Search Results (12)

Maintaining the equilibrium between electricity supply and demand remains a central concern in power systems. A demand response program can adjust the power load demand from the demand side to promote the balance of supply and demand. Load forecasting can facilitate the implementation of this program. However, as electricity consumption patterns become more diverse, the resulting load data grows increasingly irregular, making precise forecasting more difficult. Therefore, this paper developed a specialized forecasting scheme. First, the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). Then, the nonlinear power load data were decomposed into multiple subsequences using ICEEMDAN. Finally, each subsequence was independently predicted using the iTransformer model, and the overall forecast was derived by integrating these individual predictions. Data from Singapore was selected for validation. The results showed that the BWO–ICEEMDAN–iTransformer model outperformed the other comparison models, with an R² of 0.9873, RMSE of 48.0014, and MAE of 66.2221. Full article

As an important part of electronic equipment, the reliability of analog circuits directly affects the safe and stable operation of electronic equipment. The optimal measurement node set selection and intelligent fault diagnosis are combined in this paper, and an analog circuit fault diagnosis method incorporates a multi-objective selection of measurement nodes. Firstly, the fault differentiation is calculated by a classification algorithm to construct the fault information table of measurement nodes, and then the number of measurement nodes and the number of faults are set as the two objective functions of the multiple objective beluga whale optimization (MOBWO) algorithm to realize the optimal measurement node set selection. Secondly, the fault data set from multiple measurement nodes is inputted into the fault diagnosis model based on the adaptive particle swarm optimization algorithm and stacked denoising autoencoder (APSO-SDAE), in which the hyperparameter combination of the SDAE model is optimized by the APSO algorithm based on the fitness value in order to improve fault classification capability, and, finally, the analog circuit fault diagnosis task is completed. In order to verify the effectiveness and superiority of the above method, two standard circuits are selected for experimentation, and diagnostic accuracies of 97.87% and 98.41% are obtained. The results show that the method proposed in this paper can effectively improve the fault diagnosis accuracy, which is better than other comparative methods. Full article

This study is dedicated to the development of a multifunctional device that integrates air conditioning, humidification, and air purification functions, aimed at meeting the demands for energy efficiency, space-saving, and comfortable indoor environments in modern residential and commercial settings. The research focuses on achieving a balance between performance, energy consumption, and noise levels by combining bionic design principles with advanced optimization algorithms to propose innovative design and optimization methods. Specific methods include the establishment and optimization of mathematical models for air conditioning, air purification, and humidification functions. The air conditioning module employs a nonlinear programming model optimized through the Parrot Optimizer (PO) Algorithm to achieve uniform temperature distribution and minimal energy consumption. The air purification function is based on a bionic model and optimized using the Deep ACO Algorithm to ensure high efficiency and low noise levels. The humidification function utilizes a mist diffusion model optimized through the Slime Mold Algorithm (SMA) to enhance performance. Ultimately, a multi-objective optimization model is constructed using the Beluga Whale Optimization (BWO), successfully integrating the three main functions and designing a compact segmented cylindrical device that achieves a balance of high efficiency and multifunctionality. The optimization results indicate that the device exhibits superior performance, with a Clean Air Delivery Rate (CADR) of 400 m³/h, a humidification rate of 1.2 kg/h, a temperature uniformity index of 0.08, and a total power consumption controlled within 1600 W. This study demonstrates the significant potential of bionic design and optimization technology in the development of multifunctional indoor environment control devices, enhancing not only the overall performance of the device but also the comfort and sustainability of the indoor environment. Future work will focus on system scalability, experimental validation, and further optimization of bionic characteristics to expand the device’s applicability and enhance its environmental adaptability. Full article

This study presents an enhanced deep learning model, Multi-Scale Fusion MaxViT (MSF-MaxViT), designed for medical image classification. The aim is to improve both the accuracy and robustness of the image classification task. MSF-MaxViT incorporates a Parallel Attention mechanism for fusing local and global features, inspired by the MaxViT Block and Multihead Dynamic Attention, to improve feature representation. It also combines lightweight components such as the novel Multi-Scale Fusion Attention (MSFA) block, Feature Boosting (FB) Block, Coord Attention, and Edge Attention to enhance spatial and channel feature learning. To optimize the hyperparameters in the network model, the Super Beluga Whale Optimization (SBWO) algorithm is used, which combines bi-interpolation and adaptive parameter tuning, and experiments have shown that it has a relatively excellent convergence performance. The network model, combined with the improved SBWO algorithm, has an image classification accuracy of 92.87% on the HAM10000 dataset, which is 1.85% higher than that of MaxViT, proving the practicality and effectiveness of the model. Full article

