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A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming |
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| Affiliation: | 1. CNRS, Université de Montpellier, Botanique et modélisation de l''architecture des plantes et des végétations (AMAP), F-34098 Montpellier, France;2. Evo-Eco-Paléo, UMR 8198 du CNRS, Université de Lille, F-59655 Villeneuve d''Ascq, France;3. Biostratigraphy Group, Exploration Technical Services Department, Saudi Aramco, Dhahran, Saudi Arabia;4. PPP, Département de Géologie, Université de Liège, Allée du 6 Août, B18 Sart Tilman, B4000 Liège, Belgium;1. CNRS, Université de Montpellier, Botanique et modélisation de l''architecture des plantes et des végétations (UMR AMAP), F-34098 Montpellier, France;2. Evo-Eco-Paléo, UMR 8198 du CNRS, Université de Lille, F-59655 Villeneuve d''Ascq, France;3. Biostratigraphy Group, Exploration Technical Services Department, Saudi Aramco, Dhahran, Saudi Arabia;4. PPP, Département de Géologie, Université de Liège, Allée du 6 Août, B18 Sart Tilman, B4000 Liège, Belgium |
| Abstract: | The conventional unconstrained binary quadratic programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. This paper extends the single-objective UBQP to the multiobjective case (mUBQP) where multiple objectives are to be optimized simultaneously. We propose a hybrid metaheuristic which combines an elitist evolutionary multiobjective optimization algorithm and a state-of-the-art single-objective tabu search procedure by using an achievement scalarizing function. Finally, we define a formal model to generate mUBQP instances and validate the performance of the proposed approach in obtaining competitive results on large-size mUBQP instances with two and three objectives. |
| Keywords: | Unconstrained binary quadratic programming Multiobjective combinatorial optimization Hybrid metaheuristic Evolutionary multiobjective optimization Tabu search Scalarizing function |
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