zbMATH — the first resource for mathematics

A decentralization approach for swarm intelligence algorithms in networks applied to multi swarm PSO. (English) Zbl 1146.68457
Summary: The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are connected by a network. The approach is applied to a Particle Swarm Optimization (PSO) algorithm with multiple sub-swarms. PSO is a nature inspired metaheuristic where a swarm of particles searches for an optimum of a function. A multiple sub-swarms PSO can be used for example in applications where more than one optimum has to be found.
In the studied scenario the particles of the PSO algorithm correspond to data packets that are sent through the network of the computing system. Each data packet contains among other information the position of the corresponding particle in the search space and its sub-swarm number. In the proposed decentralized PSO algorithm the application specific tasks, i.e. the function evaluations, are done by the autonomous components of the system. The more general tasks, like the dynamic clustering of data packets, are done by the routers of the network.
Simulation experiments show that the decentralized PSO algorithm can successfully find a set of minimum values for the used test functions. It was also shown that the PSO algorithm works well for different type of networks, like scale-free network and ring like networks.
The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self-organization and have no central control.

68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C59 Approximation methods and heuristics in mathematical programming
Full Text: DOI
[1] DOI: 10.1126/science.286.5439.509 · Zbl 1226.05223 · doi:10.1126/science.286.5439.509
[2] Bellman, K.L., Igel, C., Schmeck, H. and Würtz, R.P. (2007), ”Special issue on organic computing”,ACM Transactions on Autonomous and Adaptive Systems, May 1.
[3] DOI: 10.1109/TEVC.2005.857074 · Zbl 05452217 · doi:10.1109/TEVC.2005.857074
[4] DOI: 10.1016/j.amc.2006.12.066 · Zbl 1122.65358 · doi:10.1016/j.amc.2006.12.066
[5] DOI: 10.1016/j.ins.2006.09.016 · Zbl 05128474 · doi:10.1016/j.ins.2006.09.016
[6] DOI: 10.1093/ietisy/e89-d.3.1181 · doi:10.1093/ietisy/e89-d.3.1181
[7] DOI: 10.1016/j.asoc.2007.05.005 · Zbl 05391585 · doi:10.1016/j.asoc.2007.05.005
[8] DOI: 10.1162/106365602760234081 · Zbl 05412730 · doi:10.1162/106365602760234081
[9] DOI: 10.1142/S0129054105003017 · Zbl 1089.90500 · doi:10.1142/S0129054105003017
[10] DOI: 10.1109/ICEC.1996.542701 · doi:10.1109/ICEC.1996.542701
[11] DOI: 10.1016/j.amc.2006.07.026 · Zbl 1112.65055 · doi:10.1016/j.amc.2006.07.026
[12] Passaro, A. and Starita, A. (2006), ”Clustering particles for multimodal function optimization”,Proceedings of GSICE/WIVA, published on CD, pp. 1970-5077.
[13] Schmeck, H. (2005), ”Organic computing”,Künstliche Intelligenz, Vol. 5 No. 3, pp. 68-9.
[14] DOI: 10.1016/S0020-0190(02)00447-7 · Zbl 1156.90463 · doi:10.1016/S0020-0190(02)00447-7
[15] DOI: 10.1038/30918 · Zbl 1368.05139 · doi:10.1038/30918
[16] Yao, J., Kharma, N., Grogono, P. and de Maisonneuve, O. (2005), ”BMPGA: a bi-objective multi-population genetic algorithm for multi-modal function optimization”,Proceedings IEEE Congress on Evolutionary Computation, pp. 816-23.
[17] Tillett, J.T., Rao, R.M., Sahin, F. and Rao, T.M. (2003), ”Particle swarm optimization for the clustering of wireless sensors”,Proceedings of SPIE, The International Society for Optical Engineering, Bellingham, WA, Vol. 5100, pp. 73-83. · doi:10.1117/12.499080
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. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.