2026-06-01T23:50:52Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/235012026-02-03T11:28:15Zcom_10630_2254col_10630_37953

Effective anytime algorithm for multiobjective combinatorial optimization problems Domínguez-Ríos, Miguel Ángel Alba-Torres, Enrique Chicano-García, José-Francisco Algoritmos In multiobjective optimization, the result of an optimization algorithm is a set of efficient solutions from which the decision maker selects one. It is common that not all the efficient solutions can be computed in a short time and the search algorithm has to be stopped prematurely to analyze the solutions found so far. A set of efficient solutions that are well-spread in the objective space is preferred to provide the decision maker with a great variety of solutions. However, just a few exact algorithms in the literature exist with the ability to provide such a well-spread set of solutions at any moment: we call them anytime algorithms. We propose a new exact anytime algorithm for multiobjective combinatorial optimization combining three novel ideas to enhance the anytime behavior. We compare the proposed algorithm with those in the state-of-the-art for anytime multiobjective combinatorial optimization using a set of 480 instances from different well-known benchmarks and four different performance measures: the overall non-dominated vector generation ratio, the hypervolume, the general spread and the additive epsilon indicator. A comprehensive experimental study reveals that our proposal outperforms the previous algorithms in most of the instances. 2021-12-21T12:41:35Z 2021-12-21T12:41:35Z 2021-12-09 2021-07 journal article Miguel Ángel Domínguez-Ríos, Francisco Chicano, Enrique Alba, "Effective anytime algorithm for multiobjective combinatorial optimization problems", Information Sciences 565: 210-228 (2021). https://hdl.handle.net/10630/23501 https://doi.org/10.1016/j.ins.2021.02.074. eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Atribución 4.0 Internacional Elsevier