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Research Article
Qiang Long
SWUST: Southwest University of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9672-5895
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Non-dominated sorting is a critical component of all multi-objective evolutionary algorithms (MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominated sorting. So the complexity of non-dominated sorting method in a large extent decides the efficiency of the MOEA. In this paper, we present a novel non-dominated sorting method called the dynamic non-dominated sorting (DNS). It is based on the sorting of each objective instead of dominance comparisons. The computational compelxity of DNS is $O(mN\log N)$ ($m$ is the number of objectives, $N$ is the population size), which equals to the best record so far. Based on DNS, we introduce a novel multi-objective genetic algorithm (MOGA) called the dynamic non-dominated sorting genetic algorithm (DSGA). Then, some numerical comparisons between different non-dominated sorting method are presented. The results shows that DNS is efficient and promising. Finally, numerical experiments on DSGA are also given. The results show that DSGA outperforms some other MOEAs both on general-scale and large-scale multi-objective problems.
Keywords
Multi-objective evolutionary algorithm , Multi-objective genetic algorithm , Non-dominated sorting , Pareto frontier
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This preprint is under consideration at Soft Computing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Qiang Long
SWUST: Southwest University of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9672-5895
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 12 Apr, 2021
No community comments so far
Reviews received
Received 09 Apr, 2021
Editor assigned
On 11 Mar, 2021
First submitted
On 05 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Non-dominated sorting is a critical component of all multi-objective evolutionary algorithms (MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominated sorting. So the complexity of non-dominated sorting method in a large extent decides the efficiency of the MOEA. In this paper, we present a novel non-dominated sorting method called the dynamic non-dominated sorting (DNS). It is based on the sorting of each objective instead of dominance comparisons. The computational compelxity of DNS is $O(mN\log N)$ ($m$ is the number of objectives, $N$ is the population size), which equals to the best record so far. Based on DNS, we introduce a novel multi-objective genetic algorithm (MOGA) called the dynamic non-dominated sorting genetic algorithm (DSGA). Then, some numerical comparisons between different non-dominated sorting method are presented. The results shows that DNS is efficient and promising. Finally, numerical experiments on DSGA are also given. The results show that DSGA outperforms some other MOEAs both on general-scale and large-scale multi-objective problems.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
The full text of this article is available to read as a PDF.
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