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A systematic comparison of performance of various multiple objective metaheuristics using a common set of analytical test functions

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Collette Y. , Siarry P. , Wong H.-I.

tom Vol. 25, No. 4

249-271

Many multiple objective optimization algorithms have been described in the literature. Some of them use a "metaheuristic" (genetic algorithm, simulated annealing, tabu search and so on) that allow, in principle, to avoid getting trapped into a local minimum of an objective function. We feel that this approach can be advantageously extended to a large set of multiple objective optimization methods. Moreover, it is interesting to perform a systematic comparison between performances of various multiple objective metaheuristics. Such a comparison needs, on the one hand, to adopt a common set of test functions and, on the other hand, to use a common set of performance criteria. In this study, we propose to compare various metaheuristics associated with various multiple objective optimization methods (such as weighted sum of objective functions, goal programming, distance method and so on). These different couples are evaluated using a set of classical test functions. The set of test functions is chosen so as to represent most of the difficulties (multifrontality, discontinuity, non-convexity and so on) that can be met in engineering when handling real multiple objective optimization problems.