Abstract
This paper proposes a decomposition-based path-relinking method for multi- and many-objective combinatorial optimization problems. The proposed approach, referred to as MOPR/D, considers the localization of solutions in the objective space and can handle this information to make choices regarding intermediate solutions in the path-relinking trajectory. Because it does not require aggregation functions, it does not suffer from a tendency to introduce search bias to specific regions of the Pareto front. The proposed approach was compared with seven path-relinking techniques from the literature on three differently structured combinatorial optimization problems: 0/1 multidimensional knapsack, quadratic assignment, and spanning tree. Experiments were conducted on sets of benchmark instances with up to five objectives. This study also reviewed multi-objective path generation strategies, investigated their behaviors, and a taxonomy is proposed for standardizing and classifying them.
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