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Evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multiobjective Optimization

delete2024-10-01
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PRE
AI
Y
Yuxiao Huang
周
周魏 (Wei Zhou)
Yu Wang 封面图
Yu Wang (Yu Wang)
李民 封面图
李民 (Min Li)
L
Liang Feng *
K
Kay Chen Tan
DOI:10.1109/TEVC.2023.3323877delete
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摘要

摘要

En 中文
Evolutionary multitasking (EMT) has attracted much attention in the community of evolutionary computation recently. It intends to improve the performance of evolutionary optimization on multiple problems via knowledge learning and transfer across them while the optimization processes progress online. Existing EMT paradigms can be classified as explicit EMT (EEMT) and implicit EMT (IEMT) according to the mechanisms adopted in the knowledge transfer. With additional knowledge learning and transfer modules, the EEMT often brings flexible algorithmic designs and effective knowledge transfer against the IEMT. However, most of the existing EEMT studies are designed for continuous optimization problems. Due to the difficulty of learning problem-specific mappings across combinatorial optimization problems, EEMT for combinatorial optimization is still in the nascent stage. Furthermore, it is worth noting that, with the growing number of tasks in today's real-world applications and the enlarged number of decision variables in each of the tasks, learning mappings across tasks becomes more challenging. Keeping the above in mind, this article presents a novel EEMT algorithm with centralized learning for solving the large-scale and multiobjective combinatorial optimization in many-task manner, in which knowledge transfer across tasks is conducted based on a centralized learning model, instead of task-specific mappings which are required in existing EEMT studies. To investigate the performance of the proposed centralized learning assisted EEMT, comprehensive empirical studies have been conducted on the large-scale and multiobjective knapsack problems. Finally, the efficacy of our proposed method is further validated on a real-world combinatorial optimization application.
Keyword:
Task analysis
Optimization
Knowledge transfer
Multitasking
Search problems
Companies
Evolutionary computation
Combinatorial optimization
evolutionary multitasking (EMT)
large-scale optimization

期刊

IEEE Transactions on Evolutionary Computation 封面图
IEEE Transactions on Evolutionary Computation
IF:
12
论文数:
1.9K
被引数:
2.4W

机构

H
hong kong polytechnic university
学者数:
3.0W
论文数: 4.1W
被引数: 921
C
Chongqing University
学者数:
5.1W
论文数: 4.1W
被引数: 6.0W
S
shenzhen university
学者数:
4.6W
论文数: 3.4W
被引数: 72
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