A Fireworks Algorithm for the System-Level Fault Diagnosis Based on MM Model

• Published in: IEEE access Vol. 7; pp. 136975 - 136985
• Main Authors: Lu, Qian, Gui, Weixia, Su, Meili
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">t -diagnosable systems; Algorithms; Computational modeling; dual population; Fault diagnosis; Fireworks; fireworks algorithm; Iterative methods; MM model; Multiprocessing systems; Mutation; Optimization; Sociology; Statistics; System-level fault diagnosis; Task analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2942336

Aiming at the characteristics of MM* model fault diagnosis, a fireworks algorithm based on a dual population strategy is designed. The dual population of the algorithm is operated independently in parallel, and cooperative operator and optimal operator are cross-executed in the iterative process. The cooperative operator enables two populations to exchange effective information, avoiding the premature maturity of the algorithm. The optimal operator helps to strengthen the global search power of the algorithm and improve the convergence rate of the algorithm. At the same time, the constraint equation is designed, a new fitness function is proposed, and the mutation operator and selection strategy are optimized. The experimental comparison shows that the algorithm improves the efficiency and accuracy of system-level fault diagnosis and has good practicability. Finally, the correctness of the algorithm is proved by theory, and the time complexity of the algorithm is analyzed.