A new fusion of salp swarm with sine cosine for optimization of non-linear functions

The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the salp swarm algorithm with sine cosine algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standar...

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Veröffentlicht in:	Engineering with computers Jg. 36; H. 1; S. 185 - 212
Hauptverfasser:	Singh, Narinder, Son, Le Hoang, Chiclana, Francisco, Magnot, Jean-Pierre
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.01.2020 Springer Nature B.V Springer Verlag
Schlagworte:	Algorithms Analysis of PDEs Basic converters Business administration CAE) and Design Calculus of Variations and Optimal Control; Optimization Cantilever beams Chaotic Dynamics Classical Mechanics Compression springs Computer Science Computer-Aided Engineering (CAD Control Convergence Differential Geometry Exactly Solvable and Integrable Systems Exploitation Exploration General Mathematics Heuristic methods Humanities and Social Sciences Library and information sciences Linear functions Math. Applications in Chemistry Mathematical analysis Mathematical and Computational Engineering Mathematical Physics Mathematics Metric Geometry Nonlinear Sciences Operator Algebras Optimization Original Article Pattern Formation and Solitons Physics Quantum Physics Searching Spectral Theory Systems Theory Trigonometric functions Salp swarm algorithm Exploration and exploitation Standard global optimization functions Sine cosine algorithm Heuristic hybridization
ISSN:	0177-0667, 1435-5663
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Abstract	The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the salp swarm algorithm with sine cosine algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standard algorithms. In this method, the position of salp swarm in the search space is updated using the position equations of sine cosine; hence the best and possible optimal solutions are obtained based on the sine or cosine function. During this process, each salp adopts the information sharing strategy of sine and cosine functions to improve their exploration and exploitation ability. The inspiration behind incorporating changes in salp swarm optimizer algorithm is to assist the basic approach to avoid premature convergence and to rapidly guide the search towards the probable search space. The algorithm is validated on 22 standard mathematical optimization functions and 3 applications namely the 3-bar truss, tension/compression spring and cantilever beam design problems. The aim is to examine and confirm the valuable behaviors of HSSASCA in searching the best solutions for optimization functions. The experimental results reveal that HSSASCA algorithm achieves the highest accuracies with least runtime in comparison with the others.
AbstractList	The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the salp swarm algorithm with sine cosine algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standard algorithms. In this method, the position of salp swarm in the search space is updated using the position equations of sine cosine; hence the best and possible optimal solutions are obtained based on the sine or cosine function. During this process, each salp adopts the information sharing strategy of sine and cosine functions to improve their exploration and exploitation ability. The inspiration behind incorporating changes in salp swarm optimizer algorithm is to assist the basic approach to avoid premature convergence and to rapidly guide the search towards the probable search space. The algorithm is validated on 22 standard mathematical optimization functions and 3 applications namely the 3-bar truss, tension/compression spring and cantilever beam design problems. The aim is to examine and confirm the valuable behaviors of HSSASCA in searching the best solutions for optimization functions. The experimental results reveal that HSSASCA algorithm achieves the highest accuracies with least runtime in comparison with the others.
Author	Singh, Narinder Son, Le Hoang Chiclana, Francisco Magnot, Jean-Pierre
Author_xml	– sequence: 1 givenname: Narinder surname: Singh fullname: Singh, Narinder organization: Department of Mathematics, Punjabi University – sequence: 2 givenname: Le Hoang orcidid: 0000-0001-6356-0046 surname: Son fullname: Son, Le Hoang email: sonlh@vnu.edu.vn organization: VNU Information Technology Institute, Vietnam National University – sequence: 3 givenname: Francisco surname: Chiclana fullname: Chiclana, Francisco organization: School of Computer Science and Informatics, De Montfort University – sequence: 4 givenname: Jean-Pierre surname: Magnot fullname: Magnot, Jean-Pierre organization: LAREMA, Universitéd’ Angers
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Keywords	Salp swarm algorithm Exploration and exploitation Standard global optimization functions Sine cosine algorithm Heuristic hybridization
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SubjectTerms	Algorithms Analysis of PDEs Basic converters Business administration CAE) and Design Calculus of Variations and Optimal Control; Optimization Cantilever beams Chaotic Dynamics Classical Mechanics Compression springs Computer Science Computer-Aided Engineering (CAD Control Convergence Differential Geometry Exactly Solvable and Integrable Systems Exploitation Exploration General Mathematics Heuristic methods Humanities and Social Sciences Library and information sciences Linear functions Math. Applications in Chemistry Mathematical analysis Mathematical and Computational Engineering Mathematical Physics Mathematics Metric Geometry Nonlinear Sciences Operator Algebras Optimization Original Article Pattern Formation and Solitons Physics Quantum Physics Searching Spectral Theory Systems Theory Trigonometric functions
Title	A new fusion of salp swarm with sine cosine for optimization of non-linear functions
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