Three-Dimensional Layers of Maxima
Algorithmica
Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods
Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods
Approximating the least hypervolume contributor: NP-hard in general, but fast in practice
Theoretical Computer Science
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
On beam search for multicriteria combinatorial optimization problems
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Convergence of set-based multi-objective optimization, indicators and deteriorative cycles
Theoretical Computer Science
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The @e-indicator subset selection selects a subset of a nondominated point set that is as close as possible to a reference point set with respect to the @e-indicator. This selection procedure is used by population-based heuristic approaches for multiobjective optimization problems. Given that this procedure is called very often during the run of the heuristic approach, efficient ways of computing the optimal subset are strongly required. In this note, we give a correctness proof of the @e-indicator subset selection algorithm proposed by Ponte et al. (2012) [1] for the bidimensional case as well as several algorithmic improvements in terms of time complexity. Extensions to larger dimension are also discussed.