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Evolutionary Many-Task Optimization Based on Multisource Knowledge Transfer
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DOI：10.1109/TEVC.2021.3101697.png)
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摘要
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Multitask optimization aims to solve two or more optimization tasks simultaneously by leveraging intertask knowledge transfer. However, as the number of tasks increases to the extent of many-task optimization, the knowledge transfer between tasks encounters more uncertainty and challenges, thereby resulting in degradation of optimization performance. To give full play to the many-task optimization framework and minimize the potential negative transfer, this article proposes an evolutionary many-task optimization algorithm based on a multisource knowledge transfer mechanism, namely, EMaTO-MKT. Particularly, in each iteration, EMaTO-MKT determines the probability of using knowledge transfer adaptively according to the evolution experience, and balances the self-evolution within each task and the knowledge transfer among tasks. To perform knowledge transfer, EMaTO-MKT selects multiple highly similar tasks in terms of maximum mean discrepancy as the learning sources for each task. Afterward, a knowledge transfer strategy based on local distribution estimation is applied to enable the learning from multiple sources. Compared with the other state-of-the-art evolutionary many-task algorithms on benchmark test suites, EMaTO-MKT shows competitiveness in solving many-task optimization problems.
Keyword:
Task analysis
Knowledge transfer
Optimization
Statistics
Sociology
Resource management
Heuristic algorithms
Evolutionary many-task optimization (EMaTO)
local distribution estimation
maximum mean discrepancy (MMD)
multisource knowledge transfer
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
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