Resilience Importance Measure and Optimization Considering the Stepwise Recovery of System Performance

Effective recovery after disruptions is essential to improve system resilience. For many distributed systems such as offshore wind farms and communication networks, spatial location characteristics and limited maintenance resources lead to discontinuous changes in performance during system recovery. However, the stepwise performance is frequently ignored. The coupling relationship between the system and maintenance teams makes it difficult to improve the system resilience from the recovery perspective. In this article, a new method that combines the resilience importance measure and enhanced pigeon-inspired optimization (PIO) is presented to optimize the system resilience under stepwise recovery conditions. First, this study proposes a resilience-oriented importance measure that evaluates the recovery priority for each component. Second, an optimization model is established to minimize the system resilience loss by joint optimization of recovery sequence and task assignment under the conditions of multiple maintenance teams. Third, the resilience importance measure is combined with roulette wheel selection to form a high-quality intitle swarm of PIO. Fourth, an importance measure-based PIO with a Gaussian mutation and adaptive crossover operator is designed to find an optimization solution for system resilience. Finally, the recovery of a network system consisting of 32 nodes and 71 edges is studied. Compared with the stochastic method and traditional PIO, the proposed method in this study reduces the system resilience loss by 48.87 and 15.54%, respectively.

Automated summary

This article proposes a new method that combines resilience importance measure and enhanced PIO to optimize system resilience. By optimizing the recovery sequence and task assignment, the method aims to improve system resilience. The study demonstrates the effectiveness of the proposed method through the recovery of a network system, showing a significant reduction in system resilience loss compared to stochastic and traditional PIO methods.