A Smart-Distributed Pareto Front Using ev-MOGA Evolutionary Algorithm
[EN] Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the nor- malized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC opt...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/144562 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/144562 |
| Access Level: | acceso abierto |
| Palavra-chave: | Multi-objective optimization Pareto front Engineering design Evolutionary algorithms Multi-objective evolutionary algorithms INGENIERIA DE SISTEMAS Y AUTOMATICA |
| Resumo: | [EN] Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the nor- malized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOGA) is pre- sented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes. |
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