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Design and Performance Analysis of walrus optimization algorithm (WaOA)-Based Cascade Controller for Load Frequency Control of a Multi-Area Power System With Renewable Sources

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2025年第2期38卷

作者： Hussain, Jahanzeab Zou, Runmin Wu, Zhenlong Pathak, Pawan Kumar Akhtar, Samina Cent South Univ Sch Automat Changsha Hunan Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou Peoples R China Banasthali Vidyapith Sch Automat Radha Kishnpura Rajasthan India Wuhan Univ Sch Elect Engn & Automation Wuhan Hubei Peoples R China

One of the key challenges in interconnected power systems is developing an effective control strategy to mitigate frequency and power deviations caused by the intermittency of renewable energy sources (RESs) and varying load demands. This research introduces an innovative cascade control strategy featuring a PPD controller followed by a PI controller (PPD-PI) for load frequency control (LFC) in a two-area power system with photovoltaic (PV), wind, and thermal reheat power sources. The walrus optimization algorithm (WaOA) is employed to fine-tune the parameters of both the PIDn and PPD-PI controllers, with the goal of minimizing the integral time absolute error (ITAE). The study first applies the WaOA-tuned PID with filter (PIDn) controller to showcase WaOA's effectiveness in LFC, achieving the lowest objective function value of 0.3862, surpassing MFO (0.3921) and GA (0.4127). The robustness of the WaOA-tuned PPD-PI controller is then evaluated under various conditions, including step load disturbances, random load patterns, and parameter uncertainties. The proposed controller achieves significant improvements, with a 36.8% reduction in ITAE compared to the second-best CGO-tuned PIDn-PI controller in Case 2, and a 54.45% reduction in ITAE compared to the second-best COA-tuned PDn-PI controller in Case 3. To further highlight the advantages of the proposed scheme, the analysis also includes nonlinearities such as governor dead band (GDB), boiler dynamics (BD), and generation rate constraints (GRC), along with sensitivity analysis and stability testing under a +/- 25%$$ \pm 25\% $$ change in system parameters. The results strongly demonstrate the superior performance of the WaOA-optimized PPD-PI controller over existing methods.

关键词： cascade PPD-PI controller load frequency control power system renewable energy walrus optimization algorithm

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An enhanced walrus optimization algorithm for flexible job shop scheduling with parallel batch processing operation

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SCIENTIFIC REPORTS 2025年第1期15卷 1-33页

作者： Lv, Shengping Zhuang, Jianwei Li, Zhuohui Zhang, Hucheng Jin, Hong Lu, Shengxiang South China Agr Univ Sch Engn Guangzhou 510642 Peoples R China Hunan Univ Finance & Econ Sch Math & Stat Changsha 410205 Peoples R China

The flexible job shop scheduling problem with parallel batch processing operation (FJSP_PBPO) in this study is motivated by real-world scenarios observed in electronic product testing workshops. This research aims to tackle the deficiency of effective methods, particularly global scheduling metaheuristics, for FJSP_PBPO. We establish an optimization model utilizing mixed-integer programming to minimize makespan and introduce an enhanced walrus optimization algorithm (WaOA) for efficiently solving the FJSP_PBPO. Key innovations of our approach include novel encoding, conversion, inverse conversion, and decoding schemes tailored to the constraints of FJSP_PBPO, a random optimal matching initialization (ROMI) strategy for generating diverse and high-quality initial solutions, as well as modifications to the original feeding, migration, and fleeing strategies of WaOA, along with the introduction of a novel gathering strategy. Our approach significantly improves solution quality and optimization efficiency for FJSP_PBPO, as demonstrated through comparative analysis with four enhanced WaOA variants, eleven state-of-the-art algorithms, and validation across 30 test instances and a real-world engineering case.

