ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems

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    Abstract

    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 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. © 2006 IEEE.
    Original languageEnglish
    Pages (from-to)50-66
    Number of pages16
    JournalIEEE Transactions on Evolutionary Computation
    Volume10
    Issue number1
    DOIs
    Publication statusPublished - Feb 2006

    Keywords

    • Design and analysis of computer experiments (DACE)
    • Efficient global optimization (EGO)
    • Expensive black-box functions
    • Kriging
    • Landscape approximation
    • Nondominated sorting genetic algorithm II (NSGA-II)
    • Pareto optima
    • Performance assessment
    • Response surfaces
    • Test suites

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