SCA: A Sine Cosine Algorithm for solving optimization problems

This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based...

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Vydáno v:Knowledge-based systems Ročník 96; s. 120 - 133
Hlavní autor: Mirjalili, Seyedali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 15.03.2016
Témata:
Algorithms
Constrained optimization
Exploitation
Mathematical analysis
Mathematical models
Meta-heuristic
Optimization
Performance measurement
Population-based algorithm
Searching
Stochastic optimization
Trigonometric functions
Meta-heuristic
Stochastic optimization
Optimization
Constrained optimization
Population-based algorithm
ISSN:0950-7051, 1872-7409
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Shrnutí:This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2015.12.022