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A parameter-tuning framework for metaheuristics based on design of experiments and artificial neural networks
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.ORCID iD: 0000-0001-9372-3416
2010 (English)In: World Academy of Science, Engineering and Technology: An International Journal of Science, Engineering and Technology, ISSN 2010-376X, E-ISSN 2070-3740, Vol. 64, 213-216 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, a framework for the simplification and standardization of metaheuristic related parameter-tuning by applying a four phase methodology, utilizing Design of Experiments and Artificial Neural Networks, is presented. Metaheuristics are multipurpose problem solvers that are utilized on computational optimization problems for which no efficient problem specific algorithm exist. Their successful application to concrete problems requires the finding of a good initial parameter setting, which is a tedious and time consuming task. Recent research reveals the lack of approach when it comes to this so called parameter-tuning process. In the majority of publications, researchers do have a weak motivation for their respective choices, if any. Because initial parameter settings have a significant impact on the solutions quality, this course of action could lead to suboptimal experimental results, and thereby a fraudulent basis for the drawing of conclusions.

Place, publisher, year, edition, pages
2010. Vol. 64, 213-216 p.
Keyword [en]
Artificial neural networks; Design of experiments; Metaheuristics; Parameter-tuning
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-28880Scopus ID: 2-s2.0-78651532490OAI: oai:DiVA.org:miun-28880DiVA: diva2:973825
Available from: 2016-09-22 Created: 2016-09-22 Last updated: 2016-09-22Bibliographically approved

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