Journal article

FOM: A framework for metaheuristic optimization

JA Parejo, J Racero, F Guerrero, T Kwok, KA Smith, PMA Sloot (ed.), D Abramson (ed.), AV Bogdanov (ed.), JJ Dongarra (ed.), AY Zomaya (ed.), YE Gorbachev (ed.)

COMPUTATIONAL SCIENCE - ICCS 2003, PT IV, PROCEEDINGS | SPRINGER-VERLAG BERLIN | Published : 2003

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..

View full abstract

University of Melbourne Researchers