Solving reliability redundancy allocation problem using grey wolf optimization algorithm

Rakhi, .; Pahuja, G. L.

doi:10.1088/1742-6596/1706/1/012155

Cited by 5 publications

(6 citation statements)

References 41 publications

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“…The obtained results are compared with Hsieh et al 18 for the outcomes of GA, 22 for the ABC algorithm, 28 for the PPSO and PSO algorithm, and Pahuja & Rakhi, 29 for GWO algorithm. Table 9 shows the comparison for the series system, where the implemented HPSGWO has given the best results among GA, PPSO, ABC, PSO algorithms.…”

Section: Comparison Of Outcomes With the Results From The Literaturementioning

confidence: 99%

“…Table 12 is the presentation of the outcomes of the HPSGWO algorithm for the gas turbine's overspeed protection system optimization problem. Also, the outcomes are compared with 18 for the outcomes of GA, 22 for the ABC algorithm, 28 for the PPSO and PSO algorithm, and 29 for GWO algorithm. In this benchmark, the implemented HPSGWO has provided the best outcomes among GA, PPSO, ABC, PSO algorithms.…”

Section: Comparison Of Outcomes With the Results From The Literaturementioning

confidence: 99%

“…The PSO and GWO have also been implemented for RRAP optimization, 28,29 where authors have been presented their optimized results for the four benchmark RRAP. In dealing with RRAP, the literature shows that RRAP subjected to linear constraints have not gained too much attention 30,31 among the researchers whereas a lot of work has been done with non-linear constraints [17][18][19][20][21][22]24,25,32 .…”

Section: Introductionmentioning

confidence: 99%

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Grey wolf optimizer and hybrid PSO‐GWO for reliability optimization and redundancy allocation problem

Bhandari

Kumar

Ram

2023

Quality & Reliability Eng

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Analysis and optimization of system reliability have very much importance for developing an optimal design for the system while using the available resources. Several studies are centered towards reliability optimization using metaheuristics. In this study, a recently developed metaheuristic optimization algorithm called hybrid PSO-GWO (HPSGWO) to solve the reliability-redundancy optimization problem has been proposed. The HPSGWO fuses the Particle Swarm Optimization's (PSO) exploitation ability with the grey wolf optimizer's (GWO) exploration ability. The comparison of results with prior best results of PSO and GWO for the four benchmarks of reliability redundancy allocation problem demonstrates the HPSGWO as a productive enhancement strategy since it got promising answers than other metaheuristic algorithms.

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Section: Comparison Of Outcomes With the Results From The Literaturementioning

confidence: 99%

Section: Comparison Of Outcomes With the Results From The Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Grey wolf optimizer and hybrid PSO‐GWO for reliability optimization and redundancy allocation problem

Bhandari

Kumar

Ram

2023

Quality & Reliability Eng

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“…Many researchers have recently presented a variety of metaheuristic methods: Gray wolf optimizer (GWO) (Mirjalili et al, 2014;Pahuja, 2020), Water Cycle Algorithm (Mahdavi-Nasab et al, 2020), Shuffled Frog Leaping Algorithm (SFLA) (Gandhi and Bhattacharjya, 2020), Moth Flame Optimization (MFO) algorithm (Mirjalili, 2015;Sahoo et al, 2023), Whale Optimization Algorithm (WOA) (Mirjalili and Lewis, 2016;Dao et al, 2016), Dwarf Mongoose Optimization Algorithm (DMOA) (Agushaka et al, 2022), Cat and Mouse Based Optimizer (CMBO) (Dehghani et al, 2021), Coati Optimization Algorithm (COA) (Dehghani et al, 2023), Gazelle Optimization Algorithm (GOA) (Agushaka et al, 2023), Dragonfly Algorithm (DA) (Mirjalili, 2016), Crystal Structure Algorithm (CSA) (Khodadadi et al, 2021), and Stochastic Paint Optimizer (SPO) (Kaveh et al, 2020), which are few meta-heuristics approaches used in reliability 2.2. Complex System Optimization.…”

