Weighted stress function method for multiobjective evolutionary algorithm based on decomposition
Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a well established state-of-the-art framework. Major concerns that must be addressed when applying MOEA/D are the choice of an appropriate scalarizing function and setting the values of main control parameters. This study sugge...
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| Formato: | conferencePaper |
| Idioma: | eng |
| Publicado em: |
2017
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| Texto completo: | http://hdl.handle.net/1822/53106 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/53106 |
| Resumo: | Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a well established state-of-the-art framework. Major concerns that must be addressed when applying MOEA/D are the choice of an appropriate scalarizing function and setting the values of main control parameters. This study suggests a weighted stress function method (WSFM) for fitness assignment in MOEA/D. WSFM establishes analogy between the stress-strain behavior of thermoplastic vulcanizates and scalarization of a multiobjective optimization problem. The experimental results suggest that the proposed approach is able to provide a faster convergence and a better performance of final approximation sets with respect to quality indicators when compared with traditional methods. The validity of the proposed approach is also demonstrated on engineering problems. |
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