Search Results (17)

The Walrus Optimization (WO) algorithm, a metaheuristic inspired by walrus behavior, is known for its competitive convergence speed and effectiveness in solving high-dimensional and practical engineering optimization problems. However, it suffers from a tendency to converge to local optima and exhibits instability during the iterative process. To overcome these limitations, this study proposes an improved WO (IMWO) algorithm based on the integration of Differential Evolution/best/1 (DE/best/1) mutation, Logistics–Sine–Cosine (LSC) Mapping, and the Beta Opposition-Based Learning (Beta-OBL) strategy. These strategies work synergistically to enhance the algorithm’s global exploration capability, improve its search stability, and accelerate convergence with higher precision. The performance of the IMWO algorithm was comprehensively evaluated using the CEC2017 and CEC2022 benchmark test suites, where it was compared against the original WO algorithm and six other state-of-the-art metaheuristics. Experimental data revealed that the IMWO algorithm achieved average fitness rankings of 1.66 and 1.33 in the two test suites, ranking first among all compared algorithms. The WSN coverage optimization problem aims to maximize the monitored area while reducing perception blind spots under limited node resources and energy constraints, which is a typical complex optimization problem with multiple constraints. In a practical application addressing the coverage optimization problem in Wireless Sensor Networks (WSNs), the IMWO algorithm attained average coverage rates of 95.86% and 96.48% in two sets of coverage experiments, outperforming both the original WO and other compared algorithms. These results confirm the practical utility and robustness of the IMWO algorithm in solving complex real-world engineering problems. Full article

Non-linear systems of equations play a fundamental role in various engineering and data science models, where accurate solutions are essential for both theoretical research and practical applications. However, solving such systems is highly challenging due to their inherent non-linearity and computational complexity. This study proposes a novel hybrid iterative method with fourth-order convergence. The foundation of the proposed scheme combines the Walrus Optimization Algorithm and a fourth-order iterative technique. The objective of this hybrid approach is to enhance global search capability, reduce the likelihood of convergence to local optima, accelerate convergence, and improve solution accuracy in solving non-linear problems. The effectiveness of the proposed method is checked on standard benchmark problems and two real-world case studies, hydrocarbon combustion and electronic circuit design, and one non-linear boundary value problem. In addition, a comparative analysis is conducted with several well-established optimization algorithms, based on the optimal solution, average fitness value, and convergence rate. Furthermore, the proposed scheme effectively addresses key limitations of traditional iterative techniques, such as sensitivity to initial point selection, divergence issues, and premature convergence. These findings demonstrate that the proposed hybrid method is a robust and efficient approach for solving non-linear problems. Full article

Microgrids with high renewable energy penetration face critical challenges in frequency stability due to reduced system inertia and the presence of parameter uncertainties. This study introduces a novel adaptive virtual inertia control strategy utilizing a combination of the Walrus Optimization Algorithm (WaOA), a recent metaheuristic optimization technique, and Proportional–Integral–Derivative (PID) controllers (WaOA-PID) to improve frequency regulation in islanded microgrids under diverse operating conditions. The proposed method is evaluated across three scenarios: medium inertia, low inertia, and parametric uncertainty. Comparative analyses with conventional, IMC-tuned PID and H∞ Vector Internal Controllers (VIC) reveal that the WaOA-PID controller achieves the lowest overshoot, undershoot, and rate of change of frequency (RoCoF), while maintaining acceptable settling times in all cases. At an estimated load deviation of 0.18, the demand is varied from 200 MW to 250 MW to evaluate the system’s performance. The proposed technique yields an Integral Time Absolute Error (ITAE) of 0.000576, with PID gains of Ki = 0.9994, Kd = 0.185, and Kp = 0.774. Compared to traditional methods, the proposed controller demonstrates high reliability and efficiency in maintaining load frequency control and enhancing power system management, validating its suitability for real-time renewable energy-integrated microgrid applications. Full article