Section: Metaheuristic Approachesmentioning

confidence: 99%

Analysis of Reliability and Cost of Complex Systems with Metaheuristic Algorithms

Choudhary,

Ram,

Goyal

et al. 2024

ees

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Introduction. The assessment of the reliability and cost of complex systems, such as Complex Bridge Systems (CBS) and Life Support Systems in Space Capsules (LSSSC), is fascinating. To achieve the ideal system design through diverse constraints and increase overall system reliability, researchers have extensively explored system reliability and cost optimization problems. Hence, the significant advancement in metaheuristic methods is the primary source of further system reliability and cost optimization process refinement. Aim and tasks. This research attempts to enhance the reliability and cost of complex systems named CBC and LSSSC has been presented. Results. The structure is based on few recent metaheuristic techniques, such as Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Gazelle Optimization Algorithm (GOA), Dragonfly Algorithm (DA), and Coati Optimization Algorithm (COA). Comparing the acquired findings to those found in other proposed techniques demonstrates the usefulness of a methodology based on COA. The proposed COA algorithm exhibits enhanced efficiency by offering superior solutions to reliability and cost-optimization problems. In addition, a non-parametric Friedman ranking was performed for validation. The results of this research are based on improving the reliability of the parameters and decreasing complex systems’ costs used by the five metaheuristic methods. Observing the convergence graph, Friedman ranking, statistical results test, and tables determined that COA is the most effective algorithm for a complex system’s cost and reliability parameters compared to other existing approaches, and also provided a faster solution. Conclusions. This study proposes unique ways to reduce costs while increasing parameter reliability in complex systems. After analysing the comparative solution, the authors found that when comparing these approaches (GOA, DA, MFO, WOA, and COA), the COA provided the best minimum solution for the cost and reliability of complex systems. Hence, the suggested COA procedure was more successful than that described in this study.

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“…Based on the literature compilation [2,5], other algorithms such as Simulated Annealing (SA) [10], Particle Swarm Optimization (PSO) [11], Modied Particle Swarm Optimization (MPSO) [12], Articial Bee Colony (ABC) [13], CS-GA [14], BAT (Bat algorithm) [15], ACO (Ant Colony Optimization) [16], Enhanced Nest Cuckoo Optimization Algorithm [17], Grey Wolf Optimization Algorithm (GWO) [18,19] and the results presented in the works [13,[20][21][22][23][24][25][26][27][28] were not as good as those obtained using the FA algorithm. However, the question remains open as to whether other algorithms could lead to different global maximum solutions for the considered (or other) RRAP criterion functions?…”

Section: Introductionmentioning

confidence: 99%

Application of RRAP reliability optimization as a test of nature-inspired algorithms

Pieprzycki,

Filipowicz

2024

Sci, Tech. Innov.

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This paper presents a discussion on the application of two swarm intelligence algorithms, Cuckoo Search (CS) and Firey Algorithm (FA), to maximize the reliability of two complex systems with resource constraints, which have been well-known in the literature. The reliability of the systems is also evaluated using several classical methods. The results indicate that although the CS algorithm, which utilizes Lévy flight, is eective, the FA rey algorithm outperformed it in the presented optimization tasks, within the given parameter range. These ndings contribute to the ongoing discussion on using nature-inspired algorithms for solving Reliability Redundancy Allocation Problem (RRAP) problems, and the two test scenarios used in the study can be useful for validating other algorithms in RRAP problems. The paper introduces metrics and methods for analyzing and comparing the performance of algorithms in RRAP optimization, including the comparison of criterion function values and other parameters introduced in the paper. Additionally, the paper discusses statistical analyses of variance (ANOVA) with post-hoc RIR Tuckey tests.

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Solving reliability redundancy allocation problem using grey wolf optimization algorithm

Cited by 5 publications

References 41 publications

Grey wolf optimizer and hybrid PSO‐GWO for reliability optimization and redundancy allocation problem

Grey wolf optimizer and hybrid PSO‐GWO for reliability optimization and redundancy allocation problem

Analysis of Reliability and Cost of Complex Systems with Metaheuristic Algorithms

Application of RRAP reliability optimization as a test of nature-inspired algorithms

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