FOM: a framework for metaheuristic optimization

Authors:
J. A. Parejo;J. Racero;F. Guerrero;T. Kwok;K. A. Smith
Affiliations:
Escuela Superior de Ingenieros, Camino de los Descubrimientos, Sevilla, Spain;Escuela Superior de Ingenieros, Camino de los Descubrimientos, Sevilla, Spain;Escuela Superior de Ingenieros, Camino de los Descubrimientos, Sevilla, Spain;School of Business Systems, Monash University, Clayton, Vic, Australia;School of Business Systems, Monash University, Clayton, Vic, Australia
Venue:
ICCS'03 Proceedings of the 2003 international conference on Computational science
Year:
2003

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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.