Abstract
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. © Springer-Verlag Berlin Heidelberg 2003.
Cite
CITATION STYLE
Parejo, J. A., Racero, J., Guerrero, F., Kwok, T., & Smith, K. A. (2003). FOM: A framework for metaheuristic optimization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2660, 886–895. https://doi.org/10.1007/3-540-44864-0_91
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