Knowledge Transfer-Based Multifactorial Evolutionary Algorithm for Selective Maintenance Optimization of Multistate Complex Systems

This article focuses on multitask selective maintenance (SM) for multistate complex systems, with the goal of selecting subsets of feasible maintenance actions on multitask systems simultaneously due to limited resources. For each task, system characteristic comprises of various configurations, such as series, parallel, bridge, and complex, Weibull distribution, and multiple states; maintenance characteristic includes perfect maintenance, imperfect maintenance (IM), and minimal repair. Considering these realistic issues, this article introduces a reliability evaluation approach, including Markov chain, universal generating function, and IM age reduction model. The challenge of solving such kind of problems lies not only in the reliability estimation, but also in the solution method. Since it is the first time to solve the multitask SM problem, this article tailors a novel multifactorial evolutionary algorithm, with an improved associate mating. In our algorithm, a similarity-based task selection mechanism tries to determine the intensity between intertask self-evolution and intertask knowledge transfer, based on the relatedness between tasks; a feedback-based task transfer mechanism adjusts the transfer intensity, with regard to convergence and diversity. Numerical experiments verify the effectiveness of the proposed method compared with the original one.