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This paper introduces a novel discrete bat algorithm designed for heterogeneous redundancy allocation problems in multi-state systems affected by probabilistic common-cause failure. It utilizes an interval-valued universal generating function for component availability evaluation and incorporates Hamming distance and Q-learning to enhance bat movement and local search strategies. Experimental results demonstrate that the proposed algorithm outperforms existing methods in solving these complex optimization p

Author

Jacek

Language

EN