Search Results (21)

In the contemporary landscape of power systems, the escalating integration of renewable energy resources and electric vehicle infrastructures into distribution networks has intensified the imperative to ensure power quality, operational optimization, and system reliability. Distribution network reconfiguration emerges as a pivotal strategy to mitigate power losses, facilitate the seamless assimilation of renewable generation, and regulate the charging and discharging dynamics of EVs, thereby constituting a critical endeavor in modern electrical engineering. While the Particle Swarm Optimization algorithm is renowned for its rapid convergence and effective exploitation of solution spaces, its capacity to thoroughly explore complex search domains remains limited, particularly in multifaceted optimization challenges. Conversely, the Grey Wolf Optimization algorithm excels in global exploration, offering robust mechanisms to circumvent local optima traps. Leveraging the complementary strengths of these approaches, this study proposes a hybrid PSO-GWO framework to address the distribution network reconfiguration problem, explicitly accounting for the integration of renewable energy sources and EV systems. Empirical validation, conducted on the IEEE 33-bus test system across diverse operational scenarios, underscores the efficacy of the proposed methodology, revealing exceptional precision and dependability. Notably, the approach achieves substantial reductions in power losses during peak demand periods with distributed generation incorporation while maintaining voltage profiles within the stringent operational bounds of 0.94 to 1.0 per unit, thus ensuring stability amidst variable load conditions. Comparative analyses further demonstrate that the hybrid method surpasses conventional optimization techniques, as evidenced by enhanced convergence rates and superior objective function outcomes. These findings affirm the proposed strategy as a potent tool for advancing the resilience and efficiency of next-generation distribution networks. Full article

Deep Neural Networks (DNNs) have been widely used to solve complex problems in natural language processing, image classification, and autonomous systems. The strength of DNNs is derived from their ability to model complex functions and to improve detection engines through deeper architecture. Despite the strengths of DNN engines, they present several crucial challenges, such as the number of hidden layers, the learning rate, and the neuron weight. These parameters are considered to play a crucial role in the ability of DNNs to detect anomalies. Optimizing these parameters could improve the detection engine and expand the utilization of DNNs for various areas of application. Bio-inspired optimization algorithms, especially Particle Swarm Intelligence (PSO) and the Gray Wolf Optimizer (GWO), have been widely used to optimize complex tasks because of their ability to explore the search space and their fast convergence. Despite the significant successes of PSO and GWO, there remains a gap in the literature regarding their hybridization and application in Intrusion Detection Systems (IDSs), such as Sunburst attack detection, especially using DNN. Therefore, in this paper, we introduce a hybrid detection model that investigates the ability to integrate PSO and GWO so as to improve the DNN architecture to detect the Sunburst attack. The PSO algorithm was used to optimize the learning rate and the number of hidden layers, while the GWO algorithm was used to optimize the neuron weight. The hybrid model was tested and evaluated based on open-source Sunburst attacks. The results demonstrate the effectiveness and robustness of the suggested hybrid DNN model. Furthermore, an extensive analysis was conducted by evaluating the suggested hybrid PSO–GWO along with other hybrid optimization techniques, namely Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The results demonstrate that the suggested hybrid model outperformed other optimization techniques in terms of accuracy, precision, recall, and F1-score. Full article

Background: This study addresses the challenge of predicting data sequences characterized by a mix of partial linearity and partial nonlinearity. Traditional forecasting models often struggle to accurately capture the complex patterns of change within the data. Methods: To this end, this study introduces a novel polynomial-driven discrete grey power model (PFDPGM(1,1)) that includes time perturbation parameters, enabling a flexible representation of complex variation patterns in the data. The model aims to determine the accumulation order, nonlinear power exponent, time perturbation parameter, and polynomial degree to minimize the fitting error under various criteria. The estimation of unknown parameters is carried out by leveraging a hybrid optimization algorithm, which integrates Particle Swarm Optimization (PSO) and the Grey Wolf Optimization (GWO) algorithm. Results: To validate the effectiveness of the proposed model, the annual total renewable energy consumption in the BRICS countries is used as a case study. The results demonstrate that the newly constructed polynomial-driven discrete grey power model can adaptively fit and accurately predict data series with diverse trend change characteristics. Conclusions: This study has achieved a significant breakthrough by successfully developing a new forecasting model. This model is capable of handling data sequences with mixed trends effectively. As a result, it provides a new tool for predicting complex data change patterns. Full article

