Reyes-Fernández-de-Bulnes, DarianBolufé-Röhler, AntonioTamayo-Vera, Dania2021-05-252021-05-252019-05-03GECONTEC: revista Internacional de Gestión del Conocimiento y la Tecnología, ISSN-e 2255-5684, Vol. 7, Nº. 2, 2019, págs. 1-192255-5684http://hdl.handle.net/10433/10338URL del artículo en la web de la Revista: https://www.upo.es/revistas/index.php/gecontec/article/view/4049Minimum Population Search is a recently developed metaheuristic for optimization of mono-objective continuous problems, which has proven to be a very effective optimizing large scale and multi-modal problems. One of its key characteristic is the ability to perform an efficient exploration of large dimensional spaces. We assume that this feature may prove useful when optimizing multi-objective problems, thus this paper presents a study of how it can be adapted to a multi-objective approach. We performed experiments and comparisons with five multi-objective selection processes and we test the effectiveness of Thresheld Convergence on this class of problems. Following this analysis we suggest a Multi-objective variant of the algorithm. The proposed algorithm is compared with multi-objective evolutionary algorithms IBEA, NSGA2 and SPEA2 on several well-known test problems. Subsequently, we present two hybrid approaches with the IBEA and NSGA-II, these hybrids allow to further improve the achieved results.application/pdfenAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Evolutionary AlgorithmMinimum Population SearchThresheld ConvergenceMulti-objective Optimizationjournal articleopen access