关键词： Flexible job shop scheduling Parallel batch processing operations walrus optimization algorithm Makespan

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Ensemble of deep learning models with walrus optimization algorithm for accurate botnet recognition and classification

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Iran Journal of Computer Science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the walrus optimization algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things walrus optimization algorithm

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Inverse Analysis of Thermal Parameters of Arch Dam Concrete Based on walrus optimization algorithm

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APPLIED SCIENCES-BASEL 2025年第4期15卷 2155-2155页

作者： Wang, Youle Miao, Zhengjian Song, Rui Zhou, Junchi Pan, Yuheng Wang, Feng Changjiang Survey Planning Design & Res Co Ltd Wuhan 430010 Peoples R China State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China Natl Dam Safety Res Ctr Wuhan 430010 Peoples R China China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Peoples R China

In the simulation of concrete thermal stress fields, thermal parameters are crucial for calculating the concrete temperature field. In actual construction, due to the adjustment of the concrete mixing ratio and the changing external environment (temperature fluctuations, cooling conditions, solar radiation, thermal insulation measures, etc.), there are significant differences between the thermal parameters obtained in tests and the actual working conditions, which affect the simulation accuracy. Therefore, the inverse analysis of concrete thermal parameters under real working conditions can be carried out based on the measured temperature data. A method for inverse analysis of thermal parameters of arch dams using the walrus optimization algorithm (WaOA) is proposed. To verify the accuracy of the inversion parameters, twelve classical test functions are used to compare the three algorithms to evaluate their fitness. The efficiency difference is analyzed by nonparametric methods such as Fredman and Wilcoxon rank sum test. The results consistently indicate that the walrus optimization algorithm performs better. Furthermore, the WaOA is utilized for the parameter inversion of an arch dam in the downstream area of the Jinsha River. We bring the inversion results into different dam sections to calculate the temperature field during construction, which effectively verifies the efficient solution ability of the WaOA for the inverse analysis of concrete thermal parameters under complex engineering backgrounds.

关键词： parameter inversion walrus optimization algorithm massive concrete finite element method circulating water cooling

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Application of the Multi-Strategy Improved walrus optimization algorithm in Mobile Robot Path Planning

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IEEE ACCESS 2024年 12卷 184216-184229页

作者： Ke, Yongfu Shi, Limei Ji, Weinan Luo, Peng Guo, Lei Guangdong Ocean Univ Sch Elect & Informat Engn Zhanjiang 524088 Peoples R China

With the widespread application of mobile robotics technology, path planning has increasingly become a research hotspot. In complex environments, planning an efficient, stable, and safe path is an urgent problem that needs to be addressed. To this end, this paper proposes the Improved walrus optimization algorithm (IWaOA) and applies it to path planning. Firstly, the Sine-Tent-Cosine chaotic mapping is used to initialize the walrus population, addressing the issue of insufficient population diversity in the later stages of the algorithm's iteration. Next, two improvement strategies are proposed: optimal value-enhanced random walk and directional evolutionary mutation. These strategies aim to enhance the algorithm's local search capability and precision, optimizing the issues of the original algorithm's proneness to falling into local optima and slow convergence speed. Finally, building on the three stages of the walrus optimization algorithm (WaOA), this paper introduces a fourth stage termed the "Hunting Stage" to the original algorithm with historical experience positions. It's capable of significantly improving the overall performance of the algorithm. Evaluating the performance of the proposed algorithm, this paper conducts experiments with three distinct sets of benchmark functions and compares the outcomes against various swarm intelligence algorithms. Furthermore, the IWaOA was applied to the path planning problem for mobile robots. The experimental results confirm the efficacy and advantage of the IWaOA compared to the traditional WaOA, demonstrating a decrease in path length by 16.7%, 3.7%, and 6.2% across three different map scenarios.

关键词： Path planning optimization Robots Particle swarm optimization Mobile robots Planning Convergence Heuristic algorithms Prediction algorithms Search problems Mobile robot path planning sine-tent-cosine chaotic mapping walrus optimization algorithm

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Multi-level Segmentation of Chilli Images Driven by walrus optimization algorithm with Two Strategies 20th

Multi-level Segmentation of Chilli Images Driven by Walrus O...

