Evolutionary Optimization based Solution approaches for Many Objective Reliability-Redundancy Allocation Problem. (April 2022)

• Record Type: Journal Article
• Authors: Nath, Rahul; Muhuri, Pranab K.
• Is Part Of: Reliability engineering & system safety. Volume 220(2022)
• Publication Date: 2022-04
• Subjects: Evolutionary optimization -- reliability-redundancy allocation problem -- many objective reliability-redundancy allocation problem -- NSGA-III -- NSGA-II -- SPEA2 -- MOEA/D; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.108190
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A novel study of the RRAP as a many objective optimization problem formulating the many objective RRAP (MaORRAP). A novel solution procedure based on the popular non-dominated sorting genetic algorithm-III (NSGA-III) is proposed to solve the newly formulated MaORRAP. We also solve the newly formulated MaORRAP using three other popular evolutionary approaches, viz. NSGA-II, MOEA/D and SPEA2. Study comprises series-parallel systems, complex bridge systems, overspeed gas turbine systems, pharmaceutical plant and large-scale system. We present a thorough comparative analysis and show that NSGA-III based solutions are superior to others for most of the cases. Abstract: Recently, a number of evolutionary optimization approaches were proposed to solve the many objective problems. The reliability redundancy allocation problem (RRAP), which usually has four different objectives, namely, maximization of system reliability, minimizations of cost, weight and volume, are however solved mainly as a multi-objective problem considering only two or three objectives. Therefore, this paper reports a novel study of the RRAP as a many objective optimization problem. Here, we formulate the many objective RRAP (MaORRAP) with various structures such as series-parallel systems, overspeed gas turbine system, and large-scale system. For the formulated MaORRAP, we then provide the details of a novel solution procedure based on the non-dominated sorting genetic algorithm-III (NSGA-III), a well-discussed many objective evolutionary optimization algorithm. We also solve MaORRAP using three other popular evolutionary approaches, viz. non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEA/D) and strength Pareto evolutionary archive 2 (SPEA2) algorithm. Accordingly, we present all the results in a competitive fashion to have a thorough comparative assessment of the performances of the considered approaches and show that, in most of the cases, NSGA-III based solutions are superior to others.