Merge Non-Dominated Sorting Algorithm for Many-Objective Optimization
Many Pareto-based multi-objective evolutionary algorithms require to rank the solutions of the population in each iteration according to the dominance principle, what can become a costly operation particularly in the case of dealing with many-objective optimization problems. In this paper, we presen...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/67564 |
| Acesso em linha: | http://hdl.handle.net/10017/67564 https://dx.doi.org/10.1109/TCYB.2020.2968301 |
| Access Level: | acceso abierto |
| Palavra-chave: | Multi-objective optimization Non-dominated sorting Many objective problems Evolutionary algorithms Informática Computer science |
| Resumo: | Many Pareto-based multi-objective evolutionary algorithms require to rank the solutions of the population in each iteration according to the dominance principle, what can become a costly operation particularly in the case of dealing with many-objective optimization problems. In this paper, we present a new efficient algorithm for computing the non-dominated sorting procedure, called Merge Non-Dominated Sorting (MNDS), which has a best computational complexity of O(NlogN) and a worst computational complexity of O(MN2), being N the population size and M the number of objectives. Our approach is based on the computation of the dominance set, i.e. fo reach solution, the set of solutions that dominate it, by taking advantage of the characteristics of the merge sort algorithm. We compare MNDS against six well-known techniques that can be considered as the state-of-the-art. The results indicate that the MNDS algorithm outperforms the other techniques in terms of number of comparisons as well as the total running time |
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