Whale Optimization Algorithm Optimization Research Articles

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

The performance and quality of steel products are significantly impacted by the alloying element control. The efficiency of alloy utilization in the steelmaking process was directly related to element yield. This study analyses the factors that influence the yield of elements in the steelmaking process using correlation analysis. A yield prediction model was developed using a t-distributed stochastic neighbor embedding (t-SNE) algorithm, a whale optimization algorithm (WOA), and a long short-term memory (LSTM) neural network. The t-SNE algorithm was used to reduce the dimensionality of the original data, while the WOA optimization algorithm was employed to optimize the hyperparameters of the LSTM neural network. The t-SNE-WOA-LSTM model accurately predicted the yield of Mn and Si elements with hit rates of 71.67%, 96.67%, and 99.17% and 57.50%, 89.17%, and 97.50%, respectively, falling within the error range of ±1%, ±2%, and ±3% for Mn and ±1%, ±3%, and ±5% for Si. The results demonstrate that the t-SNE-WOA-LSTM model outperforms the backpropagation (BP), LSTM, and WOA-LSTM models in terms of prediction accuracy. The model was applied to actual production in a Chinese plant. The actual performance of the industrial application is within a ±3% error range, with an accuracy of 100%. Furthermore, the elemental yield predicted by the model and then added the ferroalloys resulted in a reduction in the elemental content of the product by 0.017%. The model enables accurate prediction of alloying element yields and was effectively applied in industrial production.

Alleviation and control of chaotic oscillations in SMIB power systems using a modified-Whale optimization-based battery-STATCOM

BackgroundChaotic oscillations within the power system give rise to instability. While these oscillations may not have an immediate impact on the synchrony of the machine, they stimulate one of the oscillation modes, ultimately leading to voltage collapse and a loss of synchronism. ObjectiveThis paper introduces a modified Whale Optimization Algorithm (WOA)-based Battery-STATCOM (Static Synchronous Compensator) as a solution to mitigate chaotic oscillations within a Single Machine Infinite Bus (SMIB) system. MethodologyAn adaptable controller is implemented to manage the gate signal within the Battery-STATCOM. The AC-DC currents of this controller are optimally governed by two distinct WOA-tuned Proportional-Integral (PI) controllers. The battery storage unit serves as a robust voltage source, with the intelligent controller maintaining the DC-link voltage at the desired level. Test CasesAdditional disturbances, such as gradual variations in reference voltage and electromagnetic torque, are introduced to exacerbate chaotic oscillations. This is done to assess the controller's real-world performance under adverse conditions. Results and ConclusionUnder zero damping conditions, rotor parameters, including rise time, settling time, peak time, and overshoot, initially remain undefined due to uncontrolled oscillations. However, once the Battery-STATCOM is applied, these parameters are defined and achieve values (in seconds) of 0.90, 6.21, 1.71, and 21.10, respectively. After further optimization through the proposed modified WOA optimizer, the parameters reach values of 0.25, 1.01, 0.89, and 1.78, respectively. These results underscore the effectiveness of the proposed metaheuristic controller in suppressing overall chaotic oscillations within the power system.

Voting Classifier and Metaheuristic Optimization for Network Intrusion燚etection

Managing physical objects in the network’s periphery is made possible by the Internet of Things (IoT), revolutionizing human life. Open attacks and unauthorized access are possible with these IoT devices, which exchange data to enable remote access. These attacks are often detected using intrusion detection methodologies, although these systems’ effectiveness and accuracy are subpar. This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization. The employed metaheuristic optimizer is a new version of the whale optimization algorithm (WOA), which is guided by the dipper throated optimizer (DTO) to improve the exploration process ofthe traditional WOA optimizer. The proposed voting classifier categorizes the network intrusions robustly and efficiently. To assess the proposed approach, a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization. The dataset records are balanced using the locality-sensitive hashing (LSH) and Synthetic Minority Oversampling Technique (SMOTE). The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness, stability, and significance. The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

Exhaust emission reduction of a SI engine using acetone–gasoline fuel blends: Modeling, prediction, and whale optimization algorithm

