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|Title:||Development of a parallel optimization method based on genetic simulated annealing algorithm||Authors:||Wang, Z.G.
Parallel genetic algorithm
|Issue Date:||Aug-2005||Citation:||Wang, Z.G., Wong, Y.S., Rahman, M. (2005-08). Development of a parallel optimization method based on genetic simulated annealing algorithm. Parallel Computing 31 (8-9) : 839-857. ScholarBank@NUS Repository. https://doi.org/10.1016/j.parco.2005.03.006||Abstract:||This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into sub-populations, and in each sub-population the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each sub-population are migrated to neighboring ones after a certain number of epochs. An implementation of the algorithm is discussed and the performance is evaluated against a standard set of test functions. PGSA shows some remarkable improvement in comparison with the conventional parallel genetic algorithm and the breeder genetic algorithm (BGA). © 2005 Elsevier B.V. All rights reserved.||Source Title:||Parallel Computing||URI:||http://scholarbank.nus.edu.sg/handle/10635/59913||ISSN:||01678191||DOI:||10.1016/j.parco.2005.03.006|
|Appears in Collections:||Staff Publications|
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