Section: New Results
A Hybrid Metaheuristic for Multiobjective Unconstrained Binary Quadratic Programming
Participant : Arnaud Liefooghe
External participants : Jin-Kao Hao (Univ. Angers, France), Sébastien Verel (Univ. Littoral Côte d'Opale, France)
The conventional Unconstrained Binary Quadratic Programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. In  , we extend 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.