Enhanced sequential optimization and reliability assessment method for probabilistic optimization with varying design variance

Yin, Xiaolei; Chen, Wei

doi:10.1080/15732470600590317

Cited by 33 publications

(19 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As it can be seen the difference between the worst, the median and the best solution is negligible. The comparison with the results of [70] which are obtained by an enhanced SORA method shows that dBA converges to a better solution. Table 10 presents the rate of constraint violations, the reliability index and the probability of failure of each constraint computed with a different methods, namely, FORM [38,39], two variants of SORM (Breitung [40] and Tevedt [41]), and Monte Carlo Simulation (MCS) of the best solution.…”

Section: Example 3: Speed Reducer Designmentioning

confidence: 89%

“…The constraint that has the 19 probability of failure is equal to 0, means that the real Pf is less than 10 -10 . From the analysis of probability of failure of the probabilistic constraints and the violation of the deterministic constraint, we believe that the best solution presented in Table 9 is the best known solution so far of this example in the form proposed by [70].…”

Section: Example 3: Speed Reducer Designmentioning

confidence: 96%

“…Many studies [67][68][69] that treated this example considered that all design variables are random with a fixed variance, and the constraints are all probabilistic. We prefer to use the formulation proposed by [70] because of the following reasons: 1) Two of the design variables are deterministic, while the rest are random; 2) The design variables variances are varying (change in the optimization loop and depends on the mean value); 3) Mixed types of constraints, ten are probabilistic and one deterministic. These three points increase the complexity and the nonlinearity of the problem.…”

Section: Example 3: Speed Reducer Designmentioning

confidence: 99%

See 2 more Smart Citations

Reliability-based design optimization using the directional bat algorithm

Chakri

Yang

Khelif

et al. 2017

Neural Comput & Applic

View full text Add to dashboard Cite

Reliability based design optimization (RBDO) problems are important in engineering applications, but it is challenging to solve such problems. In this study, a new resolution method based on the directional Bat Algorithm (dBA) is presented. To overcome the difficulties in the evaluations of probabilistic constraints, the reliable design space concept has been applied to convert the yielded stochastic constrained optimization problem from the RBDO formulation into a deterministic constrained optimization problem. In addition, the constraint handling technique has also been introduced to the dBA so that the algorithm can solve constrained optimization problem effectively. The new method has been applied to several engineering problems and the results show that the new method can solve different varieties of RBDO problems efficiently. In fact, the obtained solutions are consistent with the best results in the literature. Citation details: A. Chakri, X.-S. Yang, R. Khelif, M. Becouaret, Reliability based design optimization using the directional bat algorithm, Neural Computing and Applications, First published online, 2017. https://doi.org/10.1007/s00521-016-2797-3 3the time delay between the two ears, they can create a 3D mental image of their surrounding and determine if there is food or not. This behavior was the basic idea that has been used to develop the bat algorithm. Several studies showed that BA can solve optimization problems with higher efficiency compared to standard algorithms such as .Despite the fact that BA is a powerful optimization algorithm, it may suffer from the premature convergence that can occur under certain conditions, which is also true for all other algorithms such as PSO and GA. To overcome this problem, several techniques have been proposed to increase the exploitation and exploration capability of the algorithm. In [23], the authors proposed to use simulated annealing and Gaussian perturbation to speed up the convergence rate. In [24], the authors suggested to use chaotic maps to control the pulse rate and loudness. In [25], the authors recommended to use the Lévy flights and the differential operator to generate the bats ' movements and, in [26], the authors proposed to consider the bats' habitat selection and their self-adaptive compensation for the Doppler effect in the algorithm formulation. Other studies suggested hybridization between the standard BA and classical algorithm such as PSO [27], Artificial Bee Colony (ABC) [28], differential evolution [29,30] and Invasive Weed Optimization (IWO) [31].

show abstract

Section: Example 3: Speed Reducer Designmentioning

confidence: 89%

Section: Example 3: Speed Reducer Designmentioning

confidence: 96%

Section: Example 3: Speed Reducer Designmentioning

confidence: 99%

See 1 more Smart Citation

Reliability-based design optimization using the directional bat algorithm

Chakri

Yang

Khelif

et al. 2017

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…It is worth mentioning that a normal distribution is adopted for the design variables. According to Yin and Chen (2006), this type of distribution is commonly used in engineering applications to describe the linear relationship between the changing variance and the mean value; i.e., σ x =r x μ x , where μ x and σ x represent the mean and the standard deviation, respectively, and r x is the variation coefficient. Rosenblatt (1952) claims that to deal with non-normal distributions, it is necessary to convert the mean and the standard deviation of this type of distribution to an equivalent value with normal distribution.…”

Section: Methodsmentioning

confidence: 99%

Reliability-Based Multi-Objective Optimization Applied to Chemical Engineering Design

Lobato

Silva

Cavalini

et al. 2019

Braz. J. Chem. Eng.

View full text Add to dashboard Cite

Chemical engineering optimization represents a significant challenge due to the complexity of the mathematical models that are frequently required in this area. These models are normally associated with nonlinear equations that represent mass, energy, and momentum balances, which are submitted to physical, constitutive, environmental, and design limitations. The design of chemical systems is generally carried out by considering the model, the vector of design variables, and system parameters as deterministic values, i.e., small variations in these quantities do not affect the objective function. In this contribution, a new methodology based on a double loop iteration process to evaluate the influence of uncertainties on chemical engineering design is proposed. The inner optimization loop is used to find the solution associated with the highest probability value by using the so-called Inverse Reliability Analysis and the outer loop is the regular optimization loop used to determine the vector of design variables. For this aim, the Multi-Objective Optimization Water Cycle Algorithm is improved, adopting a mechanism of neighborhood exploration. For illustration purposes, the proposed methodology is applied to mathematical functions and to chemical engineering design. The obtained results demonstrate that the proposed strategy represents an interesting alternative to reliability design in chemical engineering.

show abstract

“…This decision and optimization model is often called MCDM problem. In a recent paper [8], the authors have provided a reliability assessment method to improve the efficiency for solving problem of probabilistic optimization with changing variance. In order to improve the accuracy of nonlinear and multi-dimensional performance functions, Lee et al [9] proposed an inverse reliability analysis method was applied to improve the accurate probability of failure calculation for reliability design optimization.…”

Section: Introductionmentioning

confidence: 99%

ELECTRE I Decision Model of Reliability Design Scheme for Computer Numerical Control Machine

Pang¹,

Zhang²,

Chen³

2011

JSW

View full text Add to dashboard Cite

The ELECTRE I is one of the most extensively used methods to solve multiple criteria decision making (MCDM) problems. In this paper, we propose a novel AHP-based ELECTRE I method of reliability design scheme decision for computer numerical control (CNC) machine. Based on the AHP method combined with ELECTRE I, the decision model is built to select the optimal design scheme. The AHP method is applied to determinate the weights of reliability design factors through the decision model. ELECTRE I method is then designed to rank reliability design scheme in order of decision maker’s preference. To evaluate performance of the developed algorithm, an illustrative example of CNC machine is given. The computational results show that the proposed approach is reliable and performs well

show abstract

Enhanced sequential optimization and reliability assessment method for probabilistic optimization with varying design variance

Cited by 33 publications

References 27 publications

Reliability-based design optimization using the directional bat algorithm

Reliability-based design optimization using the directional bat algorithm

Reliability-Based Multi-Objective Optimization Applied to Chemical Engineering Design

ELECTRE I Decision Model of Reliability Design Scheme for Computer Numerical Control Machine

Contact Info

Product

Resources

About