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

Wireless sensors serve as a critical means of information perception and collection, profoundly influencing human life and production. In order to optimize the problem of excessive energy drain caused by the selection of cluster heads and the transmission of paths in the network, this study proposes an energy-efficient clustering–routing algorithm that combines K-means++ initialization with the multi-objective Chaotic Mapping Walrus Optimization Algorithm (CM-WaOA). The CM-WaOA employs chaotic mapping and Pareto front optimization to balance node residual energy, cluster-head-to-base-station distance, inter-cluster-head distance, and intra-cluster node count variance when selecting cluster heads. Subsequently, the Sparrow Search Algorithm (SSA) refines routing paths through adaptive population sizing and elite retention, thereby reducing transmission path loss. The simulation results over 1000 rounds demonstrate that the CM-WaOA surpasses LEACH, EEUC, CGWOA, and EBPT-CRA in terms of energy drain, node survival, and latency; it achieves the highest average residual energy, the fewest dead nodes, the most surviving nodes, and the shortest network delay. These findings confirm that the CM-WaOA can still maintain good energy utilization and low-latency characteristics under different sensor densities, effectively extending the network lifetime. Full article

Backfilling materials are commonly employed materials in mines for filling mining waste, and the strength of the consolidated backfill formed by the binding material directly influences the stability of the surrounding rock and production safety in mines. The traditional approach to obtaining the strength of the backfill demands a considerable amount of manpower and time. The rapid and precise acquisition and optimization of backfill strength parameters hold utmost significance for mining safety. In this research, the authors carried out a backfill strength experiment with five experimental parameters, namely concentration, cement–sand ratio, waste rock–tailing ratio, curing time, and curing temperature, using an orthogonal design. They collected 174 sets of backfill strength parameters and employed six population optimization algorithms, including the Artificial Ecosystem-based Optimization (AEO) algorithm, Aquila Optimization (AO) algorithm, Germinal Center Optimization (GCO), Sand Cat Swarm Optimization (SCSO), Sparrow Search Algorithm (SSA), and Walrus Optimization Algorithm (WaOA), in combination with the CatBoost algorithm to conduct a prediction study of backfill strength. The study also utilized the Shapley Additive explanatory (SHAP) method to analyze the influence of different parameters on the prediction of backfill strength. The results demonstrate that when the population size was 60, the AEO-CatBoost algorithm model exhibited a favorable fitting effect (R² = 0.947, VAF = 93.614), and the prediction error was minimal (RMSE = 0.606, MAE = 0.465), enabling the accurate and rapid prediction of the strength parameters of the backfill under different ratios and curing conditions. Additionally, an increase in curing temperature and curing time enhanced the strength of the backfill, and the influence of the waste rock–tailing ratio on the strength of the backfill was negative at a curing temperature of 50 °C, which is attributed to the change in the pore structure at the microscopic level leading to macroscopic mechanical alterations. When the curing conditions are adequate and the parameter ratios are reasonable, the smaller the porosity rate in the backfill, the greater the backfill strength will be. This study offers a reliable and accurate method for the rapid acquisition of backfill strength and provides new technical support for the development of filling mining technology. Full article

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. Full article

Polycystic ovary syndrome (PCOS) is a medical condition that impacts millions of women worldwide; however, due to a lack of public awareness, as well as the expensive testing involved in the identification of PCOS, 70% of cases go undiagnosed. Therefore, the primary objective of this study is to design an expert machine learning (ML) model for the early diagnosis of PCOS based on initial symptoms and health indicators; two datasets were amalgamated and preprocessed to accomplish this goal, resulting in a new symptomatic dataset with 12 attributes. An ensemble learning (EL) model, with seven base classifiers, and a deep learning (DL) model, as the meta-level classifier, are proposed. The hyperparameters of the EL model were optimized through the nature-inspired walrus optimization (WaO), cuckoo search optimization (CSO), and random search optimization (RSO) algorithms, leading to the WaOEL, CSOEL, and RSOEL models, respectively. The results obtained prove the supremacy of the designed WaOEL model over the other models, with a PCOS prediction accuracy of 92.8% and an area under the receiver operating characteristic curve (AUC) of 0.93; moreover, feature importance analysis, presented with random forest (RF) and Shapley additive values (SHAP) for positive PCOS predictions, highlights crucial clinical insights and the need for early intervention. Our findings suggest that patients with features related to obesity and high cholesterol are more likely to be diagnosed as PCOS positive. Most importantly, it is inferred from this study that early PCOS identification without expensive tests is possible with the proposed WaOEL, which helps clinicians and patients make better informed decisions, identify comorbidities, and reduce the harmful long-term effects of PCOS. Full article

