Simulation-based optimisation using local search and neural network metamodels
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 (Refereed)
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.
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
IdentifiersURN: urn:nbn:se:his:diva-7329ScopusID: 2-s2.0-56149111515ISBN: 0889866104ISBN: 9780889866102OAI: oai:DiVA.org:his-7329DiVA: diva2:608162
10th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2006, 28 August 2006 through 30 August 2006, Palma de Mallorca
Sponsors: Int. Assoc. Science and Technology for Development (IASTED); Technical Committee on Artificial Intelligence and Expert Systems; Technical Committee on Soft Computing2013-02-262013-02-262015-12-21Bibliographically approved