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An efficient nondominated sorting algorithm

Published: 06 July 2018 Publication History

Abstract

Nondominated sorting (NS) is commonly needed in multi-objective optimization to distinguish the fitness of solutions. Since it was suggested, several NS algorithms have been proposed to reduce its time complexity. In our study, we found that their performances are closely related to properties of the distribution of a data set, especially the number of fronts. To address this issue, we propose a novel NS algorithm Filter Sort. We also propose a new benchmark data generator for evaluating the performance of a NS algorithm. Experimental results show that our algorithm is superior to several state-of-the-art NS algorithms in most cases.

References

[1]
K. McClymont and E. Keedwell. 2012. Deductive Sort and Climbing Sort: New Methods for Non-Dominated Sorting. Evolutionary Computation 20, 1 (2012), 1--26.
[2]
H. Wang and X. Yao. 2014. Corner Sort for Pareto-Based Many-Objective Optimization. IEEE Transactions on Cybernetics 44, 1 (2014), 92--102.
[3]
X. Zhang, Y. Tian, R. Cheng, and Y. Jin. 2018. A Decision Variable Clustering-Based Evolutionary Algorithm for Large-scale Many-objective Optimization. IEEE Transactions on Evolutionary Computation 22, 1 (2018), 97--112.

Cited By

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  • (2024)Hierarchical non-dominated sort: analysis and improvementGenetic Programming and Evolvable Machines10.1007/s10710-024-09487-125:1Online publication date: 16-Apr-2024
  • (2024)A Review of Non-dominating Sorting AlgorithmsBusiness Intelligence and Information Technology10.1007/978-981-97-3980-6_15(173-183)Online publication date: 30-Aug-2024
  • (2023)On the Computational Complexity of Efficient Non-dominated Sort Using Binary SearchEvolutionary Multi-Criterion Optimization10.1007/978-3-031-27250-9_30(419-432)Online publication date: 9-Mar-2023
  • Show More Cited By

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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2018
1968 pages
ISBN:9781450357647
DOI:10.1145/3205651
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2018

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Author Tags

  1. filter sort
  2. multi-objective optimization
  3. nondominated sorting

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  • Poster

Funding Sources

  • Hubei Provincial Natural Science Foundation of China
  • National Natural Science Foundation of China
  • Fundamental Research Funds for the Central Universities, China University of Geosciences(Wuhan)
  • 111 project

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GECCO '18
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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2024)Hierarchical non-dominated sort: analysis and improvementGenetic Programming and Evolvable Machines10.1007/s10710-024-09487-125:1Online publication date: 16-Apr-2024
  • (2024)A Review of Non-dominating Sorting AlgorithmsBusiness Intelligence and Information Technology10.1007/978-981-97-3980-6_15(173-183)Online publication date: 30-Aug-2024
  • (2023)On the Computational Complexity of Efficient Non-dominated Sort Using Binary SearchEvolutionary Multi-Criterion Optimization10.1007/978-3-031-27250-9_30(419-432)Online publication date: 9-Mar-2023
  • (2022)Counterexample to the best-case running time of efficient non-dominated sorting algorithmProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3528777(798-800)Online publication date: 9-Jul-2022
  • (2020)Filter Sort Is $$\varOmega (N^3)$$ in the Worst CaseParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58115-2_47(675-685)Online publication date: 2-Sep-2020

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