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

An Optimization Algorithm Employing Multiple Metamodels and Optimizers

  • 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.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return