×

Fusion global-local-topology particle swarm optimization for global optimization problems. (English) Zbl 1407.90353

Summary: In recent years, particle swarm optimization (PSO) has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO) is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20) unimodal and multimodal nonlinear benchmark functions and its performance is compared with several well-known PSO algorithms. The experimental results showed that the proposed method improves the performance of PSO algorithm in terms of solution accuracy and convergence speed.

MSC:

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

References:

[1] Kennedy, J.; Eberhart, R., Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks · doi:10.1109/ICNN.1995.488968
[2] Chan, K. Y.; Dillon, T. S.; Kwong, C. K., Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm, Information Sciences, 181, 9, 1623-1640 (2011) · doi:10.1016/j.ins.2011.01.006
[3] Fathi, V.; Montazer, G. A., An improvement in RBF learning algorithm based on PSO for real time applications, Neurocomputing, 111, 169-176 (2013) · doi:10.1016/j.neucom.2012.12.024
[4] Huang, H.; Qin, H.; Hao, Z.; Lim, A., Example-based learning particle swarm optimization for continuous optimization, Information Sciences, 182, 1, 125-138 (2012) · Zbl 1250.90113 · doi:10.1016/j.ins.2010.10.018
[5] Qu, B. Y.; Liang, J. J.; Suganthan, P. N., Niching particle swarm optimization with local search for multi-modal optimization, Information Sciences, 197, 131-143 (2012) · doi:10.1016/j.ins.2012.02.011
[6] Wang, H.; Moon, I.; Yang, S.; Wang, D., A memetic particle swarm optimization algorithm for multimodal optimization problems, Information Sciences, 197, 38-52 (2012) · doi:10.1016/j.ins.2012.02.016
[7] Beheshti, Z.; Shamsuddin, S. M., International Journal of Advances in Soft Computing & Its Applications, 5, 1, 1-35 (2013)
[8] Kennedy, J.; Mendes, R., Population structure and particle swarm performance, Proceedings of IEEE international conference on Evolutionary Computation
[9] Kennedy, J.; Mendes, R., Neighborhood topologies in fully informed and best-of-neighborhood particle swarms, IEEE Transactions on Systems, Man, and Cybernetics Part C, 36, 515-519 (2006)
[10] Mendes, R.; Kennedy, J.; Neves, J., The fully informed particle swarm: simpler, maybe better, IEEE Transactions on Evolutionary Computation, 8, 3, 204-210 (2004) · doi:10.1109/TEVC.2004.826074
[11] Ratnaweera, A.; Halgamuge, S. K.; Watson, H. C., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Transactions on Evolutionary Computation, 8, 3, 240-255 (2004) · doi:10.1109/TEVC.2004.826071
[12] Liang, J. J.; Suganthan, P. N., Dynamic multi-swarm particle swarm optimizer, Proceedings of the IEEE Swarm Intelligence Symposium (SIS ’05) · doi:10.1109/SIS.2005.1501611
[13] Liang, J. J.; Qin, A. K.; Suganthan, P. N.; Baskar, S., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transactions on Evolutionary Computation, 10, 3, 281-295 (2006) · doi:10.1109/TEVC.2005.857610
[14] Beheshti, Z.; Shamsuddin, S. M. H.; Hasan, S., MPSO: median-oriented particle swarm optimization, Applied Mathematics and Computation, 219, 11, 5817-5836 (2013) · Zbl 1274.90503 · doi:10.1016/j.amc.2012.12.013
[15] Beheshti, Z.; Shamsuddin, S. M. Hj., CAPSO: centripetal accelerated particle swarm optimization, Information Sciences, 258, 54-79 (2014) · doi:10.1016/j.ins.2013.08.015
[16] Pant, M.; Radha, T.; Singh, V. P., A new particle swarm optimization with quadratic interpolation, Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA ’07) · doi:10.1109/ICCIMA.2007.24
[17] Liu, P.; Leng, W.; Fang, W., Training ANFIS model with an improved quantum-behaved particle swarm optimization algorithm, Mathematical Problems in Engineering, 2013 (2013) · doi:10.1155/2013/595639
[18] Zhan, Z.-H.; Zhang, J.; Li, Y.; Chung, H. S.-H., Adaptive particle swarm optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 39, 6, 1362-1381 (2009) · doi:10.1109/TSMCB.2009.2015956
