set of experiments including the average computation time, the effects of crossover rate ( CR) on the performance of HPSO, the successful rate of the elitist selection and the effects ofCR on the diversity are empirically verified.

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

[2]  Michael N. Vrahatis,et al.  Tackling magnetoencephalography with particle swarm optimization , 2009, Int. J. Bio Inspired Comput..

[3]  Russell C. Eberhart,et al.  Recent advances in particle swarm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[4]  Rajesh Kumar,et al.  A new hybrid multi-agent-based particle swarm optimisation technique , 2009, Int. J. Bio Inspired Comput..

[5]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[6]  Wenbo Xu,et al.  A Diversity-Guided Quantum-Behaved Particle Swarm Optimization Algorithm , 2006, SEAL.

[7]  Wang Jiaying,et al.  A modified particle swarm optimization algorithm , 2005 .

[8]  Rasmus K. Ursem,et al.  Diversity-Guided Evolutionary Algorithms , 2002, PPSN.

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  Jacques Riget,et al.  A Diversity-Guided Particle Swarm Optimizer - the ARPSO , 2002 .

[11]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[12]  Zhijian Wu,et al.  An improved Particle Swarm Optimization with adaptive jumps , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

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

[14]  Millie Pant,et al.  A Simple Diversity Guided Particle Swarm Optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[15]  Zhihua Cui,et al.  Particle swarm optimization with FUSS and RWS for high dimensional functions , 2008, Appl. Math. Comput..

[16]  Hui Wang,et al.  Opposition-based particle swarm algorithm with cauchy mutation , 2007, 2007 IEEE Congress on Evolutionary Computation.

[17]  Ying Tan,et al.  Predicted modified PSO with time-varying accelerator coefficients , 2009, Int. J. Bio Inspired Comput..