Optimising a shaft's geometry by applying genetic algorithms
Many engnieering design tasks involve optimising several conflicting goals; these types of problem are known as Multiobjective Optimisation Problems (MOPs). Evolutionary techniques have proved to be an effective tool for finding solutions to these MOPs during the last decade, Variations on the basic...
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| Autores principales: | , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2005
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| Materias: | |
| Acceso en línea: | https://dialnet.unirioja.es/servlet/oaiart?codigo=4902430 |
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| Sumario: | Many engnieering design tasks involve optimising several conflicting goals; these types of problem are known as Multiobjective Optimisation Problems (MOPs). Evolutionary techniques have proved to be an effective tool for finding solutions to these MOPs during the last decade, Variations on the basic generic algorithm have been particulary proposed by different researchers for finding rapid optimal solutions to MOPs. The NSGA (Non-dominated Sorting Generic Algorithm) has been implemented in this paper for finding an optimal design for a shaft subjected to cyclic loads, the conflycting goals being minimum weight and minimum lateral deflection. |
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