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Simulation-based op...
Simulation-based optimisation using local search and neural network metamodels
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- Persson, Anna (författare)
- Högskolan i Skövde,Institutionen för teknik och samhälle
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- Grimm, Henrik (författare)
- Högskolan i Skövde,Institutionen för teknik och samhälle
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- Ng, Amos (författare)
- Högskolan i Skövde,Institutionen för teknik och samhälle
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(creator_code:org_t)
- Anaheim : ACTA Press, 2006
- 2006
- Engelska.
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Ingår i: Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2006. - Anaheim : ACTA Press. - 0889866104 - 9780889866102 ; , s. 178-183
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Abstract
Ämnesord
Stäng
- 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.
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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