The proliferation of multimodal misinformation on social media has become a critical concern. Although detection methods have advanced, feature representation and cross-modal semantic alignment challenges continue to hinder the effective use of multimodal data. Therefore, this paper proposes an IBWO-CASC detection model that integrates an improved Beluga Whale Optimization algorithm with cross-modal attention feature fusion. Firstly, the Beluga Whale Optimization algorithm is enhanced by combining adaptive search mechanisms with batch parallel strategies in the feature space. Secondly, a feature alignment method is designed based on supervised contrastive learning to establish semantic consistency. Then, the model incorporates a Cross-modal Attention Promotion mechanism and global–local interaction learning pattern. Finally, a multi-task learning framework is built based on classification and contrastive objectives. The empirical analysis shows that the proposed IBWO-CASC model achieves a detection accuracy of 97.41% on our self-constructed multimodal misinformation dataset. Compared with the average accuracy of the existing six baseline models, the accuracy of this model is improved by 4.09%. Additionally, it demonstrates enhanced robustness in handling complex multimodal scenarios. Full article

The fast development of digital images and the improvement required for security measures have recently increased the demand for innovative image analysis methods. Image analysis identifies, classifies, and monitors people, events, or objects in images or videos. Image analysis significantly improves security by identifying and preventing attacks on security applications through digital images. It is crucial in diverse security fields, comprising video analysis, anomaly detection, biometrics, object recognition, surveillance, and forensic investigations. By integrating advanced software engineering models with IoT capabilities, this technique revolutionizes copy–move image forgery detection. IoT devices collect and transmit real-world data, improving software solutions to detect and analyze image tampering with exceptional accuracy and efficiency. This combination enhances detection abilities and provides scalable and adaptive solutions to reduce cutting-edge forgery models. Copy–move forgery detection (CMFD) has become possibly a major active research domain in the blind image forensics area. Between existing approaches, most of them are dependent upon block and key-point methods or integration of them. A few deep convolutional neural networks (DCNN) techniques have been implemented in image hashing, image forensics, image retrieval, image classification, etc., that have performed better than the conventional methods. To accomplish robust CMFD, this study develops a fusion of soft computing with a deep learning-based CMFD approach (FSCDL-CMFDA) to secure digital images. The FSCDL-CMFDA approach aims to integrate the benefits of metaheuristics with the DL model for an enhanced CMFD process. In the FSCDL-CMFDA method, histogram equalization is initially performed to improve the image quality. Furthermore, the Siamese convolutional neural network (SCNN) model is used to learn complex features from pre-processed images. Its hyperparameters are chosen by the golden jackal optimization (GJO) model. For the CMFD process, the FSCDL-CMFDA technique employs the regularized extreme learning machine (RELM) classifier. Finally, the detection performance of the RELM method is improved by the beluga whale optimization (BWO) technique. To demonstrate the enhanced performance of the FSCDL-CMFDA method, a comprehensive outcome analysis is conducted using the MNIST and CIFAR datasets. The experimental validation of the FSCDL-CMFDA method portrayed a superior accuracy value of 98.12% over existing models. Full article

The SEA LIFE Trust Beluga Whale Sanctuary (BWS) has been in operation for over five years and serves as a unique case study to evaluate the effectiveness of marine sanctuaries for cetaceans. While cetacean sanctuaries are often regarded as a middle-ground solution between captivity and release, evidence from the BWS highlights complexities in adapting cetaceans to these environments. Despite initial assumptions that natural conditions would inherently improve welfare, the belugas at the BWS spent the majority of the operational period (92.6%) in a conventional indoor pool, due to health and welfare concerns. Repeated delays, challenges in acclimatization, and distress-related conditions observed during periods in the bay suggest that natural environments alone may not guarantee improved welfare. Additionally, the lack of publicly accessible data on health and welfare outcomes hinders comprehensive evaluation of the sanctuary’s success and raises questions about transparency and evidence-based practices. This review underscores the need for refined sanctuary models, improved infrastructure, and structured adaptation programs tailored to species and individual cetaceans. It highlights the importance of robust planning, ongoing research, and transparency to meet the ambitious goals of marine sanctuaries in the best interests of the well-being of cetaceans under human care. These considerations also raise concerns about the decision to relocate captive cetaceans to marine sanctuaries, as the available evidence suggests that such environments may not inherently guarantee better welfare outcomes. Full article

