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Towards Optimal Alg...
Towards Optimal Algorithmic Parameters for Simulation-Based Multi-Objective Optimization
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- Andersson, Martin (author)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering
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- Bandaru, Sunith (author)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering
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- Ng, Amos H. C. (author)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering
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(creator_code:org_t)
- New York : IEEE, 2016
- 2016
- English.
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In: 2016 IEEE Congress on Evolutionary Computation (CEC). - New York : IEEE. - 9781509006236 - 9781509006229 - 9781509006243 ; , s. 5162-5169
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- The use of optimization to solve a simulation-based multi-objective problem produces a set of solutions that provide information about the trade-offs that have to be considered by the decision maker. An incomplete or sub-optimal set of solutions will negatively affect the quality of any subsequent decisions. The parameters that control the search behavior of an optimization algorithm can be used to minimize this risk. However, choosing good parameter settings for a given optimization algorithm and problem combination is difficult. The aim of this paper is to take a step towards optimal parameter settings for optimization of simulation-based problems. Two parameter tuning methods, Latin Hypercube Sampling and Genetic Algorithms, are used to maximize the performance of NSGA-II applied to a simulation-based problem with discrete variables. The strengths and weaknesses of both methods are analyzed. The effect of the number of decision variables and the function budget on the optimal parameter settings is also studied.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Production and Automation Engineering
- Produktion och automatiseringsteknik
Publication and Content Type
- ref (subject category)
- kon (subject category)
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