Dynamic cluster in particle swarm optimization algorithm. (English) Zbl 1415.68189
Summary: Particle swarm optimization is an optimization method based on a simulated social behavior displayed by artificial particles in a swarm, inspired from bird flocks and fish schools. An underlying component that influences the exchange of information between particles in a swarm, is its topological structure. Therefore, this property has a great influence on the comportment of the optimization method. In this study, we propose DCluster: a dynamic topology, based on a combination of two well-known topologies viz. Four-cluster and Fitness. The proposed topology is analyzed, and compared to six other topologies used in the standard PSO algorithm using a set of benchmark test functions and several well-known constrained and unconstrained engineering design problems. Our comparisons demonstrate that DCluster outperforms the other tested topologies and leads to satisfactory performance while avoiding the problem of premature convergence.
MSC:
| 68T20 | Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) |
| 90C59 | Approximation methods and heuristics in mathematical programming |
Keywords:
particle swarm optimization; neighborhood topologies; social networks; global best; local best; dynamic clusterSoftware:
CEC 05References:
| [1] | Ali M, Pant M, Singh VP (2010) Two modified differential evolution algorithms and their applications to engineering design problems. World J Model Simul 6(1):72-80 |
| [2] | Bastos-Filho CJA, Carvalho DF, Caraciolo MP, Miranda PBC, Figueiredo EMN (2009) Multi-ring particle swarm optimization. In: Wellington Pinheiro dos Santos (ed) Evolutionary computation. InTech, Vienna |
| [3] | Cagnina LC, Esquivel SC, Coello CA (2008) Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica (Slovenia) 32(3):319-326 · Zbl 1155.90482 |
| [4] | Clerc M (2006) Particle swarm optimization. ISTE (International Scientific and Technical Encyclopaedia), London |
| [5] | Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, MHS’95Nagoya, Japan, pp 39-43 |
| [6] | El Dor A, Clerc M, Siarry P (2012) A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization. Comput Optimiz Appl 53(1):271-295 · Zbl 1259.90130 · doi:10.1007/s10589-011-9449-4 |
| [7] | El-Saleh AA, Ismail M, Viknesh R, Mark CC, Chan ML (2009) Particle swarm optimization for mobile network design. IEICE Electron Express 6(17):1219-1225 · doi:10.1587/elex.6.1219 |
| [8] | Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, Chichester |
| [9] | Golinski J (1973) An adaptive optimization system applied to machine synthesis. J Eng Ind Trans ASME 8(4):419-436 |
| [10] | Homaifar A, Qi CX, Lai SH (1994) Constrained optimization via genetic algorithms. Simulation 62(4):242-253 · doi:10.1177/003754979406200405 |
| [11] | Hsieh ST, Sun TY, Liu CC, Tsai SJ (2009) Efficient population utilization strategy for particle swarm optimizer. IEEE Trans Syst Man Cybernet B: Cybernet 39(2):444-456 · doi:10.1109/TSMCB.2008.2006628 |
| [12] | Janson S, Middendorf M (2005) A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans Syst Man Cybernet B 35(6):1272-1282 · doi:10.1109/TSMCB.2005.850530 |
| [13] | Kannan BK, Kramer SN (1994) An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des 116(2):318-320 · doi:10.1115/1.2919393 |
| [14] | Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 congress on evolutionary computation, vol 3, CEC’99DC USA, Washington, pp 1931-1938 |
| [15] | Kennedy J (2000) Stereotyping: improving particle swarm performance with cluster analysis. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp 1507-1512. IEEE |
| [16] | Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 IEEE Congress on Evolutionary Computation, CEC’02Honolulu, HI, USA, pp 1671-1676 |
| [17] | Lane J, Engelbrecht A, Gain J (2008) Particle swarm optimization with spatially meaningful neighbours. In: Proceedings of the 2008 IEEE Swarm Intelligence Symposium. Piscataway, NJ, IEEE, pp 1-8. |
| [18] | Li C, Yang S, Nguyen TT (2011) A self-learning particle swarm optimizer for global optimization problems. IEEE Trans Syst Man Cybernet B 42(3):627-646 |
| [19] | Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In: Proceedings of the 2005 IEEE Swarm Intelligence Symposium, pp 124-129, Pasadena, CA, USA |
| [20] | Liang JJ, Suganthan PN (2006) Dynamic multi-swarm particle swarm optimizer with a novel constraint-handling mechanism. In Proceedings of the IEEE Congress on Evolutionary Computation, pp 9-16, Canada |
| [21] | Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629-640 · doi:10.1016/j.asoc.2009.08.031 |
| [22] | Marsaglia G, Zaman A (1993) The KISS generator. Technical report. Department of statistics, Florida State University, Tallahassee, FL, USA |
| [23] | Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler. May be better. IEEE Trans Evolut Comput 8(3):204-210 · doi:10.1109/TEVC.2004.826074 |
| [24] | Mendes R, Kennedy J, Neves J (2003) Watch thy neighbor or how the swarm can learn from its environment. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pp 88-94, Indianapolis, Indiana, USA |
| [25] | Nasir M, Das S, Maity D, Sengupta S, Halder U, Suganthan PN (2012) A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Inf Sci 209:16-36 · doi:10.1016/j.ins.2012.04.028 |
| [26] | Particle swarm central (2012) http://particleswarm.info. Accessed 1 Oct 2014 |
| [27] | Ragsdell K, Phillips D (1976) Optimal design of a class of welded structures using geometric programming. J Eng Ind Trans ASME 98(3):1021-1025 · doi:10.1115/1.3438995 |
| [28] | Richards M, Ventura D (2003) Dynamic cociometry in particle swarm optimization. In: Proceedings of the joint conference on information sciences, pp 1557-1560, Cary, North Carolina USA |
| [29] | Safavieh E, Gheibi A, Abolghasemi M, Mohades A (2009) Particle swarm optimization with Voronoi neighborhood. In Proceedings of the 14th international CSI computer conference (CSICC2009), pp 397-402, Tehran, Iran |
| [30] | Salomon R (1996) Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. BioSyst 39:263-278 · doi:10.1016/0303-2647(96)01621-8 |
| [31] | Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In Proceedings of the 1999 congress on evolutionary computation, CEC’99, vol 3, pp 1945-1950, Washington, DC USA |
| [32] | Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report. Nanyang Technological University, Singapore |
| [33] | Wang YX, Xiang QL (2008a) Particle swarms with dynamic ring topology. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp 419-423. IEEE |
| [34] | Wang YX, Xiang QL (2008b) Exploring new learning strategies in differential evolution algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp 204-209. IEEE |
| [35] | Watts DJ (1999) Small worlds : the dynamics of networks between order and randomness. Princeton University Press, Princeton |
| [36] | Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440-442 · Zbl 1368.05139 · doi:10.1038/30918 |
| [37] | Zhao SZ, Suganthan PN, Pan QK, Tasgetiren MF (2011) Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst Appl 38(4):3735-3742 · doi:10.1016/j.eswa.2010.09.032 |
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