Efficient method using Whale Optimization Algorithm for reliability-based design optimization of labyrinth spillway

Jafari‐Asl, Jafar; Seghier, Mohamed El Amine Ben; Ohadi, Sima; Gelder, Pieter van

doi:10.1016/j.asoc.2020.107036

Cited by 44 publications

(14 citation statements)

References 36 publications

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“…Specific models have been created to handle RBDO as well, like the Kriging model, used to improve the computational efficiency by interpolating values through a Gaussian process governed by previous covariances [44], [45]. Other models involve a hybrid of various techniques to increase efficiency, such as combining the Monte Carlo Simulation (MCS) with a hybrid Artificial Neural Network (ANN) based Whale Optimization Algorithm (WOA) [46]. RBDO is utilized for a number of reasons, the first being that it allows for simulating and optimizing a scenario that would either be too costly [47], [48], would be too unsafe [49] or would be unfeasible [50] to run a physical model.…”

Section: List Of Figuresmentioning

confidence: 99%

Design optimization of a wind tunnel force balance using stepwise response surface method

Chaisson

Lee²,

Burns³

2022

J Mech Sci Technol

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applied to a body during a wind tunnel test. The balance is equipped with strain gauges that measure structural deformations for evaluation of the forces and moments being applied to the body. The force balance is required to have structural safety under a large roll moment, due to design changes with the testing apparatus. The balance must be optimized to allow for this increased roll moment while guaranteeing the balance will not fail and the gauges within the balance will generate an acceptable gauge reading.

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Section: List Of Figuresmentioning

confidence: 99%

Design optimization of a wind tunnel force balance using stepwise response surface method

Chaisson

Lee²,

Burns³

2022

J Mech Sci Technol

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“…For evaluating and validating the prediction effects of the improved ANN, the root mean squared error (RMSE), the mean absolute error (MAE), the MAPE, and the determination coefficient (R 2 ) are employed in this study. The four evaluation indexes are defined as 32 :

RMSE = \sqrt{\frac{1}{m} {false\sum}_{i = 1}^{m} {((), y_{i} goodbreak- {\overset{falsê}{y}}_{i})}^{2}}

MAE = \frac{1}{m} {false\sum}_{i = 1}^{m} |((), y_{i} goodbreak- {\overset{falsê}{y}}_{i})|

MAPE = \frac{1}{m} {false\sum}_{i = 1}^{m} \frac{(||, (y_{i} - {\overset{y}{true}}_{i}))}{y_{i}} \times 100 %

{normalR}^{2} = 1 - \frac{\underset{i}{false\sum} {((), {\overset{falsê}{y}}_{i} goodbreak- y_{i})}^{2}}{\underset{i}{false\sum} {((), {\overset{true¯}{y}}_{i} goodbreak- y_{i})}^{2}}

…”

Section: Optimizationmentioning

confidence: 99%

“…Finally, the errors between the output results (efficiency and cost rate) and the ideal results are calculated by the activation function. The activation function and the error function are given by Equations ( 31) and (32).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Energy, exergy, economy analysis, and multi‐objective optimization of a novel integrated energy system by combining artificial neural network and whale optimization algorithm

Bian

You

Zhao

et al. 2022

Intl J of Energy Research

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Summary This study proposed a novel solid oxide fuel cell (SOFC)‐integrated system fueled by biogas production from anaerobic fermentation of organic municipal solid wastes. The proposed system composed of the organic Rankine cycle (ORC), the anaerobic digester (AD), the SOFC, Kalina cycle (KC), and the parabolic trough solar collector (PTSC) which provided heat for the continuous and stable operation of anaerobic fermentation. Firstly, the mathematical model was established and then the energy, exergy, and economy analysis were evaluated. The results showed that the total exergy efficiency and the cost rate achieved 30.96% and 19.68$/h under design conditions. It was found that the total exergy efficiency increased as the increasing in the evaporating pressure of ORC and the basic ammonia solution concentration, but it decreased with the increase in the solar radiation intensity. When the input temperature and the current density of SOFC were increased, the total exergy efficiency was increased firstly then decreased and reached the maximum value of 439.2 and 481 kW at the SOFC input temperature of 628.6°C and the current density of 8286 A/m2. Besides, the cost rate was increased with the increase of the power consumption of the main components. The Pareto frontier was obtained by using the non‐dominated sorting whale optimization algorithm (NSWOA) which was employed to perform the multi‐objective optimization, and the comprehensive decision‐making method (TOPSIS) was used to get the optimal solution. The optimal solution showed that the total exergy efficiency and the cost rate could reach 39.56% and 14.23 $/h, respectively. Furthermore, in order to reduce optimization time and improve the accuracy of surrogate model, this study examined an improved artificial neural network (ANN) algorithm combining data‐driven surrogate model and whale optimization algorithm (WOA) to replace the physical model. It was also found that the time using the developed surrogate model to obtain the optimal solution set spent only 5 minutes which is far less than the physical method which spent more than 40 hours under the same computer configuration.

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“… 2020 ; Jafari-Asl et al. 2021 ; El-Fergany et al. 2019 ), it still suffers from the local minima and the low convergence speed.…”

Section: Introductionmentioning

confidence: 99%

A new fusion of whale optimizer algorithm with Kapur’s entropy for multi-threshold image segmentation: analysis and validations

2022

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The separation of an object from other objects or the background by selecting the optimal threshold values remains a challenge in the field of image segmentation. Threshold segmentation is one of the most popular image segmentation techniques. The traditional methods for finding the optimum threshold are computationally expensive, tedious, and may be inaccurate. Hence, this paper proposes an Improved Whale Optimization Algorithm (IWOA) based on Kapur’s entropy for solving multi-threshold segmentation of the gray level image. Also, IWOA supports its performance using linearly convergence increasing and local minima avoidance technique (LCMA), and ranking-based updating method (RUM). LCMA technique accelerates the convergence speed of the solutions toward the optimal solution and tries to avoid the local minima problem that may fall within the optimization process. To do that, it updates randomly the positions of the worst solutions to be near to the best solution and at the same time randomly within the search space according to a certain probability to avoid stuck into local minima. Because of the randomization process used in LCMA for updating the solutions toward the best solutions, a huge number of the solutions around the best are skipped. Therefore, the RUM is used to replace the unbeneficial solution with a novel updating scheme to cover this problem. We compare IWOA with another seven algorithms using a set of well-known test images. We use several performance measures, such as fitness values, Peak Signal to Noise Ratio, Structured Similarity Index Metric, Standard Deviation, and CPU time.

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Efficient method using Whale Optimization Algorithm for reliability-based design optimization of labyrinth spillway

Cited by 44 publications

References 36 publications

Design optimization of a wind tunnel force balance using stepwise response surface method

Design optimization of a wind tunnel force balance using stepwise response surface method

Energy, exergy, economy analysis, and multi‐objective optimization of a novel integrated energy system by combining artificial neural network and whale optimization algorithm

A new fusion of whale optimizer algorithm with Kapur’s entropy for multi-threshold image segmentation: analysis and validations

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