DocumentCode :
2916526
Title :
Improved Particle Swarm Optimization with low-discrepancy sequences
Author :
Pant, Millie ; Thangaraj, Radha ; Grosan, Crina ; Abraham, Ajith
Author_Institution :
Indian Inst. of Technol. Roorkee, Saharanpur
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
3011
Lastpage :
3018
Abstract :
Quasirandom or low discrepancy sequences, such as the Van der Corput, Sobol, Faure, Halton (named after their inventors) etc. are less random than a pseudorandom number sequences, but are more useful for computational methods which depend on the generation of random numbers. Some of these tasks involve approximation of integrals in higher dimensions, simulation and global optimization. Sobol, Faure and Halton sequences have already been used [7, 8, 9, 10] for initializing the swarm in a PSO. This paper investigates the effect of initiating the swarm with another classical low discrepancy sequence called Vander Corput sequence for solving global optimization problems in large dimension search spaces. The proposed algorithm called VC-PSO and another PSO using Sobol sequence (SO-PSO) are tested on standard benchmark problems and the results are compared with the Basic Particle Swarm Optimization (BPSO) which follows the uniform distribution for initializing the swarm. The simulation results show that a significant improvement can be made in the performance of BPSO, by simply changing the distribution of random numbers to quasi random sequence as the proposed VC-PSO and SO-PSO algorithms outperform the BPSO algorithm by noticeable percentage, particularly for problems with large search space dimensions.
Keywords :
particle swarm optimisation; random sequences; search problems; Sobol sequence; Vander Corput sequence; global optimization; integrals approximation; large dimension search spaces; low discrepancy sequence; low-discrepancy sequences; particle swarm optimization; pseudorandom number sequences; standard benchmark problems; Benchmark testing; Computational modeling; Genetic algorithms; Particle swarm optimization; Probability distribution; Random number generation; Random sequences; Robustness; Space technology; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
Type :
conf
DOI :
10.1109/CEC.2008.4631204
Filename :
4631204
Link To Document :
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