A quantum-inspired evolutionary approach for non-homogeneous redundancy allocation in series-parallel multi-state systems

Redundancy allocation is a family of well-known reliability optimization problems. The non-homogeneous type of redundancy allocation in series-parallel multi-state systems is among the most difficult ones. Evolutionary algorithms (EAs) are frequently applied to solve the problem, mainly due to the huge search space and the non-closed-form system reliability. This work proposes an efficient approach that combines a quantum-inspired evolutionary algorithm (QEA) with a newly designed local search strategy. Different from the existing EAs, it is able to evolve an explicit probabilistic model to explore the search space in an iterative way. The proposed method is tested on two benchmark problems with the comparisons to the published results. The results are promising in terms of both solution quality and computation efficiency.