Abstract
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.
Highlights
With the advent of the era of big data, the application of multiprocessor systems is becoming more and more common
Lu et al.: Fireworks Algorithm for the System-Level Fault Diagnosis Based on MM* Model
If the i fireworks xi is the fireworks with the best fitness value in the current fireworks population, the value of the explosion radius calculated by Eq (8) is almost zero, this creates in the actual problem in the process of optimization search, because of the explosion range is too small, the best individuals in the current fireworks population have not played the role of local excavation, and even cannot be said to play any search function, which is contrary to the design principle of the fireworks algorithm
Summary
With the advent of the era of big data, the application of multiprocessor systems is becoming more and more common. Q. Lu et al.: Fireworks Algorithm for the System-Level Fault Diagnosis Based on MM* Model. We present a dual population fireworks fault diagnosis(DPFWFD)algorithm base on MM* model. (iii) We apply the improved fireworks algorithm to the system under the MM* model This algorithm can solve the shortcomings of the above fault diagnosis methods. The basic idea of system-level fault diagnosis is: using the communication ability and processing ability of each node in the network, let the computer nodes in the multi-processor system test each other, and get the test results. Algorithm 1 Generate an Initial Population Based on the Specified Fault-Free Node Method of the MM* Model. For the DPFWFD algorithm, the specified fault-free node method is used to generate population 1 and population 2.
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