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20th International Conference on Intelligent Computing (ICIC)

作者： Ye, Chen Shao, Peng Zhang, Shaoping Jiangxi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Jiangxi Peoples R China

ISBN: (纸本)9789819755776;9789819755783

High-precision image segmentation is beneficial for meeting the requirements of precision agricultural management. In this regard, an enhanced walrus optimization algorithm (LE-WaOA) is proposed, combined with minimum cross-entropy method for multi-level segmentation of chilli images. LE-WaOA integrates lifespan-based Levy flight and elite group genetic strategy, enhancing optimization convergence and accuracy. The smaller the cross-entropy, the more refined the segmentation of the chili pepper images. By minimizing the cross-entropy between the segmented and original images, LE-WaOA aims to find the optimal set of threshold combinations for the highest segmentation accuracy. The smaller the cross-entropy, the more detailed segmentation the chilli images present. Comparative experiments on CEC2017 and real chilli images demonstrate the superiority of LE-WaOA over DE, CMAES, and other metaheuristic algorithms. LE-WaOA achieves the lowest cross-entropy and performs excellently in the peak signal-to-noise ratio evaluation metric.

关键词： Multi-level threshold segmentation walrus optimization algorithm Lifespan-based Levy flight Elite group genetic strategy

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Borehole Breakout Prediction Based on Multi-Output Machine Learning Models Using the walrus optimization algorithm

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APPLIED SCIENCES-BASEL 2024年第14期14卷 6164页

作者： Zhang, Rui Zhou, Jian Tao, Ming Li, Chuanqi Li, Pingfeng Liu, Taoying Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Grenoble Alpes Univ Lab 3SR CNRS UMR 5521 F-38000 Grenoble France Hongda Blasting Engn Grp Co Ltd Guangzhou 510623 Peoples R China

Borehole breakouts significantly influence drilling operations' efficiency and economics. Accurate evaluation of breakout size (angle and depth) can enhance drilling strategies and hold potential for in situ stress magnitude inversion. In this study, borehole breakout size is approached as a complex nonlinear problem with multiple inputs and outputs. Three hybrid multi-output models, integrating commonly used machine learning algorithms (artificial neural networks ANN, random forests RF, and Boost) with the walrus optimization algorithm (WAOA) optimization techniques, are developed. Input features are determined through literature research (friction angle, cohesion, rock modulus, Poisson's ratio, mud pressure, borehole radius, in situ stress), and 501 related datasets are collected to construct the borehole breakout size dataset. Model performance is assessed using the Pearson Correlation Coefficient (R2), Mean Absolute Error (MAE), Variance Accounted For (VAF), and Root Mean Squared Error (RMSE). Results indicate that WAOA-ANN exhibits excellent and stable prediction performance, particularly on the test set, outperforming the single-output ANN model. Additionally, SHAP sensitivity analysis conducted on the WAOA-ANN model reveals that maximum horizontal principal stress (sigma H) is the most influential parameter in predicting both the angle and depth of borehole breakout. Combining the results of the studies and analyses conducted, WAOA-ANN is considered to be an effective hybrid multi-output model in the prediction of borehole breakout size.

关键词： borehole breakout ANN RF XGBoost walrus optimization algorithm multi-output

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Dynamic performance improvement of oscillating water column wave energy conversion system using optimal walrus optimization algorithm-based control strategy

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AIN SHAMS ENGINEERING JOURNAL 2024年第12期15卷

作者： Eldemery, Habiba A. Hasanien, Hany M. Alharbi, Mohammed Sun, Chuanyu Zaky, Dina A. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Harbin Inst Technol Sch Elect Engn & Automat Harbin 150006 Peoples R China Higher Inst Engn & Technol Elect Power Engn Dept Cairo Egypt

The optimal design of the proportional-integral (PI) controller using the walrus optimization algorithm (WOA) with the aim to enhance the dynamic performance of a grid-connected wave energy conversion (WEC) system when subjected to diverse operational conditions is presented in this paper. The proposed system under study consists of an oscillating water column (OWC) device coupled with a permanent magnet synchronous generator (PMSG) to supply power to the grid. Power electronics devices in the form of a generator-side converter (GSC) and a grid-side inverter (GSI) are used to couple the grid and WEC systems. The GSC is used to minimize generator losses and maximize generator real power through the control of d-axis and q-axis currents (id, and iq) of the PMSG. The GSI is used to control the point of common coupling (PCC) and DC-link voltages (VPCC,VDC), respectively. The PI controllers, used to minimize the error between the actual current and voltage values with their respective reference values, are optimally designed using the WOA. The fitness function of the optimization problem is based on the integral square error criterion (ISE). Presented in this paper is a model for the OWC-WEC system and a control strategy to maximize generated power, minimize generator losses, and keep the VDC, V PCC at required values, the usage of WOA to design the PI controllers, and the simulations of system results. The proposed WOA-based PI controller design's effectiveness is evaluated by comparing its simulation results with that obtained from using genetic algorithm (GA), grey wolf (GWO), particle swarm (PWO), and harmony search (HS) optimization-based PI controllers under symmetrical and unsymmetrical faults. The proposed strategy shows an enhancement in the dynamic performance of OWC wave energy systems when compared to the other optimization algorithm-based PI controllers, as well as achieving the least value for ISE, which reached 0.172.

