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Simulation-based optimisation using local search and neural network metamodels
University of Skövde, School of Technology and Society.ORCID iD: 0000-0003-3973-3394
University of Skövde, School of Technology and Society.
University of Skövde, School of Technology and Society.ORCID iD: 0000-0003-0111-1776
2006 (English)In: Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2006 / [ed] Angel Pasqual del Pobil, Anaheim: ACTA Press, 2006, 178-183 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a new algorithm for enhancing the efficiency of simulation-based optimisation using local search and neural network metamodels. The local search strategy is based on steepest ascent Hill Climbing. In contrast to many other approaches that use a metamodel for simulation optimisation, this algorithm alternates between the metamodel and its underlying simulation model, rather than using them sequentially. On-line learning of the metamodel is applied to improve its accuracy in the current region of the search space. The proposed algorithm is applied to a theoretical benchmark problem as well as a real-world manufacturing optimisation problem and initial results show good performance when compared to a standard Hill Climbing strategy.

Place, publisher, year, edition, pages
Anaheim: ACTA Press, 2006. 178-183 p.
Keyword [en]
Local search, Metamodel, Neural network, Optimisation, Simulation, Artificial intelligence, Channel capacity, Neural networks, Soft computing, Benchmark problems, Local searches, New algorithms, On-line learnings, Search spaces, Simulation models, Steepest ascents, To many, Image classification
Identifiers
URN: urn:nbn:se:his:diva-7329Scopus ID: 2-s2.0-56149111515ISBN: 0889866104 ISBN: 9780889866102 OAI: oai:DiVA.org:his-7329DiVA: diva2:608162
Conference
10th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2006, 28 August 2006 through 30 August 2006, Palma de Mallorca
Note

Sponsors: Int. Assoc. Science and Technology for Development (IASTED); Technical Committee on Artificial Intelligence and Expert Systems; Technical Committee on Soft Computing

Available from: 2013-02-26 Created: 2013-02-26 Last updated: 2015-12-21Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
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Output format
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