A novel discrete bat algorithm for heterogeneous redundancy allocation of multi-state systems subject to probabilistic common-cause failure

Abstract This paper focuses on a heterogeneous redundancy allocation problem (RAP) for multi-state series-parallel systems subject to probabilistic common-cause failure and proposes novel discrete bat algorithm solve it. Although abundant research studies have been published solving RAPs, few of them studied common cause failure, which motivates this paper. Due the insufficient data components, an interval-valued universal generating function is utilized evaluate availability components whole system. The challenge kind RAPs lies in not only reliability estimation, but also solution method. presents (BA) effectively dealing with proposed RAP alleviating premature convergence BA. Two main features adaptation are Hamming distance-based movement (HDBM) Q learning-based local search (QLLS). HDBM transfers distance between current best swarm rate. Then, QLLS utilizes Q-learning adjust strategies dynamically during iterations. computational results from extensive experiments demonstrate that powerful, more efficient than other state-of-the-arts sort problems.