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Computational Intelligence and Neuroscience - Volume 2015 2015, Article ID 967320, 11 pages -

Research Article

Applied Artificial Intelligence Research Centre, Near East University, P.O. Box 670, Lefkosa, Northern Cyprus, Mersin 10, Turkey

Computer and Instructional Technologies Education, Eastern Mediterranean University, Famagusta, Northern Cyprus, Mersin 10, Turkey

Received 26 September 2014; Revised 7 February 2015; Accepted 10 February 2015

Academic Editor: Piotr Franaszczuk

Copyright © 2015 Rahib H. Abiyev and Mustafa Tunay. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel learning algorithm for solving global numerical optimization problems is proposed. The proposedlearning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and iscalled 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 solutionand evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluatesobjective functions using new points, and the search area process determines next hypercube using certain rules and evaluates thenew solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithmis tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functionsof 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate thatthe proposed algorithm is a potential candidate for optimization of both low and high dimensional functions.

Autor: Rahib H. Abiyev and Mustafa Tunay

Fuente: https://www.hindawi.com/


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