关键词： optimization algorithm walrus optimization algorithm Oscillating water column Renewable energy system Wave energy

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Precise modeling of lithium-ion battery in industrial applications using walrus optimization algorithm

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ENERGY 2024年 294卷

作者： Fahmy, Hend M. Alqahtani, Ayedh H. Hasanien, Hany M. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Publ Author Appl Educ & Training Coll Technol Studies Elect Engn Dept Kuwait Kuwait Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt

This research article demonstrates how to get a precise lithium-ion battery (LIB) model using one of the artificial intelligence algorithms called the walrus optimization algorithm (WaOA). The model's accuracy affects several transient and dynamic analysis simulations, which are carried out for power systems, electric vehicles, and many transportation applications. The LIB model may have one, two, or three resistance-capacitance (RC) models, signifying the complexity of the optimization challenge. Therefore, the WaOA is used to minimize the cost function that relies on an integral square error criterion. This criterion calculates the error between the estimated and experimental voltages. The proposed method is validated under several conditions, taking into account load variation, battery degradation, temperature fluctuation, and different RC models. The numerical results of the WaOA method are compared with their experimental results for a 2.6 Ah LIB. In addition, the proposed WaOA model has undergone validation alongside numerous optimization algorithms-based models. It is worth noting that utilization WaOA with battery modeling stands as a reliable tool for attaining precise model.

关键词： Artificial intelligence Lithium-ion batteries Metaheuristic algorithms walrus optimization algorithm

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A novel approach to node coverage enhancement in wireless sensor networks using walrus optimization algorithm

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RESULTS IN ENGINEERING 2024年 24卷

作者： Saravanan, V. Indhumathi, G. Palaniappan, Ramya Narayanasamy, P. Kumar, M. Hema Sreekanth, K. Navaneethan, S. Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Nano Elect Mat & Sensors Tamilnadu Chennai India Rajalakshmi Engn Coll Dept ECE Chennai Tamil Nadu India Madanapalle Inst Technol & Sci Dept Comp Sci & Engn Madanapalle Andhra Pradesh India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Math Chennai Tamilnadu India Sona Coll Technol Dept ECE Salem Tamilnadu India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India Saveetha Engn Coll Dept ECE Chennai Tamilnadu India

Wireless Sensor Networks (WSNs) are crucial components of modern technology, supporting applications like healthcare, industrial automation, and environmental monitoring. This research aims to design intelligent and adaptive sensor networks by integrating metaheuristics with node coverage optimization in WSNs. By incorporating metaheuristics and optimizing node coverage, WSNs can become more resilient and robust, leading to the development of self-adapting, self-organizing networks capable of efficiently covering dynamic and diverse environments. This research introduces the walrus optimization algorithm for Node Coverage Enhancement in WSNs, called the WaOA-NCEWSN technique. The primary goal of this technique is to optimize the coverage of a target region using a limited number of Sensor Nodes (SNs) and by improving their placement. The WaOA is inspired by walrus behaviours like feeding, migrating, breeding, escaping, roosting, and gathering in response to environmental signals. The WaOA-NCEWSN technique uses an objective function that defines the coverage ratio, representing the maximum probability of coverage in a 2D-WSN monitoring area. Comparative analysis with other models using 50, 75, 100, and 200 nodes shows that the WaOA-NCEWSN technique performs better. The compilation times for the WaOA-NCEWSN technique are 5.14s, 6.48s, 6.54s, and 7.47s for 50, 75, 100, and 200 nodes, respectively. Experimental results indicate that the WaOA-NCEWSN technique offers superior coverage performance compared to other models.

关键词： Node coverage optimization problem Wireless sensor network Performance estimation walrus optimization algorithm

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