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Towards Optimal Algorithmic Parameters for Simulation-Based Multi-Objective Optimization
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0001-5436-2128
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0003-0111-1776
2016 (engelsk)Inngår i: 2016 IEEE Congress on Evolutionary Computation (CEC), New York: IEEE, 2016, s. 5162-5169Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
New York: IEEE, 2016. s. 5162-5169
Serie
IEEE Congress on Evolutionary Computation
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-13331DOI: 10.1109/CEC.2016.7748344ISI: 000390749105045Scopus ID: 2-s2.0-85008258262ISBN: 978-1-5090-0623-6 (digital)ISBN: 978-1-5090-0622-9 (tryckt)ISBN: 978-1-5090-0624-3 (tryckt)OAI: oai:DiVA.org:his-13331DiVA, id: diva2:1067397
Konferanse
2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, July 24-29, 2016
Tilgjengelig fra: 2017-01-20 Laget: 2017-01-20 Sist oppdatert: 2018-11-07bibliografisk kontrollert
Inngår i avhandling
1. A bilevel approach to parameter tuning of optimization algorithms using evolutionary computing: Understanding optimization algorithms through optimization
Åpne denne publikasjonen i ny fane eller vindu >>A bilevel approach to parameter tuning of optimization algorithms using evolutionary computing: Understanding optimization algorithms through optimization
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Most optimization problems found in the real world cannot be solved using analytical methods. For these types of difficult optimization problems, an alternative approach is needed. Metaheuristics are a category of optimization algorithms that do not guarantee that an optimal solution will be found, but instead search for the best solutions using some general heuristics. Metaheuristics have been shown to be effective at finding “good-enough” solutions to a wide variety of difficult problems. Most metaheuristics involve control parameters that can be used to modify how the heuristics perform its search. This is necessary because different problems may require different search strategies to be solved effectively. The control parameters allow for the optimization algorithm to be adapted to the problem at hand. It is, however, difficult to predict what the optimal control parameters are for any given problem. The problem of finding these optimal control parameter values is known as parameter tuning and is the main topic of this thesis. This thesis uses a bilevel optimization approach to solve parameter tuning problems. In this approach, the parameter tuning problem itself is formulated as an optimization problem and solved with an optimization algorithm. The parameter tuning problem formulated as a bilevel optimization problem is challenging because of nonlinear objective functions, interacting variables, multiple local optima, and noise. However, it is in precisely this kind of difficult optimization problem that evolutionary algorithms, which are a subclass of metaheuristics, have been shown to be effective. That is the motivation for using evolutionary algorithms for the upper-level optimization (i.e. tuning algorithm) of the bilevel optimization approach. Solving the parameter tuning problem using a bilevel optimization approach is also computationally expensive, since a complete optimization run has to be completed for every evaluation of a set of control parameter values. It is therefore important that the tuning algorithm be as efficient as possible, so that the parameter tuning problem can be solved to a satisfactory level with relatively few evaluations. Even so, bilevel optimization experiments can take a long time to run on a single computer. There is, however, considerable parallelization potential in the bilevel optimization approach, since many of the optimizations are independent of one another. This thesis has three primary aims: first, to present a bilevel optimization framework and software architecture for parallel parameter tuning; second, to use this framework and software architecture to evaluate and configure evolutionary algorithms as tuners and compare them with other parameter tuning methods; and, finally, to use parameter tuning experiments to gain new insights into and understanding of how optimization algorithms work and how they can used be to their maximum potential. The proposed framework and software architecture have been implemented and deployed in more than one hundred computers running many thousands of parameter tuning experiments for many millions of optimizations. This illustrates that this design and implementation approach can handle large parameter tuning experiments. Two types of evolutionary algorithms, i.e. differential evolution (DE) and a genetic algorithm (GA), have been evaluated as tuners against the parameter tuning algorithm irace. The as pects of algorithm configuration and noise handling for DE and the GA as related to the parameter tuning problem were also investigated. The results indicate that dynamic resampling strategies outperform static resampling strategies. It was also shown that the GA needs an explicit exploration and exploitation strategy in order not become stuck in local optima. The comparison with irace shows that both DE and the GA can significantly outperform it in a variety of different tuning problems.

sted, utgiver, år, opplag, sider
Skövde: University of Skövde, 2018. s. 210
Serie
Dissertation Series ; 25
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-16368 (URN)978-91-984187-7-4 (ISBN)
Disputas
2018-09-24, ASSAR Industrial Innovation Arena, Skövde, 10:00
Opponent
Veileder
Tilgjengelig fra: 2018-11-15 Laget: 2018-11-07 Sist oppdatert: 2018-11-15bibliografisk kontrollert

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