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Descripción
The existing multiobjective evolutionary algorithms (EAs) based on nondominated sorting may encounter serious difficulties in tackling many-objective optimization problems (MaOPs), because the number of nondominated solutions increases exponentially with the number of objectives, leading to a severe loss of selection pressure. To address this problem, some existing many-objective EAs (MaOEAs) adopt Euclidean or Manhattan distance to estimate the convergence of each solution during the environmental selection process. Nevertheless, either Euclidean or Manhattan distance is a special case of Minkowski distance with the order P=2 or P=1, respectively. Thus, it is natural to adopt Minkowski distance for convergence estimation, in order to cover various types of Pareto fronts (PFs) with different concavity-convexity degrees. In this paper, a Minkowski distance-based EA is proposed to solve MaOPs. In the ?
Internacional	Si
JCR del ISI	Si
Título de la revista	Ieee Transactions on Cybernetics
ISSN	2168-2267
Factor de impacto JCR	8,803
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Esta actividad pertenece a memorias de investigación

• Autor: Hang Xu
• Autor: Wenhua Zeng
• Autor: Xiangxiang Zeng . (UPM)
• Autor: Gary G Yen

• Creador: Grupo de Investigación: Grupo de Inteligencia Artificial (LIA)
• Departamento: Inteligencia Artificial