An algorithm for computationally expensive engineering optimization problems
| Autor: | Tenne Yoel |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Optimization problem
Optimization algorithm business.industry Computer science media_common.quotation_subject Evolutionary algorithm Computational intelligence Machine learning computer.software_genre Computer Science Applications Theoretical Computer Science Engineering optimization Set (abstract data type) Control and Systems Engineering Modeling and Simulation Artificial intelligence Data mining Function (engineering) business Engineering design process Algorithm computer Information Systems media_common |
| Zdroj: | International Journal of General Systems. 42:458-488 |
| ISSN: | 1563-5104 0308-1079 |
| DOI: | 10.1080/03081079.2013.775128 |
| Popis: | Modern engineering design often relies on computer simulations to evaluate candidate designs, a scenario which results in an optimization of a computationally expensive black-box function. In these settings, there will often exist candidate designs which cause the simulation to fail, and can therefore degrade the search effectiveness. To address this issue, this paper proposes a new metamodel-assisted computational intelligence optimization algorithm which incorporates classifiers into the optimization search. The classifiers predict which candidate designs are expected to cause the simulation to fail, and this prediction is used to bias the search towards designs predicted to be valid. To enhance the search effectiveness, the proposed algorithm uses an ensemble approach which concurrently employs several metamodels and classifiers. A rigorous performance analysis based on a set of simulation-driven design optimization problems shows the effectiveness of the proposed algorithm. |
| Databáze: | OpenAIRE |
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