Showing papers by "Daniel Rodriguez published in 2020"
TL;DR: The merge nondominated sorting (MNDS) algorithm proposed in this paper has a best computational complexity of O(Nlog N) and a worst computational complexity O(MN²), with N being the population size and M being the number of objectives.
Abstract: Many Pareto-based multiobjective evolutionary algorithms require ranking the solutions of the population in each iteration according to the dominance principle, which can become a costly operation particularly in the case of dealing with many-objective optimization problems In this article, we present a new efficient algorithm for computing the nondominated sorting procedure, called merge nondominated sorting (MNDS), which has a best computational complexity of O(Nlog N) and a worst computational complexity of O(MN²), with N being the population size and M being the number of objectives Our approach is based on the computation of the dominance set, that is, for each solution, the set of solutions that dominate it, by taking advantage of the characteristics of the merge sort algorithm We compare MNDS against six well-known techniques that can be considered as the state-of-the-art The results indicate that the MNDS algorithm outperforms the other techniques in terms of the number of comparisons as well as the total running time
8 citations