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ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
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DOI：10.1109/TEVC.2005.851274.png)
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This paper concerns multiobjective optimization in scenarios where each solution evaluation is financially and/or temporally expensive. We make use of nine relatively low-dimensional, nonpathological, real-valued functions, such as arise in many applications, and assess the performance of two algorithms after just 100 and 250 (or 260) function evaluations. The results show that NSGA-II, a popular multiobjective evolutionary algorithm, performs well compared with random search, even within the restricted number of evaluations used. A significantly better performance (particularly, in the worst case) is, however, achieved on our test set by an algorithm proposed herein-ParEGO-which is an extension of the single-objective efficient global optimization (EGO) algorithm of Jones et al. ParEGO uses a design-of-experiments inspired initialization procedure and learns a Gaussian processes model of the search landscape, which is updated after every function evaluation. Overall, ParEGO exhibits a promising performance for multiobjective optimization problems where evaluations are expensive or otherwise restricted in number.
Keyword:
design and analysis of computer experiments (DACE)
efficient global optimization (EGO)
expensive black-box functions
Kriging
landscape approximation
metamodels
multiobjective optimization
nondominated sorting genetic algorithm II (NSGA-II)
Pareto optima
performance assessment
response surfaces
test suites
期刊
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12
论文数:
1.9K
被引数:
2.4W
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