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Title: Tournament selection based probability scheme in spider monkey optimization algorithm
Authors: Gupta K.
Deep, K.
Geem Z.W.
Kim J.H.
Published in: Proceedings of Advances in Intelligent Systems and Computing
Abstract: In this paper, a modified version of Spider Monkey Optimization (SMO) algorithm is proposed. This modified version is named as Tournament selection based Spider Monkey Optimization (TS-SMO). TS-SMO replaces the fitness proportionate probability scheme of SMO with tournament selection based probability scheme with an objective to improve the exploration ability of SMO by avoiding premature convergence. The performance of the proposed variant is tested over a large benchmark set of 46 unconstrained benchmark problems of varying complexities broadly classified into two categories: scalable and non-scalable problems. The performance of TS-SO is compared with that of SMO. Results for scalable and non-scalable problems have been analysed separately. A statistical test is employed to access the significance of improvement in results. Numerical and statistical results show that the proposed modification has a positive impact on the performance of original SMO in terms of reliability, efficiency and accuracy. © Springer-Verlag Berlin Heidelberg 2016.
Citation: Proceedings of Advances in Intelligent Systems and Computing, (2016), 239- 250
Issue Date: 2016
Publisher: Springer Verlag
Keywords: Spider monkey optimization
Swarm intelligent techniques
Tournament selection
Unconstrained optimization
Learning algorithms
Bench-mark problems
Optimization algorithms
Pre-mature convergences
Swarm intelligent
Tournament selection
Unconstrained optimization
ISBN: 9.78E+12
ISSN: 21945357
Author Scopus IDs: 56954165300
Author Affiliations: Gupta, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India
Appears in Collections:Conference Publications [MA]

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