Gradient-based cuckoo search for global optimization
type: Item
instance of: scholarly article
title: Gradient-based cuckoo search for global optimization
zbMATH Open document ID: 1407.90356
DOI: 10.1155/2014/493740
author: Seif-Eddeen K. Fateen
author: Adrián Bonilla-Petriciolet
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: One of the major advantages of stochastic global optimization methods is the lack of the need of the gradient of the objective function. However, in some cases, this gradient is readily available and can be used to improve the numerical performance of stochastic optimization methods specially the quality and precision of global optimal solution. In this study, we proposed a gradient-based modification to the cuckoo search algorithm, which is a nature-inspired swarm-based stochastic global optimization method. We introduced the gradient-based cuckoo search (GBCS) and evaluated its performance vis-à-vis the original algorithm in solving twenty-four benchmark functions. The use of GBCS improved reliability and effectiveness of the algorithm in all but four of the tested benchmark problems. GBCS proved to be a strong candidate for solving difficult optimization problems, for which the gradient of the objective function is readily available.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 7016577
Wikidata QID: Q59067828
describes a project that uses: MultiMin
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/493740
OpenAlex ID: W1995335159
cites work: A review of recent advances in global optimization
cites work: Derivative-free optimization: a review of algorithms and comparison of software implementations
cites work: Engineering optimisation by cuckoo search
description: scientific article; zbMATH DE number 7016577