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dc.contributor.authorGupta S.-
dc.contributor.authorDeep, K.-
dc.contributor.authorMirjalili S.-
dc.contributor.authorKim J.H.-
dc.identifier.citationExpert Systems with Applications, 154-
dc.description.abstractInspired 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-
dc.publisherElsevier Ltd-
dc.relation.ispartofExpert Systems with Applications-
dc.subjectEngineering optimization problems-
dc.subjectExploration and exploitation-
dc.subjectGenetic Algorithm-
dc.subjectGrey Wolf Optimizer-
dc.subjectMultilayer perceptron-
dc.subjectParticle Swarm Optimization-
dc.subjectSine Cosine Algorithm-
dc.titleA modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization-
dc.affiliationGupta, S., Institute for Mega Construction, Korea University, Seoul, 02841, South Korea-
dc.affiliationDeep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India-
dc.affiliationMirjalili, S., Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, 90 Bowen Terrace, Fortitude Valley, Queensland 4006, Australia-
dc.affiliationKim, J.H., School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, South Korea-
dc.description.correspondingauthorDeep, K.; Department of Mathematics, India; email:
Appears in Collections:Journal Publications [MA]

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