Abstract
A multiprocessor system should be able to identify and eliminate faults in time to avoid the paralysis of a whole system. This paper proposes an improved binary bat algorithm to identify faulty processors in a multiprocessor system. Compared with most existing works based on metaheuristic algorithms, the proposed algorithm employs a random initial population and does not require transfer functions. The exclusive-OR operation in the velocity equation is used to measure the distance between two individuals in binary space. To improve population diversity and avoid local optima, the mutation operator is integrated into the position update equation. A new local search strategy is proposed to strengthen the ability of local search in binary space. Experimental results show that the proposed algorithm based on the Malek model can maintain approximately \(100\%\) diagnostic accuracy in a small random initial population with fewer iterations and less CPU running time.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61862003, Grant 61862004, and in part by the Ph.D. Scientific Research Foundation of Guangxi University of Finance and Economics under Grant BS2021016.
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Gui, W., Pan, F., Zhu, D. et al. Faulty processor identification for a multiprocessor system under the Malek model using an improved binary bat algorithm. J Supercomput 79, 3791–3820 (2023). https://doi.org/10.1007/s11227-022-04790-z
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DOI: https://doi.org/10.1007/s11227-022-04790-z