[19] Juang, Y.-T.; Tung, S.-L.; Chiu, H.-C., Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions, Information Sciences, 181, 20, 4539-4549 (2011) · Zbl 1242.68288 · doi:10.1016/j.ins.2010.11.025
[20] Beheshti, Z.; Shamsuddin, S. M.; Beheshti, E.; Yuhaniz, S. S., Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis, Soft Computing (2013) · doi:10.1007/s00500-013-1198-0
[21] Beheshti, Z.; Shamsuddin, S. M.; Yuhaniz, S. S., Binary accelerated particle swarm algorithm (BAPSA) for discrete optimization problems, Journal of Global Optimization, 57, 2, 549-573 (2013) · Zbl 1315.90033 · doi:10.1007/s10898-012-0006-1
[22] Cavuslu, M. A.; Karakuzu, C.; Karakaya, F., Neural identification of dynamic systems on FPGA with improved PSO learning, Applied Soft Computing Journal, 12, 9, 2707-2718 (2012) · doi:10.1016/j.asoc.2012.03.022
[23] Liu, L.; Yang, S.; Wang, D., Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima, Information Sciences, 182, 1, 139-155 (2012) · doi:10.1016/j.ins.2010.11.013
[24] Beheshti, Z.; Shamsuddin, S. M., Centripetal accelerated particle swarm optimization and its applications in machine learning [Ph.D. thesis] (2013), Universiti Teknologi Malaysia
[25] Wang, Y.; Zhou, J.; Zhou, C.; Wang, Y.; Qin, H.; Lu, Y., An improved self-adaptive PSO technique for short-term hydrothermal scheduling, Expert Systems with Applications, 39, 3, 2288-2295 (2012) · doi:10.1016/j.eswa.2011.08.007
[26] Luo, Q.; Yi, D., A co-evolving framework for robust particle swarm optimization, Applied Mathematics and Computation, 199, 2, 611-622 (2008) · Zbl 1143.65046 · doi:10.1016/j.amc.2007.10.017
[27] Andrews, P. S., An investigation into mutation operators for particle swarm optimization, Proceedings of the IEEE Congress on Evolutionary Computation (CEC ’06)
[28] Lovbjerg, M.; Rasmussen, T. K.; Krink, T., Hybrid particle swarm optimizer with breeding and subpopulations, Proceedings of the 3rd Genetic and Evolutionary Computation Conference
[29] Tsafarakis, S.; Saridakis, C.; Baltas, G.; Matsatsinis, N., Hybrid particle swarm optimization with mutation for optimizing industrial product lines: an application to a mixed solution space considering both discrete and continuous design variables, Industrial Marketing Management, 42, 4, 496-506 (2013) · doi:10.1016/j.indmarman.2013.03.002
[30] Angeline, P. J., Using selection to improve particle swarm optimization, Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC ’98)
[31] Chen, Y.-P.; Peng, W.-C.; Jian, M.-C., Particle swarm optimization with recombination and dynamic linkage discovery, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 37, 6, 1460-1470 (2007) · doi:10.1109/TSMCB.2007.904019
[32] Zhan, Z.-H.; Zhang, J.; Li, Y.; Shi, Y.-H., Orthogonal learning particle swarm optimization, IEEE Transactions on Evolutionary Computation, 15, 6, 832-847 (2011) · doi:10.1109/TEVC.2010.2052054
[33] Gao, W.-F.; Liu, S.-Y.; Huang, L.-L., Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique, Communications in Nonlinear Science and Numerical Simulation, 17, 11, 4316-4327 (2012) · Zbl 1254.90300 · doi:10.1016/j.cnsns.2012.03.015
[34] Tang, K.; Yao, X.; Suganthan, P. N.; MacNish, C.; Chen, Y. P.; Chen, C. M.; Yang, Z., Benchmark functions for the CEC’2008 special session and competition on large scale global optimization, Anhui, China: Nature Inspired Computation and Applications Laboratory, USTC, Anhui, China
[35] Yao, X.; Liu, Y.; Lin, G., Evolutionary programming made faster, IEEE Transactions on Evolutionary Computation, 3, 2, 82-102 (1999) · doi:10.1109/4235.771163
[36] Salomon, R., Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms, BioSystems, 39, 3, 263-278 (1996) · doi:10.1016/0303-2647(96)01621-8
[37] Wilcoxon, F., Individual comparisons by ranking methods, Biometrics Bulletin, 1, 6, 80-83 (1945) · doi:10.2307/3001968
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.