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<p>This study proposes a tree-profiled Artificial Bee Colony algorithm (TABC) inspired by Monte Carlo Tree Search to enhance the optimization of reliability-redundancy allocation problems (RRAPs) in <i>k</i>-out-of-<i>n</i> systems. TABC identifies promising foraging areas through multiple playouts and improves exploitation using a “clustered search in promising foraging areas” method. Additionally, a Continuous Time Markov Chain (CTMC) is employed for modules of reliability calculations based on mixed redundancy strategies, allowing subsystems to select appropriate redundancy strategies according to specific <i>k</i> values or total component numbers. Through experiments with four newly proposed benchmark problems in the literature, TABC demonstrated superior system reliability with lower configuration costs compared to state-of-the-art methods for subsystems comprising identical components. Notably, for RRAPs, this study is the first to integrate a component mixing (CM) strategy into <i>k</i>-out-of-<i>n</i> (G) systems, further enhancing system reliability with heterogonous components in subsystems.</p>

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Reliability-redundancy allocation in k-out-of-n (G) systems with mixed redundancy and component mixing strategies by using a tree-profiled artificial bee colony algorithm

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Tsung-Jung Hsieh

摘要

This study proposes a tree-profiled Artificial Bee Colony algorithm (TABC) inspired by Monte Carlo Tree Search to enhance the optimization of reliability-redundancy allocation problems (RRAPs) in k-out-of-n systems. TABC identifies promising foraging areas through multiple playouts and improves exploitation using a “clustered search in promising foraging areas” method. Additionally, a Continuous Time Markov Chain (CTMC) is employed for modules of reliability calculations based on mixed redundancy strategies, allowing subsystems to select appropriate redundancy strategies according to specific k values or total component numbers. Through experiments with four newly proposed benchmark problems in the literature, TABC demonstrated superior system reliability with lower configuration costs compared to state-of-the-art methods for subsystems comprising identical components. Notably, for RRAPs, this study is the first to integrate a component mixing (CM) strategy into k-out-of-n (G) systems, further enhancing system reliability with heterogonous components in subsystems.