The increasing adoption of plug-in electric vehicles (PEVs) leads to negative impacts on distribution network efficiency due to the extra load added to the system. To overcome this problem, this manuscript aims to optimally integrate distributed generators (DGs) in radial distribution networks (RDNs), while including uncoordinated charging of PEVs added to the basic daily load curve with different load models. The main objectives are minimizing the network’s daily energy losses, improving the daily voltage profile, and enhancing voltage stability considering various constraints like power balance, buses’ voltages, and line flow. These objectives are combined using weighting factors to formulate a weighted sum multi-objective function (MOF). A very recent metaheuristic approach, namely the Walrus optimization algorithm (WO), is addressed to identify the DGs’ best locations and sizes that achieve the lowest value of MOF, without violating different constraints. The proposed optimization model along with a repetitive backward/forward load flow (BFLF) method are simulated using MATLAB 2016a software. The WO-based optimization model is applied to IEEE 33-bus, 69-bus, and a real system in El-Shourok City-district number 8 (ShC-D8), Egypt. The simulation results show that the proposed optimization method significantly enhanced the performance of RDNs incorporated with PEVs in all aspects. Moreover, the proposed WO approach proved its superiority and efficiency in getting high-quality solutions for DGs’ locations and ratings, compared to other programmed algorithms. Full article

The phases of high-entropy alloys (HEAs) are crucial to their material properties. Although meta-learning can recommend a desirable algorithm for materials designers, it does not utilize the optimal solution information of similar historical problems in the HEA field. To address this issue, a transferable meta-learning model (MTL-AMWO) based on an adaptive migration walrus optimizer is proposed to predict the phases of HEAs. Firstly, a transferable meta-learning algorithm frame is proposed, which consists of meta-learning based on adaptive migration walrus optimizer, balanced-relative density peaks clustering, and transfer strategy. Secondly, an adaptive migration walrus optimizer model is proposed, which adaptively migrates walruses according to the changes in the average fitness value of the population over multiple iterations. Thirdly, balanced-relative density peaks clustering is proposed to cluster the samples in the source and target domains into several clusters with similar distributions, respectively. Finally, the transfer strategy adopts the maximum mean discrepancy to find the most matching historical problem and transfer its optimal solution information to the target domain. The effectiveness of MTL-AMWO is validated on 986 samples from six datasets, including 323 quinary HEAs, 366 senary HEAs, and 297 septenary HEAs. The experimental results show that the MTL-AMWO achieves better performance than other algorithms. Full article

Transport energy demand (TED) forecasting is a crucial issue for countries like Turkey that are dependent on external resources. The accuracy and effectiveness of these forecasts are extremely important, especially for the strategies and plans to be developed. With this in mind, different forms of forecasting models were developed in the present study using the Walrus Optimizer (WO) and White Shark Optimizer (WSO) algorithms to estimate Turkey’s energy consumption related to road and railway transportation modes. Additionally, another objective of this study was to examine the impacts of different transport modes on energy demand. To investigate the effect of demand distribution among transport modes on energy consumption, model parameters such as passenger-kilometers (P-km), freight-kilometers (F-km), carbon dioxide emissions (CO₂), gross domestic product (GDP), and population (POP) were utilized in the development of the models. It was found that the WO algorithm outperformed the WSO algorithm and was the most suitable method for energy demand forecasting. All the developed models demonstrated a better performance level than those reported in previous studies, with the best performance achieved by the semi-quadratic model developed with the WO, showing a 0.95% MAPE value. Projections for energy demand up to the year 2035 were established based on two different scenarios: the current demand distribution among transport modes, and a demand shift from road to rail transportation. It is anticipated that the proposed energy demand models will serve as an important guide for effective planning and strategy development. Moreover, the findings suggest that a balanced distribution among transport modes will have a positive impact on transport energy and will result in lower energy requirements. Full article

