ParEGO extensions for multi-objective optimization of expensive evaluation functions
| Autor: | Franck Plestan, Guillermo Pita-Gil, Saïd Moussaoui, Joan Davins-Valldaura |
|---|---|
| Přispěvatelé: | Technocentre Renault [Guyancourt], RENAULT, Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), École Centrale de Nantes (ECN), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
021103 operations research Control and Optimization Applied Mathematics 0211 other engineering and technologies Pareto principle Evolutionary algorithm 02 engineering and technology Management Science and Operations Research Multi-objective optimization Computer Science Applications [SPI.AUTO]Engineering Sciences [physics]/Automatic Reduction (complexity) Surrogate model Approximation error 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] Global optimization [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Mathematics |
| Zdroj: | Journal of Global Optimization Journal of Global Optimization, Springer Verlag, 2017, 17 (1), pp.79-96. ⟨10.1007/s10898-016-0419-3⟩ |
| ISSN: | 0925-5001 1573-2916 |
| DOI: | 10.1007/s10898-016-0419-3⟩ |
| Popis: | International audience; This paper deals with multi-objective optimization in the case of expensive objective functions. Such a problem arises frequently in engineering applications where the main purpose is to find a set of optimal solutions in a limited global processing time. Several algorithms use linearly combined criteria to use directly mono-objective algorithms. Nevertheless, other algorithms, such as multi-objective evolutionary algorithm (MOEA) and model-based algorithms, propose a strategy based on Pareto dominance to optimize efficiently all criteria. A widely used model-based algorithm for multi-objective optimization is Pareto efficient global optimization (ParEGO). It combines linearly the objective functions with several random weights and maximizes the expected improvement (EI) criterion. However, this algorithm tends to favor parameter values suitable for the reduction of the surrogate model error, rather than finding non-dominated solutions. The contribution of this article is to propose an extension of the ParEGO algorithm for finding the Pareto Front by introducing a double Kriging strategy. Such an innovation allows to calculate a modified EI criterion that jointly accounts for the objective function approximation error and the probability to find Pareto Set solutions. The main feature of the resulting algorithm is to enhance the convergence speed and thus to reduce the total number of function evaluations. This new algorithm is compared against ParEGO and several MOEA algorithms on a standard benchmark problems. Finally, an automotive engineering problem allowing to illustrate the applicability of the proposed approach is given as an example of a real application: the parameter setting of an indirect tire pressure monitoring system. |
| Databáze: | OpenAIRE |
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