Multi-objective grey wolf optimizer approach to the reliability-cost optimization of life support system in space capsule

Kumar, Anuj; Pant, Sangeeta; Ram, Mangey; Chaube, Shshank

doi:10.1007/s13198-019-00781-1

Cited by 29 publications

(7 citation statements)

References 23 publications

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“…So, some researchers are constantly working on developing different types of nature inspired meta-heuristics technique. Some of them recently developed are Evolutionary algorithm (EA) (Ramírez-Rosado & Bernal-Agustín, 2001), Ant colony optimization (ACO), (Zha et al, 2007;Dorigo & Gambardella, 1997) Particle Swarm Optimization Algorithm (PSO) (Eberhart & Kennedy, 1995;Kennedy & Eberhart, 1997;Hu & Eberhart, 2002, Pant & Singh, 2011 Grey wolf optimization technique (GWO) (Mirjalili et al 2014;Fouad et al, 2015;Jayabarathi et al, 2016;Mosavi et al, 2016;Kumar et al, 2017;Kumar et al, 2019aKumar et al, , 2019bPant et al, 2019;), Flower pollination Algorithm (Pant et al, 2017) and Cuckoo search algorithm (CSA) (Yang & Deb, 2009). The detailed reviews of reliability optimization especially GWO, PSO optimization techniques are given by Kuo and Prasad (2000); Negi et al (2020); Padhye et al (2009); Uniyal et.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Basically, reliability optimization problems can be classified into three categories depending upon the decision variables involved. These are (i) reliability allocation (Li et al, 2008;Mirjalili et al, 2016;Pant et al, 2017;Kumar et al, 2019aKumar et al, , 2019b;) (ii) redundancy allocation (Atiqullah & Rao, 1993;Misra & Sharma, 1991a, 1991bYang & Deb, 2009) and (iii) reliability-redundancy allocation (Sakawa, 1978;Coelho, 2009;Deep & Deepti, 2009). Going by the concept of mathematical programming reliability allocation is a continuous nonlinear programming problem (NLP).…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

Negi

Kumar

Pant

et al. 2021

Decis. Mak. Appl. Manag. Eng.

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Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

Negi

Kumar

Pant

et al. 2021

Decis. Mak. Appl. Manag. Eng.

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“…These algorithms are very fast and search for the best possible solution when implemented on a computer. For more detail, about these algorithms, one can refer (Kumar et al , 2017a, b, 2018, 2019a, b; Pant et al , 2017; Mirjalili et al , 2014). For obtaining the desired reliability from the ATVM, one can apply any of these meta-heuristic approaches for the proper component selection of the ATVM from the list of components which may have various reliabilities, performances, costs and weights.…”

Section: Future Scopementioning

confidence: 99%

Reliability assessment for multi-state automatic ticket vending machine (ATVM) through software and hardware failures

Kumar

2021

JQME

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PurposeThis paper presents the performance analysis of the automatic ticket vending machine (ATVM) through the functioning of its different hardware and software failures.Design/methodology/approachFrequent failures in the working of ATVM have been observed; therefore, the authors of the paper intend to analyze the performance measures of the same. Authors have developed a mathematical model based on different hardware and software failures/repairs, which may occur during the operation, with the help of the Markov process. The developed model has been solved for two kinds of failure/repair rates namely variable failures (very much similar to real-time failure) and constant failures. Lagrange's method and Laplace transformation are used for the solution of the developed model.FindingsReliability and mean time to failure of the ATVM are determined. Sensitivity analysis for ATVM is also carried out in the paper. Critical components of the ATVM, which affect the performance of the same, in terms of reliability and MTTF are also identified.Originality/valueA mathematical model based on different hardware and software failures/repairs of ATVM has been developed to analyze its performance, which has not been done in the past.

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“…Various researchers of the field of reliability engineering have applied the concepts of nature inspired optimization techniques to their reliability optimization problems (Table 1). Recently, the reliability-cost optimization of the life support system in space capsule by using a very recent metaheuristic named Multi-Objective Grey Wolf Optimizer (MOGWO) approach has been done by Kumar et al (2019b). The efficiency of MOGWO in optimizing the reliability-cost of life support system has also been demonstrated by comparing its results with a very popular swarm based optimization technique named multi-objective particle swarm optimization.…”

Section: Reliability Optimizationmentioning

confidence: 99%

An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications

Uniyal¹,

Pant²,

Kumar³

2020

Int J Math, Eng, Manag Sci

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Optimization has been a hot topic due to its inevitably in the development of new algorithms in almost every applied branch of Mathematics. Despite the broadness of optimization techniques in research fields, there is always an open scope of further refinement. We present here an overview of nature-inspired optimization with a subtle background of fundamentals and classification and their reliability applications. An attempt has been made to exhibit the contrast nature of multi objective optimization as compared to single objective optimization. Though there are various techniques to achieve the optimality in optimization problems but nature inspired algorithms have proved to be very efficient and gained special attention in modern research problems. The purpose of this article is to furnish the foundation of few nature inspired optimization techniques and their reliability applications to an interested researcher.

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Multi-objective grey wolf optimizer approach to the reliability-cost optimization of life support system in space capsule

Cited by 29 publications

References 23 publications

Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

Reliability assessment for multi-state automatic ticket vending machine (ATVM) through software and hardware failures

An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications

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