Lung and colon cancer (LCC) is a dominant life-threatening disease that needs timely attention and precise diagnosis for efficient treatment. The conventional diagnostic techniques for LCC regularly encounter constraints in terms of efficiency and accuracy, thus causing challenges in primary recognition and treatment. Early diagnosis of the disease can immensely reduce the probability of death. In medical practice, the histopathological study of the tissue samples generally uses a classical model. Still, the automated devices that exploit artificial intelligence (AI) techniques produce efficient results in disease diagnosis. In histopathology, both machine learning (ML) and deep learning (DL) approaches can be deployed owing to their latent ability in analyzing and predicting physically accurate molecular phenotypes and microsatellite uncertainty. In this background, this study presents a novel technique called Lung and Colon Cancer using a Swin Transformer with an Ensemble Model on the Histopathological Images (LCCST-EMHI). The proposed LCCST-EMHI method focuses on designing a DL model for the diagnosis and classification of the LCC using histopathological images (HI). In order to achieve this, the LCCST-EMHI model utilizes the bilateral filtering (BF) technique to get rid of the noise. Further, the Swin Transformer (ST) model is also employed for the purpose of feature extraction. For the LCC detection and classification process, an ensemble deep learning classifier is used with three techniques: bidirectional long short-term memory with multi-head attention (BiLSTM-MHA), Double Deep Q-Network (DDQN), and sparse stacked autoencoder (SSAE). Eventually, the hyperparameter selection of the three DL models can be implemented utilizing the walrus optimization algorithm (WaOA) method. In order to illustrate the promising performance of the LCCST-EMHI approach, an extensive range of simulation analyses was conducted on a benchmark dataset. The experimentation results demonstrated the promising performance of the LCCST-EMHI approach over other recent methods. Full article

Feature selection (FS) is a significant dimensionality reduction technique in machine learning and data mining that is adept at managing high-dimensional data efficiently and enhancing model performance. Metaheuristic algorithms have become one of the most promising solutions in FS owing to their powerful search capabilities as well as their performance. In this paper, the novel improved binary walrus optimizer (WO) algorithm utilizing the golden sine strategy, elite opposition-based learning (EOBL), and population regeneration mechanism (BGEPWO) is proposed for FS. First, the population is initialized using an iterative chaotic map with infinite collapses (ICMIC) chaotic map to improve the diversity. Second, a safe signal is obtained by introducing an adaptive operator to enhance the stability of the WO and optimize the trade-off between exploration and exploitation of the algorithm. Third, BGEPWO innovatively designs a population regeneration mechanism to continuously eliminate hopeless individuals and generate new promising ones, which keeps the population moving toward the optimal solution and accelerates the convergence process. Fourth, EOBL is used to guide the escape behavior of the walrus to expand the search range. Finally, the golden sine strategy is utilized for perturbing the population in the late iteration to improve the algorithm’s capacity to evade local optima. The BGEPWO algorithm underwent evaluation on 21 datasets of different sizes and was compared with the BWO algorithm and 10 other representative optimization algorithms. The experimental results demonstrate that BGEPWO outperforms these competing algorithms in terms of fitness value, number of selected features, and F1-score in most datasets. The proposed algorithm achieves higher accuracy, better feature reduction ability, and stronger convergence by increasing population diversity, continuously balancing exploration and exploitation processes and effectively escaping local optimal traps. Full article

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. Full article

Recent advancements in renewable energy technologies, alongside changes in utility infrastructure and progressive government policies, have bolstered the integration of renewable-based distributed generation units within distribution systems. This paper introduces the Energy Valley Optimizer, a novel tool designed for the strategic placement of distributed generation units and capacitor banks. This placement is crucial not only for optimizing energy loss and enhancing bus voltage stability but also for promoting sustainable energy use and reducing environmental impact over the long term. By minimizing energy loss and voltage fluctuations, the optimizer contributes to a more sustainable and resilient energy system. It achieves this through the optimal allocation of resources across various load patterns within a 24 h period and is tested on the ALG-AB-Hassi-Sida 157-bus distribution network in South Algeria. Comparative analysis with existing algorithms—such as the Liver Cancer Algorithm, Walrus Optimization Algorithm, and Zebra Optimization Algorithm—demonstrates the superior performance of the Energy Valley Optimizer. It not only enhances technical and economic efficiencies but also significantly lowers the total cost of energy over 24 years, thus supporting sustainable development goals in energy management. Full article