Simple jQuery Dropdowns
Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/31464
Title: Genetic algorithm search space splicing particle swarm optimization as general-purpose optimizer
Authors: Hao Li
Chanin Nantasenamat
Teerawat Monnor
Chartchalerm Isarankura-Na-Ayudhya
Virapong Prachayasittikul
Mahidol University
Keywords: Chemical Engineering;Chemistry;Computer Science
Issue Date: 15-Oct-2013
Citation: Chemometrics and Intelligent Laboratory Systems. Vol.128, (2013), 153-159
Abstract: A heuristic search space splicing scheme has been implemented to aid the convergence of the particle swarm optimization (PSO) algorithm to the global optimum. Genetic algorithm (GA) was used to splice the search space into smaller subspaces, thereby reducing the number of local minima. PSO algorithm was subsequently used to locate the global optima in the subspaces. A set of 11 well-known test functions had been used for the assessment of this novel GA search space splicing PSO (GA-SSS-PSO) architecture. Of the methods tested in this study, the GA-SSS-PSO approach was the only one that could optimize all functions to a desirable level. To demonstrate the algorithm's applicability, three optimization tasks of different categories commonly faced in the field of chemometrics were subjected to optimization by GA-SSS-PSO and results indicated that the novel hybrid algorithm provided robust performance for both theoretical and real life problems and may be suited as general-purpose optimizer for medium-sized optimization tasks.© 2013 Elsevier B.V.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84884383367&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/31464
ISSN: 18733239
01697439
Appears in Collections:Scopus 2011-2015

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.