×

A dynamic inertia weight particle swarm optimization algorithm. (English) Zbl 1146.90533

Summary: Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly.

MSC:

90C59 Approximation methods and heuristics in mathematical programming
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Clerc, M.; Kennedy, J., The particle swarm: explosion stability and convergence in a multi-dimensional complex space, IEEE Trans Evolution Comput, 6, 1, 58-73 (2002)
[2] Eberhart, R.; Kennedy, J., New optimizer using particle swarm theory [C], (Proceedings of the sixth international symposium on micro machine and human science (1995), Nagoya: Nagoya Japan), p. 39-73
[3] Eberhart, R. C.; Shi, Y., Comparison between genetic algorithms and particle swarm optimization, (Evolutionary programming VII: proceedings of the seventh annual conference on evolutionary programming, San Diego CA (1998), Springer-Verlag: Springer-Verlag Berlin), 611-616
[4] Eberhart, R. C.; Shi, Y., Particle swarm optimization: developments, applications and resources, Proceedings of IEEE international conference on evolutionary computation, 81-86 (2001)
[5] Fan, H. Y., A modification to particle swarm optimization algorithm, Eng Comput, 19, 8, 970-989 (2002) · Zbl 1022.65007
[6] He, S.; QH, W.; Wen, J. Y.; Saunders, J. R.; Paton, R. C., A particle swarm optimizer with passive congregation[J], BioSystems, 78, 135-147 (2004)
[7] Ji, M. J.; Tang, H. W., Application of chaos in simulated annealing, Chaos, Solitons & Fractals, 21, 933-941 (2004) · Zbl 1045.37054
[8] Kennedy, J.; Eberhart, R., Particle swarm optimization[C], (IEEE intl conf on neural networks (1995), Perth: Perth Australia)
[9] Kennedy, J., Stereotyping: improving particle swarm performance with cluster analysis, Proceedings of the IEEE international conference on evolutionary computation, 1507-1512 (2000)
[10] Kennedy, J., The particle swarm: social adaptation of knowledge, (Proc. IEEE international conference on evolutionary computation, Indianapolis, IN, Piscataway (1997), IEEE Service Center: IEEE Service Center NJ, USA), 303-308
[11] Kennedy, J.; Mendes, R., Population structure and particle swarm performance, (Proceedings of the 2002 congress on evolutionary computation CEC2002 (2002), IEEE Press), 1671-1676
[12] Lian, Z. G.; Gu, X. S.; Jiao, Bin, A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan, Chaos, Solitons & Fractals (2006)
[13] Liu, B.; Wang, L.; Jin, Y. H.; Tang, F.; Huang, D. X., Improved particle swarm optimization combined with chaos, Chaos, Solitons & Fractals, 25, 1261-1271 (2005) · Zbl 1074.90564
[14] Lu, Z.; Shieh, L. S.; Chen, G. R., On robust control of uncertain chaotic systems: a sliding-mode synthesis via chaotic optimization, Chaos, Solitons & Fractals, 18, 819-827 (2003) · Zbl 1068.93053
[15] Robinson J, Sinton S, Rahmat-Samii Y. Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. In: IEEE antennas and propagation society international symposium and URSI national radio science meeting, IEEE, San Antonio, TX, 2002, p. 168-75.; Robinson J, Sinton S, Rahmat-Samii Y. Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. In: IEEE antennas and propagation society international symposium and URSI national radio science meeting, IEEE, San Antonio, TX, 2002, p. 168-75.
[16] Shi Y, Eberhart RC. A modified particle swarms optimiser. In: Proceedings of the IEEE international conference on evolutionary computation, 1997, p. 303-8.; Shi Y, Eberhart RC. A modified particle swarms optimiser. In: Proceedings of the IEEE international conference on evolutionary computation, 1997, p. 303-8.
[17] Shi Y, Eberhart R. A modified particle swarm optimizer [C]. In: IEEE world congress on computational intelligence, 1998, p. 69-73.; Shi Y, Eberhart R. A modified particle swarm optimizer [C]. In: IEEE world congress on computational intelligence, 1998, p. 69-73.
[18] Shi Y, Eberhart RC. Parameter selection in particle swarm optimization. In: Evolutionary programming VII: proceedings of the seventh annual conference on evolutionary programming, New York, 1998, p. 591-600.; Shi Y, Eberhart RC. Parameter selection in particle swarm optimization. In: Evolutionary programming VII: proceedings of the seventh annual conference on evolutionary programming, New York, 1998, p. 591-600.
[19] Shi, X. H.; Liang, Y. C.; Lee, H. P.; Lu, C.; Wang, L. M., An improved GA and a novel PSO-GA-based hybrid algorithm [J], Inform Process Lett, 93, 255-261 (2005) · Zbl 1173.68828
[20] Trelea, I. C., The particle swarm optimization algorithm: convergence analysis and parameter selection, Inform Process Lett, 85, 6, 317-325 (2003) · Zbl 1156.90463
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.