Evolutionary Multi-Tasking Optimization for High-Efficiency Time Series Data Clustering-Reference-Cited by-同舟云学术

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Evolutionary Multi-Tasking Optimization for High-Efficiency Time Series Data Clustering

Published:2024-04 Issue:2 Volume:29 Page:343-355
ISSN:1007-0214
Container-title:Tsinghua Science and Technology
language:
Short-container-title:Tsinghua Sci. Technol.

Author:

Wang Rui¹,Li Wenhua²,Shen Kaili³,Zhang Tao²,Liao Xiangke⁴

Affiliation:

1. Xiangjiang Laboratory,Changsha,China,410205

2. College of Systems Engineering, National University of Defense Technology,Changsha,China,410073

3. Ant Group Co., Ltd.,Hangzhou,China,310000

4. College of Computer Science and Technology, NUDT,Changsha,China,410073

Funder

National Natural Science Foundation of China

Publisher

Tsinghua University Press

Subject

Multidisciplinary

Link

http://xplorestaging.ieee.org/ielx7/5971803/10258149/10258256.pdf?arnumber=10258256

Reference48 articles.

1. A Knee-Point Driven Multi-objective Evolutionary Algorithm for Flexible Job Shop Scheduling

2. Multiobjective Multifactorial Optimization in Evolutionary Multitasking

3. Genetic algorithm-based clustering technique

4. Evolutionary algorithm and multifactorial evolutionary algorithm on clustered shortest-path tree problem

5. Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An online transfer learning based multifactorial evolutionary algorithm for solving the clustered Steiner tree problem;Knowledge-Based Systems;2024-07

2. Ocean: Online Clustering and Evolution Analysis for Dynamic Streaming Data;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

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