Fuzzy multiobjective system reliability optimization by genetic algorithms and clustering analysis

Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Adjerid, Smail

doi:10.1002/qre.2809

Cited by 14 publications

(6 citation statements)

References 42 publications

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“…Muhuri et al [21] constructed an interval type-2 fuzzy reliability of the component-based model for MORRAP, which was solved using the KM and NSGA-II methods. Chebouba et al [58] solve the multiobjective system reliability of fuzzy quantities using the non-sorting genetic algorithms (NSGA-III). To describe the series-parallel and parallel-series systems, Ashraf et al [59] presented an IT2 Fuzzy membership function, EKM and PSO were used to solve the formulated interval type-2 fuzzy MORRAPs, and the outcomes were compared to GA.…”

Section: B Reliability Optimizationmentioning

confidence: 99%

A Generalized Multiobjective Reliability Redundancy Allocation With Uncertainties

et al. 2023

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Multi-objective reliability-redundancy allocation problem (MORRAP) needs to maximize system reliability and minimize cost, weight, and volume with underlining constraints. In the systems' design and analysis phase, uncertainties can occur from various sources, such as manufacturing variability, environmental conditions, user behavior, etc. To deal with this, we present a generalization of the traditional MORRAP under multiple empirical and ambiguous circumstances, named interval type-2 fuzzy multiobjective reliability redundancy allocation problem (IT2FMORRAP). The newly formulated IT2FMORRAP considers optimizing goals as reliability, cost, and weight for a series-parallel system with interval type-2 fuzzy number. The mathematical formulation is established under which the proposed IT2FMORRAP model reduces to T1FMORRAP (type-1 fuzzy MORRAP), IVMORRAP (interval-valued MORRAP), and classical MORRAP. An Enhanced Karnik-Mendel and NSGA-II algorithm-based solving strategy is developed for the proposed IT2FMORRAP. The real-world dataset is considered to demonstrate the efficacy of the solution method for the proposed problem. A Kmean clustering technique identifies the best solution sets from the knee region of the generated Pareto fronts. An experimental study on commonly used performance metrics reveals that IT2FMORRAP performs significantly better than T1FMORRAP and crisp MORRAP. Further, the statistical analysis also confirms the hypothesis established in the empirical research. Finally, a comparative performance study has been conducted with notable state-of-the-art papers from the literature to encounter an appropriate establishment for the proposed work in the domain.

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Section: B Reliability Optimizationmentioning

confidence: 99%

A Generalized Multiobjective Reliability Redundancy Allocation With Uncertainties

et al. 2023

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“…However, most methods use a defuzzification procedure to convert fuzzy numbers into crisp numbers before inputting them into the reliability models. [28][29][30][31][32] Therefore, the reliability analysis is based on crisp values and has not taken into account the initial fuzzy information. Even though it is a productive strategy, the fuzzy information is left out after the defuzzification procedure.…”

Section: Introductionmentioning

confidence: 99%

“…The utilization of fuzzy logic considers practical situations and provides more explanatory and comprehensive reliability analysis results for safety critical systems. However, most methods use a defuzzification procedure to convert fuzzy numbers into crisp numbers before inputting them into the reliability models 28–32 . Therefore, the reliability analysis is based on crisp values and has not taken into account the initial fuzzy information.…”

Section: Introductionmentioning

confidence: 99%

A reliability analysis technique for complex systems with retaining fuzzy information

Zeng

Sun

2023

Quality & Reliability Eng

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This paper proposed a method for the reliability analysis of systems characterized by fuzzy failure probabilities and intricate failure behaviors, while retaining the fuzzy information throughout the analysis process. Specifically, we introduced a combinational modeling method that integrates generalized stochastic Petri nets (GSPN) and dynamic fault trees (DFT) to capture the dynamic failure behaviors and address the limitations of DFT modeling. This combinational approach is capable of capturing more sophisticated failure behaviors, such as competing failures and global failure propagation patterns. Furthermore, we propose an Monte Carlo technique based on endpoint sampling to enable quantitative analysis of GSPN‐based composite models, which preserves the fuzzy fault information of the system. Finally, we demonstrate the effectiveness of the proposed method through an example of a flight control system.

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“…These are modeled on the way artificial bees collect food. The clustering methods based on Genetic Algorithms (GA) proposed in [18,19,20] are motivated by biological processes as crossing, mutation, and inheritance.…”

Section: Introductionmentioning

confidence: 99%

Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data Clustering

Sahoo¹,

Pattanaik²,

Mohanty³

et al. 2023

IJACSA

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Clustering of data in case of data mining has a major role in recent research as well as data engineers. It supports for classification and regression type of problems. It needs to obtain the optimized clusters for such application. The partitional clustering and meta-heuristic search techniques are two helpful tools for this task. However the convergence rate is one of the important factors at the time of optimization. In this paper, authors have taken a data clustering approach with improved bee colony algorithm and opposition based learning to improve the rate of convergence and quality of clustering. It introduces the opposite bees that are created using opposition based learning to achieve better exploration. These opposite bees occupy exactly the opposite position that of the mainstream bees in the solution space. Both the mainstream and opposite bees explore the solution space together with the help of Bee Colony Optimization based clustering algorithm. This boosts the explorative power of the algorithm and hence the convergence rate. The algorithm uses a steady state selection procedure as a tool for exploration. The crossover and mutation operation is used to get balanced exploitations. This enables the algorithm to avoid sticking in local optima. To justify the effectiveness of the algorithm it is verified with the open datasets from the UCI machine learning repository as the benchmark. The simulation result shows that it performs better than some benchmark as well as recently proposed algorithms in terms of convergence rate, clustering quality, and exploration and exploitation capability.

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Fuzzy multiobjective system reliability optimization by genetic algorithms and clustering analysis

Cited by 14 publications

References 42 publications

A Generalized Multiobjective Reliability Redundancy Allocation With Uncertainties

A Generalized Multiobjective Reliability Redundancy Allocation With Uncertainties

A reliability analysis technique for complex systems with retaining fuzzy information

Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data Clustering

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