Fuzzy reliability redundancy optimisation with signed distance method for defuzzification using genetic algorithm

Mahato, Sanat Kumar; Bhattacharyee, Nabaranjan; Pramanik, Rajesh

doi:10.1504/ijor.2020.105441

Cited by 13 publications

(5 citation statements)

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“…In the process of multidisciplinary optimization, an evaluation function is constructed to convert the multidisciplinary optimization problem into a single-objective optimization problem. As shown in (1), it is a multidisciplinary optimization model:…”

Section: Interval Optimization Model Based On Evaluationmentioning

confidence: 99%

“…Te traditional equipment and systems are usually constructed by deterministic optimization model and solved by traditional method such as the probability method. However, in the practice process, there are various uncertain factors such as material characteristics, manufacturing errors, and operating environment [1][2][3][4]. Dealing with the needs of modern equipment and systems, it is urgent to further research the theory and method of uncertain design optimization.…”

Section: Introductionmentioning

confidence: 99%

“…According to the numerical analysis method to solve the double-nested interval optimization problem, there are two problems as follows. (1) In general, the inner layer conducts uncertainty analysis while the outer layer conducts design variable solving. Te outer layer will call the inner layer, solving cycle dozens or even hundreds of times at a time, resulting in an exponential increase in computation, which often takes a long time and afects efciency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Reliability-Based Multidisciplinary Design Optimization Strategy considering Interval Uncertainty Based on the Point-Infilled Kriging Model

Yuan

Zhang

Sun

et al. 2022

Mathematical Problems in Engineering

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Generally, traditional uncertainty design optimization (UDO) methods are based on probability density distribution function or fuzzy membership function. In this situation, a large amount of uncertain information is necessary to construct the UDO model accurately. While, the interval UDO methods require less design information. Only the upper and lower bounds of interval uncertainty are utilized to construct the optimization model. In this study, to enhance the efficiency and accuracy of UBDO considering interval uncertainty, a reliability-based multidisciplinary design optimization (RBMDO) strategy using the point-infilled Kriging model is proposed. In the given method, a double-nested RBMDO model considering interval uncertainty is established. The collaborative optimization is utilized to deal with coupling relationships among complex systems. Then, the point-infilled Kriging response surface strategy is introduced to approximate the RBMDO model. The procedure of the interval multidisciplinary collaborative optimization method based on the Kriging model is discussed. Two examples are given to illustrate the application of the proposed method.

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Section: Interval Optimization Model Based On Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Reliability-Based Multidisciplinary Design Optimization Strategy considering Interval Uncertainty Based on the Point-Infilled Kriging Model

Yuan

Zhang

Sun

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…However, when dealing with complex optimization problems, it was difficult for traditional methods to obtain accurate analytical expressions, which limited the applications of these methods. In recent years, with the development of modern computing technologies, multidisciplinary design optimization methods, 14 swarm intelligent optimization algorithms, 15,16 are all widely used in reliability optimization. As a typical heuristic random search swarm intelligence algorithm, particle swarm optimization (PSO) algorithm is widely used in all kinds of engineering practice, which has the characteristics of clear and understandable biological interpretation, simple mathematical expression and less adjustable parameters 17 .…”

Section: Introductionmentioning

confidence: 99%

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Zheng

Zhang

et al. 2023

Quality & Reliability Eng

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In order to improve the global optimization of particle swarm optimization (PSO), enhance the performance of PSO in dealing with these complex, highdimensional, multimodal optimization problems, and furthermore, promote the optimization effect in reliability optimization applications, a direct position updating-based trying-mutation PSO (DTPSO) was proposed. In this algorithm, the direct position updating strategy and trying-mutation strategy were designed, which could effectively maintain the population diversity, balance the exploitation and exploration, and increase the probability of obtaining global optimal solution. After being verified and compared by nine complex test functions, the rationality of algorithm design and the excellent global optimization performances were proved. In the reliability optimization, the reliability redundancy allocation and reliability allocation were optimized by different advanced improved algorithms, the comparison results proved the stability and optimization performance of DTPSO. Due to its stable and efficient global optimization performance, it was concluded the proposed DTPSO could provide effective technical support for practical applications such as reliability optimization.

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“…In recent studies shows that Mahato S.K. et al [14] discussed the optimization of a complex network system. They use trapezoidal fuzzy number in the place of parameters and then use signed distance method for defuzzification.…”

Section: Introductionmentioning

confidence: 99%

FEOQ model with octagonal fuzzy demand rate and optimize with signed distance method

Rajput¹,

Chauhan

Goel

et al. 2022

YUGOSLAV J OPERATION

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Uncertainties in the stock control system development is one of the main concerns of researchers, several studies has been done in this research field with different fuzzy numbers by using different defuzzification methods. In this present article, at first, we have studied the scenario of increasing demand rate with the time and shortage are fully backlogged. Due to some reasons, there is flexible(uncertain) demand rate for the production, so the demand rate has to be taken as octagonal fuzzy number and the model converted into fuzzy model. The purpose of presented model is to optimize the total inventory cost in both the crisp and the fuzzy situations. The designated model included with Signed distance method for an appropriate numerical example, sensitivity analysis and graphical illustration for better explanation of the article.

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Fuzzy reliability redundancy optimisation with signed distance method for defuzzification using genetic algorithm

Cited by 13 publications

References 0 publications

A Reliability-Based Multidisciplinary Design Optimization Strategy considering Interval Uncertainty Based on the Point-Infilled Kriging Model

A Reliability-Based Multidisciplinary Design Optimization Strategy considering Interval Uncertainty Based on the Point-Infilled Kriging Model

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

FEOQ model with octagonal fuzzy demand rate and optimize with signed distance method

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