|作者：||Darian Reyes-Fernández-de-Bulnes, Antonio Bolufé-Röhler, Dania Tamayo-Vera|
1Instituto Tecnológico de Tijuana
2University of Prince Edward Island
3Thinking Big Inc.
|刊名：||International Journal of Knowledge and Technology Management, 2019, Vol.7 (2), pp.1-19|
|来源数据库：||Revista Internacional de Gestión del Conocimiento y la Tecnología|
|关键词：||Evolutionary Algorithm; Minimum Population Search; Thresheld Convergence; Multi-objective Optimization;|
|英文摘要：||Minimum 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.|