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• W2023242330 abstract "Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach." @default.
• W2023242330 created "2016-06-24" @default.
• W2023242330 creator A5035132865 @default.
• W2023242330 date "2013-06-01" @default.
• W2023242330 modified "2023-09-23" @default.
• W2023242330 title "An Optimization Algorithm Employing Multiple Metamodels and Optimizers" @default.
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• W2023242330 doi "https://doi.org/10.1007/s11633-013-0716-y" @default.
• W2023242330 hasPublicationYear "2013" @default.
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