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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-018-00696-8