Acetone–gasoline fuel is one potential favorable alternative option to regular gasoline fuel. This work attempts to mitigate pollution while simultaneously improving SI engine performance with acetone (AC) proportion in fuel mixture and varying engine speed. Acetone–gasoline alternative fuel was experimentally prepared by mixing 5–10 vol.% of acetone into ordinary gasoline. The experiments were performed in a spark ignition (SI) single-cylinder, four-stroke at variable engine speed. The engine performance was measured by an eddy current dynamometer (ECD), while the exhaust engine was measured by a gas analyzer connected to the gasoline engine. The proposed strategy, the integrated combined polynomial regression with whale optimization algorithm (WOA), was implemented. The results showed that the acetone blending of 10 % with gasoline (AC10: 10 vol. % acetone + 90 vol. % gasoline) decreases carbon monoxide (CO), unburned hydrocarbon (UHC), and nitrogen oxides (NOx), emissions by 26.3%, 30.3%, and 6.6%, respectively. Furthermore, the AC10 exhibited better engine brake power (BP) than pure gasoline, with an improvement of 4.39%. According to the optimization results, the BP was enhanced by AC10 to record 2.426 kW at 2887 rpm. In addition, engine emissions reduced to the lowest levels of 142.849 ppm, 0.426%, and 1088.178 ppm, in terms of UHC, CO, and NOx, respectively. The experiment results show that the WOA optimizer can accurately predict the optimal point in SI engine performance and emissions.

GMM Clustering Based on WOA Optimization and Space-Time Coupled Urban Rail Traffic Flow Prediction by CEEMD-SE-BiGRU-AM

Accurately predicting the short-term passenger flow of urban subways is very important for urban subway stations to formulate passenger flow organization and evacuation plans effectively and rationally plan passenger travel routes. This paper establishes a novel framework to predict the hourly inbound and outbound passenger flow of subway stations based on WOA-GMM station classifiers, CEEMD-SE noise reduction, and BiGRU optimized by attention. Firstly, this paper classifies subway stations using the improved Gaussian mixture model (GMM) with Whale Optimization Algorithm (WOA) to realize the feature extraction of different types of subway stations. Secondly, this paper uses the Complementary Ensemble Empirical Mode Decomposition (CEEMD) to decompose the noise reduction of each station’s hourly inbound and outbound passenger flow. It combines the empirical modal components by calculating the sample entropy (SE), which makes the time series stable and reduces the time cost of forecasting. Finally, a Bidirectional Gated Recurrent Unit (BiGRU) model improved by attention mechanism (AM) is established for each station’s inbound and outbound passenger flow. The prediction model established in this paper is verified by subway passenger flow data in Shanghai, China, within 24 days. Finally, it is concluded that the model can predict the passenger flow of subway stations. Compared with the traditional Backpropagation Neural Network (BP), the Long Short-Term Memory (LSTM), and the normal BiGRU model, the model proposed in this paper has an average reduction of 65.90%, 64.54%, and 49.06% in Mean Absolute Percentage Error (MAPE) in the prediction of the hourly inbound and outbound passenger flow of each type of station, respectively.

Modeling and simulation of an optimal unified tank model for aeration-sedimentation processes in wastewater treatment plants

The operation of Wastewater Treatment Plants (WWTPs) depend not only on the influent characteristics and air blown into the tank, but also the design of the tank structure. In this paper, the mathematical model of the aeration tank is derived by the dynamic equations of physical and biological processes. It is aimed to obtain an effective alternative model for secondary treatment. In the conventional approach, two separate tanks are used for aeration and sedimentation processes. A Unified Tank Model (UTM) for both phases is proposed and investigated in this research. To reach the desired optimized solution, a recently introduced metaheuristic method based on whale feeding behavior, named as Whale Optimization Algorithm (WOA) is used. The dynamic model of real system behavior in the proposed conditions is observed by computer simulation. The results are examined and discussed in the cases of the conventional approach and proposed alternative model. The optimization problems based on aeration system and dissolved oxygen (DO) concentration are defined to find optimal location of the diffusers in tank which is solved using whale optimization method. The optimal design shows better DO performance compared to the conventional approach. The aeration duration and accuracy of DO variation in the proposed UTM design with WOA optimization are shown to be significantly improved. The results of optimal WOA solution in UTM design present a useful alternative with good accuracy in following the desired DO value.

Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm

Wind power as a renewable source of energy has numerous economic, environmental, and social benefits. To enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) was used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh, and Deylaman) to increase the accuracy of the subsequent hybrid model. The capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP optimized by the Genetic Algorithm (MLP-GA) and standalone MLP without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the MLP-GA and standalone MLP model. In other words, the hybrid MLP-WOA models, with acceptable performances, reduced the RMSE values from 0.570∼2.995 to 0523∼2.751. Also, the obtained results indicated that the examined MLP-GA did not have a significant effect in increasing the estimation accuracy of standalone MLP models. It was concluded that the WOA optimization algorithm could improve the prediction accuracy of the MLP model and may be recommended for accurate wind speed prediction.

Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete

Due to the important role of concrete in construction sector, a novel metaheuristic method, namely whale optimization algorithm (WOA), is employed for simulating 28-day compressive strength of concrete (CSC). To this end, the WOA is coupled with a neural network (NN) to optimize its computational parameters. Also, dragonfly algorithm (DA) and ant colony optimization (ACO) techniques are considered as the benchmark methods. The CSC influential parameters are cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA). First, a population-based sensitivity analysis is carried out to achieve the most efficient structure of the proposed model. In this sense, the WOA-NN with the population size of 400 and five hidden nodes constructed the best-fitted network. The results revealed that the WOA-NN (Error = 2.0746 and Correlation = 0.8976) presents the most reliable prediction of the CSC, followed by the DA-NN (Error = 2.5138 and Correlation = 0.8209) and ACO-NN (Error = 2.8843 and Correlation = 0.8000) benchmark models. The findings showed that utilizing the WOA optimization technique, along with typical neural network, results in developing a promising tool for modeling the CSC.

Optimal power flow control of hybrid renewable energy system with energy storage: A WOANN strategy

This paper proposed an optimal control technique for power flow control of hybrid renewable energy systems (HRESs) like a combined photovoltaic and wind turbine system with energy storage. The proposed optimal control technique is the joined execution of both the whale optimization algorithm (WOA) and the artificial neural network (ANN). Here, the ANN learning process has been enhanced by utilizing the WOA optimization process with respect to the minimum error objective function and named as WOANN. The proposed WOANN predicts the required control gain parameters of the HRES to maintain the power flow, based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy source accessibility, storage element state of charge, and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. The proposed technique is implemented in the MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques.

Whale Optimization Algorithm Based Technique for Distributed Generation Installation in Distribution System

This paper presents Whale Optimization Algorithm (WOA) Based Technique for Distributed Generation Installation in Transmission System. In this study, WOA optimization engine is developed for the installation of Distributed Generation (DG). Prior to the optimization process, a pre-developed voltage stability index termed Fast Voltage Stability Index (FVSI) was used as an indicator to identify the location for the DG to be installed in the system. Meanwhile, for sizing the DG WOA is employed to identify the optimal sizing. By installing DG in the transmission system, voltage stability and voltage profile can be improved, while power losses can be minimized. The proposed algorithm was tested on 30-bus radial distribution network. Results obtained from the EP were compared with firefly algorithm (FA); indicating better results. This highlights the strength of WOA over FA in terms of minimizing total losses.

Chaotic whale optimization algorithm

AbstractThe Whale Optimization Algorithm (WOA) is a recently developed meta-heuristic optimization algorithm which is based on the hunting mechanism of humpback whales. Similarly to other meta-heuristic algorithms, the main problem faced by WOA is slow convergence speed. So to enhance the global convergence speed and to get better performance, this paper introduces chaos theory into WOA optimization process. Various chaotic maps are considered in the proposed chaotic WOA (CWOA) methods for tuning the main parameter of WOA which helps in controlling exploration and exploitation. The proposed CWOA methods are benchmarked on twenty well-known test functions. The results prove that the chaotic maps (especially Tent map) are able to improve the performance of WOA.Highlights Chaos has been introduced into WOA to improve its performance. Ten chaotic maps have been investigated to tune the key parameter ‘ p’ of WOA. The proposed CWOA is validated on a set of twenty benchmark functions. The proposed CWOA is validated on a set of twenty benchmark functions. Statistical results suggest that CWOA has better reliability of global optimality.

Novel Adaptive Whale Optimization Algorithm for Global Optimization

Background/Objectives: In the meta-heuristic algorithms, randomization plays a very crucial role in both exploration and exploitation. So meta-heuristic algorithms are proposed to avoid these problems. Methods/Statistical Analysis: A novel bio-inspired optimization algorithm based on the special bubble-net hunting strategy used by humpback whales called the Whale Optimization Algorithm (WOA). In contrast to meta-heuristic, main feature is randomization having a relevant role in both exploration and exploitation in optimization problem. A novel randomization technique termed adaptive technique is integrated with WOA and exercised on ten unconstraint test benchmark function. Findings: WOA algorithm has quality feature that it uses logarithmic spiral function so it covers a broader area in exploration phase then addition with powerful randomization adaptive technique potent the adaptive whale optimization Algorithm (AWOA) to attain global optimal solution and faster convergence with less parameter dependency. Application/Improvements: Adaptive WOA (AWOA) solutions are evaluated and results shows its competitively better performance over standard WOA optimization algorithm.