Browsing Mathematics by Author "Ali, M.M."
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Ali, M.M.; Kaelo, P. (Elsevier Ltd. www.elevier.com/locate/amc, NaN, 2008)[more][less]
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. URI: http://hdl.handle.net/10311/178 Files in this item: 2
Ali_Kaelo_AMC_2008.pdf (3.814Mb)license.txt (1.998Kb) -
Kaelo, P.; Ali, M.M. (Elsevier; www.elsevier.com/locate/ejor, NaN, 2006)[more][less]
Abstract: Modifications in mutation and localization in acceptance rule are suggested to the differential evolution algorithm for global optimization. Numerical experiments indicate that the resulting algorithms are considerably better than the original differential evolution algorithm. Therefore, they offer a reasonable alternative to many currently available stochastic algorithms, especially for problems requiring 'direct search type' methods. Numerical study is carried out using a set of 50 test problems many of which are inspired by practical applications. URI: http://hdl.handle.net/10311/64 Files in this item: 2
kaelo_EJOP_2006.pdf (1.708Mb)license.txt (1.998Kb)
Now showing items 1-2 of 2