Gradient-based optimizer: A new metaheuristic optimization algorithm

•The gradient-based optimizer (GBO) inspired by the gradient-based Newton’s search method.•The GBO uses two main operators: gradient search rule (GSR) and local escaping operator (LEO).•The exploration and exploitation capabilities of GBO is assessed by 28 mathematical functions.•The results on the...

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Bibliographic Details
Published in:Information sciences Vol. 540; pp. 131 - 159
Main Authors: Ahmadianfar, Iman, Bozorg-Haddad, Omid, Chu, Xuefeng
Format: Journal Article
Language:English
Published: Elsevier Inc 01.11.2020
Subjects:
Constrained optimization problem
Gradient-based method
Metaheuristic algorithm
Optimization
Gradient-based method
Metaheuristic algorithm
Constrained optimization problem
Optimization
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:•The gradient-based optimizer (GBO) inspired by the gradient-based Newton’s search method.•The GBO uses two main operators: gradient search rule (GSR) and local escaping operator (LEO).•The exploration and exploitation capabilities of GBO is assessed by 28 mathematical functions.•The results on the mathematical test functions demonstrate the high competitiveness of GBO algorithm.•The results on engineering optimization problems prove the suitable performance of the GBO to optimize practical problems. In this study, a novel metaheuristic optimization algorithm, gradient-based optimizer (GBO) is proposed. The GBO, inspired by the gradient-based Newton’s method, uses two main operators: gradient search rule (GSR) and local escaping operator (LEO) and a set of vectors to explore the search space. The GSR employs the gradient-based method to enhance the exploration tendency and accelerate the convergence rate to achieve better positions in the search space. The LEO enables the proposed GBO to escape from local optima. The performance of the new algorithm was evaluated in two phases. 28 mathematical test functions were first used to evaluate various characteristics of the GBO, and then six engineering problems were optimized by the GBO. In the first phase, the GBO was compared with five existing optimization algorithms, indicating that the GBO yielded very promising results due to its enhanced capabilities of exploration, exploitation, convergence, and effective avoidance of local optima. The second phase also demonstrated the superior performance of the GBO in solving complex real-world engineering problems. Source codes of the GBO algorithm are publicly available at http://imanahmadianfar.com/codes/.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2020.06.037