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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/23260
Title: A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization
Authors: Gupta S.
Deep, K.
Mirjalili S.
Kim J.H.
Published in: Expert Systems with Applications
Abstract: Inspired by the mathematical characteristics of sine and cosine trigonometric functions, the Sine Cosine Algorithm (SCA) has shown competitive performance among other meta-heuristic algorithms. However, despite its sufficient global search ability, its low exploitation ability and immature balance between exploitation and exploration remain weaknesses. In order to improve Sine Cosine Algorithm (SCA), this paper presents a modified version of the SCA called MSCA. Firstly, a non-linear transition rule is introduced instead of a linear transition to provide comparatively better transition from the exploration to exploitation. Secondly, the classical search equation of the SCA is modified by introducing the leading guidance based on the elite candidate solution. When the above proposed modified search mechanism fails to provide a better solution, in addition, a mutation operator is used to generate a new position to avoid the situation of getting trapped in locally optimal solutions during the search. Thus, the MSCA effectively maximizes the advantages of proposed strategies in maintaining a comparatively better balance of exploration and exploitation as compared to the classical SCA. The validity of the MSCA is tested on a set of 33 benchmark optimization problems and employed for training multilayer perceptrons. The numerical results and comparisons among several algorithms show the enhanced search efficiency of the MSCA. © 2020 Elsevier Ltd
Citation: Expert Systems with Applications, 154
URI: https://doi.org/10.1016/j.eswa.2020.113395
http://repository.iitr.ac.in/handle/123456789/23260
Issue Date: 2020
Publisher: Elsevier Ltd
Keywords: Algorithm
Benchmark
Engineering optimization problems
Exploration and exploitation
Genetic Algorithm
Grey Wolf Optimizer
Multilayer perceptron
Optimization
Particle Swarm Optimization
Sine Cosine Algorithm
ISSN: 9574174
Author Scopus IDs: 57209786185
8561208900
51461922300
35100406500
Author Affiliations: Gupta, S., Institute for Mega Construction, Korea University, Seoul, 02841, South Korea
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
Mirjalili, S., Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, 90 Bowen Terrace, Fortitude Valley, Queensland 4006, Australia
Kim, J.H., School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, South Korea
Corresponding Author: Deep, K.; Department of Mathematics, India; email: kusumfma@iitr.ac.in
Appears in Collections:Journal Publications [MA]

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