A novel evolutionary solution approach for many-objective reliability-redundancy allocation problem based on objective prioritization and constraint optimization

Rahul Nath and Pranab K. Muhuri

Reliability Engineering and System Safety, 2024, vol. 244, issue C

Abstract: The reliability redundancy allocation problem (RRAP) has been mostly solved either as a single or as a multi-objective optimization problem. However, this problem also has numerous important constraints which play prominent roles in meeting the objectives. This paper proposes a novel formulation named ‘prioritized many reliability redundancy allocation problems (PrMaORRAP)’ that optimizes all the problem objectives concurrently, and also preserves the priority among them. Then, we propose a hybrid method which utilizes the features of many-objective optimization as well as priority relations between different objectives. Here, we divide the procedure into two modules: one is the main priority or the leader which will stay at the top level; underneath the first lie the second part in which rest of the objectives are optimized. The solution approach embeds the optimization structure within the evolutionary process making a prioritized many-objective evolutionary algorithms. We formulate various structures such as series, series-parallel, complex bridge and overspeed gas turbine system of RRAP as many-objective optimization problems, and provide detailed experimental demonstration on how our proposed model works for all these structures. We compare the results given by the proposed approach with the results of other approaches available in the literature and establish the superiority of our proposed solution approach.

Keywords: Many-objective optimization; Reliability-redundancy allocation problem (RRAP); Evolutionary algorithm; Evolutionary optimization; Objective prioritization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:244:y:2024:i:c:s0951832023007494

DOI: 10.1016/j.ress.2023.109835

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