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation of ionic and covalent bonds—MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP’s quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57% compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods. Full article

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO). The adaptive population learning strategy is proposed to improve the global exploration capability of BWO. The introduction of the roulette equilibrium selection strategy allows BWO to have more reference points to choose among during the exploitation phase, which enhances the flexibility of the algorithm. In addition, the adaptive avoidance strategy improves the algorithm’s ability to escape from local optima and enriches the population quality. In order to validate the performance of the proposed AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on the CEC2017 and CEC2022 test sets. Statistical tests, convergence analysis, and stability analysis show that the AMBWO exhibits a superior overall performance. Finally, the applicability and superiority of the AMBWO was further verified by several engineering optimization problems. Full article

Feature selection (FS) is a classic and challenging optimization task in most machine learning and data mining projects. Recently, researchers have attempted to develop more effective methods by using metaheuristic methods in FS. To increase population diversity and further improve the effectiveness of the beluga whale optimization (BWO) algorithm, in this paper, we propose a multi-strategies improved BWO (MSBWO), which incorporates improved circle mapping and dynamic opposition-based learning (ICMDOBL) population initialization as well as elite pool (EP), step-adaptive Lévy flight and spiral updating position (SLFSUP), and golden sine algorithm (Gold-SA) strategies. Among them, ICMDOBL contributes to increasing the diversity during the search process and reducing the risk of falling into local optima. The EP technique also enhances the algorithm′s ability to escape from local optima. The SLFSUP, which is distinguished from the original BWO, aims to increase the rigor and accuracy of the development of local spaces. Gold-SA is introduced to improve the quality of the solutions. The hybrid performance of MSBWO was evaluated comprehensively on IEEE CEC2005 test functions, including a qualitative analysis and comparisons with other conventional methods as well as state-of-the-art (SOTA) metaheuristic approaches that were introduced in 2024. The results demonstrate that MSBWO is superior to other algorithms in terms of accuracy and maintains a better balance between exploration and exploitation. Moreover, according to the proposed continuous MSBWO, the binary MSBWO variant (BMSBWO) and other binary optimizers obtained by the mapping function were evaluated on ten UCI datasets with a random forest (RF) classifier. Consequently, BMSBWO has proven very competitive in terms of classification precision and feature reduction. Full article

Big data and artificial intelligence have promoted mining innovation and sustainable development, and the transportation used in open-pit mining has increasingly incorporated unmanned driving, real-time information sharing, and intelligent algorithm applications. However, the traditional manual scheduling used for mining transportation often prioritizes output over efficiency and quality, resulting in high operational expenses, traffic jams, and long lines. In this study, a novel scheduling model with multi-objective optimization was created to overcome these problems. Production, demand, ore grade, and vehicle count were the model’s constraints. The optimization goals were to minimize the shipping cost, total waiting time, and ore grade deviation. An enhanced multi-objective beluga whale optimization (IMOBWO) algorithm was implemented in the model. The algorithm’s superior performance was demonstrated in ten test functions, as well as the IEEE 30-bus system. It was enhanced by optimizing the population initialization, improving the adaptive factor, and adding dynamic domain perturbation. The case analysis showed that, in comparison to the other three conventional multi-objective algorithms, IMOBWO reduced the shipping cost from 7.65 to 0.84%, the total waiting time from 35.7 to 7.54%, and the ore grade deviation from 14.8 to 3.73%. The implementation of this algorithm for truck scheduling in open-pit mines increased operational efficiency, decreased operating costs, and advanced intelligent mine construction and transportation systems. These factors play a significant role in the safety, profitability, and sustainability of open-pit mines. Full article

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy. Full article