A direct search algorithm for global optimization
type: Item
instance of: scholarly article
title: A direct search algorithm for global optimization
zbMATH Open document ID: 1461.90169
author: Enrique Baeyens
author: Alberto Herreros
author: José R. Perán
published in: Algorithms
publication date: 2019-03-26T00:00:00Z
review text: Summary: A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm's performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. The results of the computational study show that the algorithm combines both simplicity and efficiency and is competitive with the heuristics-based strategies presently used for global optimization.
Mathematics Subject Classification ID: 90C56
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 7042349
zbMATH Keywords: global optimization
zbMATH Keywords: direct search methods
zbMATH Keywords: search space transformation
zbMATH Keywords: derivative-free optimization
zbMATH Keywords: heuristics-based optimization
describes a project that uses: Matlab
describes a project that uses: Tabu search
describes a project that uses: fminsearch
describes a project that uses: MultiMin
describes a project that uses: DeMat
describes a project that uses: DIRECT
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a9020040
OpenAlex ID: W2423494995
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DOI: 10.3390/A9020040
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