Recent developed evolutionary algorithms for the multi-objective optimization of design allocation problems
Conference proceedings article
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Author list: Taboada H.A., Coit D.W., Wattanapongsakorn N.
Publisher: Hindawi
Publication year: 2008
Start page: 337
End page: 342
Number of pages: 6
ISBN: 9789810594046
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
This paper presents an overview of a collection of recent developed evolutionary algorithms for solving different types of allocation problems under the consideration of several conflicting objectives. These algorithms are: MOEA-DAP, MOMS-GA and the Multi-Task Multi-State MOEA. MOEA-DAP is a custom multiple objective evolutionary algorithm for solving design allocation problems. MOEA-DAP considers binarystate reliability. In contrast, MOMS-GA, which is a natural extension of MOEA-DAP, works under the assumption that both, the system and its components, experience more than two possible states of performance. The last algorithm presented in the paper is the Multi-Task Multi-State MOEA, which is a multiple objective algorithm designed to determine optimal configurations of multi-state, multi-task production systems based on availability analysis. These three algorithms are novel approaches that offer distinct advantages to current existing MOEAs. copy; 2008 ICQR.
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