期刊
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
卷 6, 期 2, 页码 300-314出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TETCI.2021.3051970
关键词
Task analysis; Optimization; Particle swarm optimization; Search problems; Multitasking; Topology; Statistics; Evolutionary multitasking; multifactorial optimization; multitask optimization; particle swarm optimization
资金
- National Natural Science Foundation of China [62036006]
- National key research and development program of China [2017YFB0802200]
- Key Research and Development Program of Shaanxi Province [2018ZDXM-GY-045]
This study introduces a level-based inter-task learning strategy that categorizes particles into different levels and employs various inter-task learning methods. This strategy enhances the transfer of shared information among cross-task neighborhoods, allowing the algorithm to efficiently explore the search space and refine search areas.
Existing multifactorial particle swarm optimization algorithms treat all particles equally with a consistent inter-task exemplar selection and generation strategy. This may lead to poor performance when the algorithm searches partial optimal areas belonging to different tasks at the later stage. In pedagogy, teachers teach students in different levels distinctively under their cognitive and learning abilities. Inspired by this idea, in this work, we devise a novel level-based inter-task learning strategy upon a dynamic local topology of inter-task particles. The proposed method separates particles into several levels and assigns particles to different levels with distinct inter-task learning methods. Specifically, we propose a level-based inter-task learning strategy to transfer sharing information among the cross-task neighborhood. By assigning the particles with diverse search preferences, the algorithm is able to explore the search space by using the cross-task knowledge, while reserving an ability to refine the search area. In addition, to address the issue of inter-task neighbor selection, we reform dynamically the local topology structure across the inter-task particles by methodical sampling, evaluating and selecting processes. Experimental results on the benchmark problems demonstrate that the proposed method enables the efficient cross-domain information transfer via the level-based inter-task learning.
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