Ponencia
FOM: A Framework for Metaheuristic Optimization
Autor/es | Parejo Maestre, José Antonio
Racero Moreno, Jesús Guerrero López, Fernando Kwok, T. Smith, K. A. |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I |
Fecha de publicación | 2003 |
Fecha de depósito | 2022-07-05 |
Publicado en |
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ISBN/ISSN | 978-3-540-40197-1 0302-9743 |
Resumen | Most metaheuristic approaches for discrete optimization are usually
implemented from scratch. In this paper, we introduce and discuss FOM, an
object-oriented framework for metaheuristic optimization to be used as a
general ... Most metaheuristic approaches for discrete optimization are usually implemented from scratch. In this paper, we introduce and discuss FOM, an object-oriented framework for metaheuristic optimization to be used as a general tool for the development and the implementation of metaheuristic algorithms. The basic idea behind the framework is to separate the problem side from the metaheuristic algorithms, allowing this to reuse different metaheuristic components in different problems. In addition to describing the design and functionality of the framework, we apply it to illustrative examples. Finally, we present our conclusions and discuss futures developments |
Cita | Parejo Maestre, J.A., Racero Moreno, J., Guerrero López, F., Kwok, T. y Smith, K.A. (2003). FOM: A Framework for Metaheuristic Optimization. En ICCS 2003: International Conference on Computational Science (886-895), Melbourne, VIC, Australia: Springer. |
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