Multi-objective optimization in the design of load sharing systems with mixed redundancy strategies under random shocks

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2025-02-01 DOI:10.1016/j.jocs.2024.102495

Mohammad Yaghtin, Youness Javid, Mostafa Abouei Ardakan

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引用次数: 0

Abstract

The redundancy allocation problem (RAP) focuses on assigning one or more components in parallel to enhance the overall reliability of a system. Selecting a redundancy type (active or standby) for each component is a critical challenge in system design. Active components can share the load among themselves (unlike standby components), and standby components are not subjected to shock attacks (unlike active components). This research presents a multi-objective optimization model to enhance system reliability and minimize costs. The proposed model is designed for a load-sharing system with a series-parallel structure, subject to shock attacks. Reliability (availability) is calculated using a stochastic approach based on the Markov chain, and the NSGA-II algorithm solves the multi-objective optimization problem. Two numerical examples investigate the proposed approach, identifying appropriate solutions through Pareto frontiers and analyzing the impact of load-sharing and shock attacks on optimization results.

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冗余分配问题（RAP）侧重于并行分配一个或多个组件，以增强系统的整体可靠性。为每个组件选择冗余类型（活动或备用）是系统设计中的一个关键挑战。主用组件之间可以分担负载（与备用组件不同），备用组件不受冲击（与主用组件不同）。提出了一种以提高系统可靠性和降低成本为目标的多目标优化模型。所提出的模型是针对具有串并联结构的负载共享系统进行设计的。采用基于马尔可夫链的随机方法计算可靠性（可用性），采用NSGA-II算法解决多目标优化问题。两个数值算例验证了所提出的方法，通过Pareto边界确定了合适的解，并分析了负载分担和冲击攻击对优化结果的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).