SSC: A hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications

Chimp Optimization Algorithm (ChoA) is a recently developed meta-heuristic approach which is inspired by the individual intelligence and sexual motivation of chimps. It is designed for trapping the local optima to alleviate the slow convergence speed. In this paper, a hybrid algorithm is developed w...

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Vydané v:Knowledge-based systems Ročník 222; s. 106926
Hlavný autor: Dhiman, Gaurav
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 21.06.2021
Elsevier Science Ltd
Predmet:
Algorithms
Chimp Optimization Algorithm (choA)
Compression
Compression springs
Convergence
Crashworthiness
Effectiveness
Efficiency
Engineering
Engineering design
Exploitation
Gear trains
Heuristic
Heuristic methods
Impact strength
Intelligence
Meta-heuristics
Optimization
Optimization algorithms
Pressure vessel design
Pressure vessels
Primates
Robustness
Sexual motivation
Sine–cosine
Spotted Hyena Optimizer (SHO)
Swarm-intelligence
Trigonometric functions
Swarm-intelligence
Engineering design
Chimp Optimization Algorithm (choA)
Spotted Hyena Optimizer (SHO)
Sine–cosine
Optimization
Meta-heuristics
ISSN:0950-7051, 1872-7409
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Popis
Shrnutí:Chimp Optimization Algorithm (ChoA) is a recently developed meta-heuristic approach which is inspired by the individual intelligence and sexual motivation of chimps. It is designed for trapping the local optima to alleviate the slow convergence speed. In this paper, a hybrid algorithm is developed which is based on the sine–cosine functions and attacking strategy of Spotted Hyena Optimizer (SHO). This hybrid algorithm is termed as Sine–cosine and Spotted Hyena-based Chimp Optimization Algorithm (SSC). This algorithm is used to find the best optimal solutions of real-life complex problems. The sine–cosine and attacking strategy of SHO algorithm is responsible for better exploration and exploitation. These strategies are applied to update the equations of chimps during the searching process to overcome the drawbacks of the ChoA algorithm such as slow convergence and local minima. Experimental results based on IEEE CEC’17 and six real-life engineering problems such as welded beam design, tension/compression spring design, pressure vessel design, multiple disk clutch brake design, gear train design, and car side crashworthiness, demonstrate the robustness, effectiveness, efficiency, and convergence analysis of the proposed SSC algorithm in comparison with other competitor approaches. Note that the source codes are available at http://www.dhimangaurav.com.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.106926