Volume 10 Number 3
June 2013
Article Contents
An Optimization Algorithm Employing Multiple Metamodels and Optimizers. International Journal of Automation and Computing, vol. 10, no. 3, pp. 227-241, 2013. doi: 10.1007/s11633-013-0716-y
Cite as: An Optimization Algorithm Employing Multiple Metamodels and Optimizers. International Journal of Automation and Computing, vol. 10, no. 3, pp. 227-241, 2013. doi: 10.1007/s11633-013-0716-y

An Optimization Algorithm Employing Multiple Metamodels and Optimizers

  • Received: 2011-10-17
  • 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.
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An Optimization Algorithm Employing Multiple Metamodels and Optimizers

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.

An Optimization Algorithm Employing Multiple Metamodels and Optimizers. International Journal of Automation and Computing, vol. 10, no. 3, pp. 227-241, 2013. doi: 10.1007/s11633-013-0716-y
Citation: An Optimization Algorithm Employing Multiple Metamodels and Optimizers. International Journal of Automation and Computing, vol. 10, no. 3, pp. 227-241, 2013. doi: 10.1007/s11633-013-0716-y
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