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Multiobjective Multitask Optimization-Neighborhood as a Bridge for Knowledge Transfer
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AI
DOI：10.1109/TEVC.2022.3154416.png)
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摘要
En 中文
The implicit parallelism of a population in evolutionary algorithms (EAs) provides an ideal platform for dealing with multiple tasks simultaneously. However, little effort has been made to explore what information among different tasks can be used as valuable knowledge to help the optimization of different tasks. This article proposes a multiobjective multitask optimization (MO-MTO) EA based on decomposition with dual neighborhoods (MTEA/D-DN), in which the neighborhood is used as a bridge to achieve knowledge transfer among different tasks. In MTEA/D-DN, each subproblem not only maintains a neighborhood (internal neighborhood) within its own task based on the Euclidean distance between weight vectors but also keeps a neighborhood (external neighborhood) with the subproblems of other tasks via gray relation analysis in order to mine valuable information and communicate among tasks. The experimental studies show that our proposed algorithm outperforms five other state-of-the-art algorithms on a set of benchmark test instances and a real-world problem in steel plant.
Keyword:
Task analysis
Optimization
Knowledge transfer
Evolutionary computation
Sociology
Collaboration
Bridges
Decomposition
evolutionary algorithm (EA)
multiobjective multitask optimization (MO-MTO)
neighborhood
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
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An Effective Knowledge Transfer Approach for Multiobjective Multitasking Optimization多目标多任务优化的有效知识转移方法
PREAI
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