Proceedings of the Genetic and Evolutionary Computation Conference 2021
DOI: 10.1145/3449639.3459360
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On the design and anytime performance of indicator-based branch and bound for multi-objective combinatorial optimization

Abstract: In this article, we propose an indicator-based branch and bound (I-BB) approach for multi-objective combinatorial optimization that uses a best-first search strategy. In particular, assuming maximizing objectives, the next node to be processed is chosen with respect to the quality of its upper bound. This quality is given by a binary quality indicator, such as the binary hypervolume or the -indicator, with respect to the archive of solutions maintained by the branch and bound algorithm. Although the I-BB will … Show more

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