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Descripción
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| 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 ? | |
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Internacional
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JCR del ISI
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Título de la revista
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Ieee Transactions on Cybernetics |
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ISSN
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2168-2267 |
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Factor de impacto JCR
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8,803 |
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Información de impacto
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Volumen
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DOI
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Número de revista
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