Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.parco.2005.03.006
Title: Development of a parallel optimization method based on genetic simulated annealing algorithm
Authors: Wang, Z.G. 
Wong, Y.S. 
Rahman, M. 
Keywords: Genetic algorithm
Parallel genetic algorithm
Simulated annealing
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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