Publicación: Self-Adaptive Polynomial Mutation in Multi-Objective Evolutionary Algorithms
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2022-09-01
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info:eu-repo/semantics/openAccess
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Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
Resumen
Evolutionary multi-objective optimization is a field that has experienced a rapid growth in the last two decades. Although an important number of new multi-objective evolutionary algorithms have been designed by the scientific community, the popular Non-Dominated Sorting Genetic Algorithm (NSGAII) remains as a widely used baseline for performance comparison purposes. Since every evolutionary algorithm needs several parameters to be set up in order to operate, parameter control constitutes a crucial task for the effective and efficient performance of multi-objective evolutionary algorithms. However, despite the advancements in parameter control for evolutionary algorithms, NSGA-II has been mainly used in the literature with fine-tuned static parameters. This paper introduces a novel and computationally lightweight self-adaptation mechanism for controlling the distribution index parameter of the polynomial mutation operator usually employed by NSGA-II in particular and by multi-objective evolutionary algorithms in general. Additionally, the classical NSGA-II using polynomial mutation with a static distribution index is compared with this new version utilizing a self-adapted parameter. The experiments carried out over twenty-five benchmark problems using three quality indicators (hypervolume, generalized spread, and modified inverted generational distance) show that the proposed self-adaptive mutator variant outperforms its static counterpart in most of the cases. This result supports the potential of self-adaptive parameter control in multi-objective evolutionary algorithms.
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Categorías UNESCO
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multi-objective evolutionary algorithm, NSGA-II, polynomial mutation, distribution index self-adaptation
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Facultades y escuelas::E.T.S. de Ingeniería Informática
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Inteligencia Artificial