An investigation of new local search strategies in memetic algorithm for redundancy allocation in multi-state series-parallel systems

Redundancy allocation is widely used for optimizing system reliability in various fields. Non-homogeneous redundancy allocation in multi-state series-parallel systems has captured much attention in recent years. Due to the large and complex solution space, it is among the most difficult types of redundancy allocation problems. Due to the multimodal landscape of solution neighborhood, ordinary evolutionary algorithms (EAs) could not sufficiently exploit the solution neighborhood and thus usually trap into local optimums. Therefore, local search (IS) becomes a critical attachment to the main EA to achieve better performance. This work proposes two originally designed inter-subsystem LS methods for non-homogeneous redundancy allocation in multi-state series-parallel systems. They provide a well-balanced mechanism to reduce system cost via inter-subsystem operation and ensure system availability. The inter-subsystem local search methods are combined with quantum-inspired evolutionary algorithm (QEA), establishing an efficient memetic algorithm. The proposed memetic algorithm is validated on five benchmark problems with different availability requirements. The statistical tests results indicate that the inter-subsystem IS strategies are significantly better than the LS strategies published in QEA approach proposed by one of the authors of this paper, at small computational costs. The comparisons to various published results show that the proposed memetic algorithm achieves the best published solutions using much fewer fitness evaluations and higher robustness.