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dc.contributor.author Ali, M.M.
dc.contributor.author Kaelo, P.
dc.date.accessioned 2008-07-30T07:08:09Z
dc.date.available 2008-07-30T07:08:09Z
dc.date.issued 2008
dc.identifier.citation Ali, M.M. & Kaelo, P. (2008) Improved particle swarm algorithms for global optimization, Applied Mathematics and Computation 196, pp. 578-593 en
dc.identifier.issn 0096-3003/S
dc.identifier.uri http://hdl.handle.net/10311/178
dc.description.abstract Particle swarm optimization algorithm has recently gained much attention in the global optimization research community. As a result, a few variants of the algorithm have been suggested. In this paper, we study the efficiency and robustness of a number of particle swarm optimization algorithms and identify the cause for their slow convergence. We then propose some modifications in the position update rule of particle swarm optimization algorithm in order to make the convergence faster. These modifications result in two new versions of the particle swarm optimization algorithm. A numerical study is carried out using a set of 54 test problems some of which are inspired by practical applications. Results show that the new algorithms are much more robust and efficient than some existing particle swarm optimization algorithms. A comparison of the new algorithms with the differential evolution algorithm is also made. en
dc.language.iso en en
dc.publisher Elsevier Ltd. www.elevier.com/locate/amc en
dc.subject Particle swarm en
dc.subject Differential evolution en
dc.subject Population set en
dc.subject Global optimization en
dc.title Improved particle swarm algorithms for global optimization en
dc.type Article en


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