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Neural Computing and Applications

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27.04.2016 | Original Article

\(\beta\)-Hill climbing: an exploratory local search

verfasst von: Mohammed Azmi Al-Betar

Erschienen in: Neural Computing and Applications | Sonderheft 1/2017

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Abstract

Hill climbing method is an optimization technique that is able to build a search trajectory in the search space until reaching the local optima. It only accepts the uphill movement which leads it to easily get stuck in local optima. Several extensions to hill climbing have been proposed to overcome such problem such as Simulated Annealing, Tabu Search. In this paper, an extension version of hill climbing method has been proposed and called \(\beta\)-hill climbing. A stochastic operator called \(\beta\)-operator is utilized in hill climbing to control the balance between the exploration and exploitation during the search. The proposed method has been evaluated using IEEE-CEC2005 global optimization functions. The results show that the proposed method is a very efficient enhancement to the hill climbing providing powerful results when it compares with other advanced methods using the same global optimization functions.

Vorheriger Artikel A path cost-based GRASP for minimum independent dominating set problem

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Titel: -Hill climbing: an exploratory local search
verfasst von: Mohammed Azmi Al-Betar
Publikationsdatum: 27.04.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2328-2

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