Constrained multi-objective aerodynamic shape optimization via swarm intelligence
In this paper, we present a Multi-objective Particle Swarm Optimizer (MOPSO) based on a decomposition approach, which is proposed to solve Constrained Multi-Objective Aerodynamic Shape Optimization Problems (CMO-ASOPs). The constraint-handling technique adopted in this approach is based on the well-...
| Autores: | , , |
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| Formato: | capítulo de livro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | México |
| Recursos: | Universidad Autónoma Metropolitana |
| Repositorio: | Concentración de Recursos de Información Científica y Académica, UAM Cuajimalpa |
| Idioma: | inglés |
| OAI Identifier: | oai:ilitia.cua.uam.mx:123456789/476 |
| Acesso em linha: | http://ilitia.cua.uam.mx:8080/jspui/handle/123456789/476 |
| Access Level: | acceso abierto |
| Palavra-chave: | info:eu-repo/classification/cti/7 Inteligencia de enjambre Optimización matemática Inteligencia computacional |
| Resumo: | In this paper, we present a Multi-objective Particle Swarm Optimizer (MOPSO) based on a decomposition approach, which is proposed to solve Constrained Multi-Objective Aerodynamic Shape Optimization Problems (CMO-ASOPs). The constraint-handling technique adopted in this approach is based on the well-known epsilon-constraint method. Since the ε-constraint method was initially proposed to deal with constrained single-objective optimization Problems, we adapted it so that it could be incorporated into a MOPSO. Our main focus is to solve CMO-ASOPs in an efficient and effective manner. The proposed constrained MOPSO guides the search by updating the position of each particle using a set of solutions considered as the global best according to both the decomposition approach and the epsilon-constraint method. Our preliminary results indicate that our proposed approach is able to outperform a state-of-the-art MOEA in several CMO-ASOPs. |
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