A decomposition-based binary ACO algorithm for the multiobjective UBQP

The multiobjective unconstrained binary quadratic programming (mUBQP) is a combinatorial optimization problem which is able to represent several multiobjective optimization problems (MOPs). The problem can be characterized by the number of variables, the number of objectives and the objective correlation strength. Multiobjective evolutionary algorithms (MOEAs) are known as an efficient technique for solving MOPs. Moreover, several recent studies have shown the effectiveness of the MOEA/D framework applied to different MOPs. Previously, we have presented a preliminary study on an algorithm based on MOEA/D framework and the bio-inspired metaheuristic called binary ant colony optimization (BACO). The metaheuristic uses a positive feedback mechanism according to the best solutions found so far to update a probabilistic model which maintains the learned information. This paper presents the improved MOEA/D-BACO framework for solving the mUBQP. The components (i) mutation-like effect, and (ii) diversity preserving method are incorporated into the framework to enhance its search ability avoiding the premature convergence of the model and consequently maintaining a more diverse population of solutions. Experimental studies were conducted on a set of mUBQP instances. The results have shown that the proposed MOEA/D-BACO has outperformed MOEA/D, which uses genetic operators, in most of the test instances. Moreover, the algorithm has produced competitive results in comparison to the best approximated Pareto fronts from the literature.