Journal
APPLIED SOFT COMPUTING
Volume 16, Issue -, Pages 10-19Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2013.11.008
Keywords
Unconstrained binary quadratic programming; Multiobjective combinatorial optimization; Hybrid metaheuristic; Evolutionary multiobjective optimization; Tabu search; Scalarizing functiona
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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 mUBQ Pinstances with two and three objectives. (C) 2013 Elsevier B. V. All rights reserved.
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