Knowledge transfer-based multi-factorial evolutionary algorithm for selective maintenance optimization of multi-state complex systems
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Abstract
This paper focuses on multi-task selective maintenance for multi-state complex systems, with the goal of selecting subsets of feasible maintenance actions on multi-task 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, and minimal repair. Considering these realistic issues, this paper introduces a reliability evaluation approach, including Markov chain, universal generating function, and imperfect maintenance 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 multi-task selective maintenance problem, this paper tailors a novel multi-factorial evolutionary algorithm, with an improved associate mating. In our algorithm, a similarity-based task selection mechanism tries to determine the intensity between inter-task self-evolution and inter-task 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.