A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming

Authors:
Arnaud Liefooghe;Sébastien Verel;Jin-Kao Hao
Affiliations:
-;-;-
Venue:
Applied Soft Computing
Year:
2014

Citing 24
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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.