Gradient-Based Cuckoo Search for Global Optimization
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 qu...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2014/493740 |
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doaj-37b0ee0da1844bf7bb684385a26c283e2020-11-24T22:49:12ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472014-01-01201410.1155/2014/493740493740Gradient-Based Cuckoo Search for Global OptimizationSeif-Eddeen K. Fateen0Adrián Bonilla-Petriciolet1Department of Chemical Engineering, Cairo University, Giza 12316, EgyptDepartment of Chemical Engineering, Aguascalientes Institute of Technology, 20256 Aguascalientes, AGS, MexicoOne 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.http://dx.doi.org/10.1155/2014/493740 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Seif-Eddeen K. Fateen Adrián Bonilla-Petriciolet |
spellingShingle |
Seif-Eddeen K. Fateen Adrián Bonilla-Petriciolet Gradient-Based Cuckoo Search for Global Optimization Mathematical Problems in Engineering |
author_facet |
Seif-Eddeen K. Fateen Adrián Bonilla-Petriciolet |
author_sort |
Seif-Eddeen K. Fateen |
title |
Gradient-Based Cuckoo Search for Global Optimization |
title_short |
Gradient-Based Cuckoo Search for Global Optimization |
title_full |
Gradient-Based Cuckoo Search for Global Optimization |
title_fullStr |
Gradient-Based Cuckoo Search for Global Optimization |
title_full_unstemmed |
Gradient-Based Cuckoo Search for Global Optimization |
title_sort |
gradient-based cuckoo search for global optimization |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2014-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2014/493740 |
work_keys_str_mv |
AT seifeddeenkfateen gradientbasedcuckoosearchforglobaloptimization AT adrianbonillapetriciolet gradientbasedcuckoosearchforglobaloptimization |
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1725676814405206016 |