Research on Optimization Technology for Sequential Diagnostic Strategy based on Improved Quasi-Depth First Search Algorithm
Authors: 
Xiaofeng Lv, Deyun Zhou, Fuqiang LiAuthors Info & Claims
Pages 684 - 692
Abstract
Abstract: Aiming at the problem that sequential diagnosis strategy optimization techniques for complex avionics systems are currently difficult to obtain optimal solutions quickly and effectively, this paper proposes an improved Quasi-Depth First Search (QDFS) algorithm based on the Rollout algorithm to optimize the design of sequential diagnosis strategies. The algorithm determines a test in two steps. Firstly, the Rollout algorithm is used to construct a temporary diagnostic tree with the selected test as the vertex, and then the "evaluation function" of the improved QDFS algorithm is used to calculate and compare the constructed temporary diagnostic tree to obtain the optimal diagnostic tree. The first test of the diagnostic tree is the current optimal test. Through a case study of sequential diagnosis strategy optimization, the results show that the improved algorithm can obtain a global optimal solution and generate an optimal diagnosis tree compared to the improved QDFS algorithm.
References
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Published In
May 2023
1025 pages
ISBN:9798400700705
DOI:10.1145/3603781
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 July 2023
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- Research-article
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- Refereed limited
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CNIOT'23
CNIOT'23: 2023 4th International Conference on Computing, Networks and Internet of Things
May 26 - 28, 2023
Xiamen, China
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Overall Acceptance Rate 39 of 82 submissions, 48%
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