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Aggregate meta-models for evolutionary multiobjective and many-objective optimization
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DOI：10.1016/j.neucom.2012.06.043.png)
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Evolutionary algorithms are among the best multiobjective optimizers. However, they need a large number of function evaluations. In this paper a meta-model based approach to the reduction in the needed number of function evaluations is presented. Local aggregate meta-models are used in a memetic operator. The algorithm is first discussed from a theoretical point of view and then it is shown that the meta-models greatly reduce the number of function evaluations. The approach is compared to a similar one with a single global meta-model as well as to more traditional NSGA-II and epsilon-IBEA. Moreover, it is shown that aggregate meta-models work even for a larger number of objectives and therefore should be considered when designing many-objective evolutionary algorithms. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Evolutionary algorithms
Multiobjective optimization
Many-objective optimization
Surrogate models
Meta-models
Memetic algorithm
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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