Optimization of High-Dimensional Functions through Hypercube Evaluation

A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm compri...

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Main Authors: Rahib H. Abiyev, Mustafa Tunay
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Computational Intelligence and Neuroscience
Online Access:http://dx.doi.org/10.1155/2015/967320
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spelling doaj-cff351c5866c4d19ada69e8b2cb13a402020-11-24T21:12:09ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732015-01-01201510.1155/2015/967320967320Optimization of High-Dimensional Functions through Hypercube EvaluationRahib H. Abiyev0Mustafa Tunay1Applied Artificial Intelligence Research Centre, Near East University, P.O. Box 670, Lefkosa, Northern Cyprus, Mersin 10, TurkeyComputer and Instructional Technologies Education, Eastern Mediterranean University, Famagusta, Northern Cyprus, Mersin 10, TurkeyA novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the initialization and evaluation process, displacement-shrink process, and searching space process. The initialization and evaluation process initializes initial solution and evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluates objective functions using new points, and the search area process determines next hypercube using certain rules and evaluates the new solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithm is tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functions of 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low and high dimensional functions.http://dx.doi.org/10.1155/2015/967320
collection DOAJ
language English
format Article
sources DOAJ
author Rahib H. Abiyev
Mustafa Tunay
spellingShingle Rahib H. Abiyev
Mustafa Tunay
Optimization of High-Dimensional Functions through Hypercube Evaluation
Computational Intelligence and Neuroscience
author_facet Rahib H. Abiyev
Mustafa Tunay
author_sort Rahib H. Abiyev
title Optimization of High-Dimensional Functions through Hypercube Evaluation
title_short Optimization of High-Dimensional Functions through Hypercube Evaluation
title_full Optimization of High-Dimensional Functions through Hypercube Evaluation
title_fullStr Optimization of High-Dimensional Functions through Hypercube Evaluation
title_full_unstemmed Optimization of High-Dimensional Functions through Hypercube Evaluation
title_sort optimization of high-dimensional functions through hypercube evaluation
publisher Hindawi Limited
series Computational Intelligence and Neuroscience
issn 1687-5265
1687-5273
publishDate 2015-01-01
description A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the initialization and evaluation process, displacement-shrink process, and searching space process. The initialization and evaluation process initializes initial solution and evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluates objective functions using new points, and the search area process determines next hypercube using certain rules and evaluates the new solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithm is tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functions of 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low and high dimensional functions.
url http://dx.doi.org/10.1155/2015/967320
work_keys_str_mv AT rahibhabiyev optimizationofhighdimensionalfunctionsthroughhypercubeevaluation
AT mustafatunay optimizationofhighdimensionalfunctionsthroughhypercubeevaluation
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