Traffic lights play an important role in efficient traffic management, especially in crowded cities. Optimizing traffic helps to reduce crowding, save time, and ensure the smooth flow of traffic. Metaheuristic algorithms have a proven ability to optimize smart traffic management systems. This paper investigates the effectiveness of two metaheuristic algorithms: particle swarm optimization (PSO) and grey wolf optimization (GWO). In addition, we posit a hybrid PSO-GWO method of optimizing traffic light control using IoT-enabled data from sensors. In this study, we aimed to enhance the movement of traffic, minimize delays, and improve overall traffic precision. Our results demonstrate that the hybrid PSO-GWO method outperforms individual PSO and GWO algorithms, achieving superior traffic movement precision (0.925173), greater delay reduction (0.994543), and higher throughput improvement (0.89912) than standalone methods. PSO excels in reducing wait times (0.7934), while GWO shows reasonable performance across a range of metrics. The hybrid approach leverages the power of both PSO and GWO algorithms, proving to be the most effective solution for smart traffic management. This research highlights using hybrid optimization techniques and IoT (Internet of Things) in developing traffic control systems. Full article

Sanitation and toilets are important infrastructure for public health and societal stability. However, the adoption of adequate treatment technologies and techniques is a major challenge for both developing and underdeveloped areas. Answering the question of how to improve sanitation and toilet infrastructure in rural areas, for poverty alleviation, inequality mitigation, and good health and well-being under the Sustainable Development Goals, is more challenging compared with urban areas. Decision support models (DSMs) are important for selecting rural sanitation and toilet technologies. However, previous models have not fully respected local standards, needs, and operational environments, and are mainly limited to technological sustainability performance. To overcome such research gaps, this study developed a rural sanitation and toilet technology decision support model (DSM) assessing economic, environmental, and technological sustainability. Both technology and village weighting methods based on 217 general experts and seven local residents, respectively, were adopted to fully tailor indicator weights to rural contexts. The results showed an economic sustainability weight of 0.205, an environmental sustainability weight of 0.466, and a technological sustainability weight of 0.329. The sanitation and toilet technologies were divided into wastewater treatment technologies and toilet technologies, with the former subdivided into primary, secondary, and tertiary wastewater treatment technologies. This study confirmed that the PSO-GWO algorithm outperformed in accuracy and effectiveness. Accordingly, the PSO-GWO algorithm was adopted to demonstrate the optimization of sanitation and toilet technologies in four villages in plateau, mountain, plain, and basin areas. The study can assist local governments in selecting appropriate rural sanitation and toilet technologies during the planning phase. This can enhance the living standards of rural residents and promote sustainable rural development. Full article

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations. Full article

Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for the problem being studied. These irrelevant features reduce both the efficiency and accuracy of the algorithm. It is possible to think about feature selection as an optimization problem. Swarm intelligence algorithms are promising techniques for solving this problem. This research paper presents a hybrid approach for tackling the problem of feature selection. A filter method (chi-square) and two wrapper swarm intelligence algorithms (grey wolf optimization (GWO) and particle swarm optimization (PSO)) are used in two different techniques to improve feature selection accuracy and system execution time. The performance of the two phases of the proposed approach is assessed using two distinct datasets. The results show that PSOGWO yields a maximum accuracy boost of 95.3%, while chi2-PSOGWO yields a maximum accuracy improvement of 95.961% for feature selection. The experimental results show that the proposed approach performs better than the compared approaches. Full article

In the evolving landscape of device-free localization techniques, Wi-Fi channel state information (CSI) emerges as a pivotal tool for environmental sensing. This study introduces a novel fingerprint localization algorithm. It employs an improved Hybrid Grey Wolf Particle Swarm Optimization (IPSOGWO) in combination with Multi-Output Support Vector Regression (MSVR) to enhance indoor positioning accuracy. To counteract the limitations of standard DBSCAN and PCA in noise reduction and feature extraction from complex nonlinear data, we propose an adaptive denoising algorithm based on spatial clustering (A-DBSCAN) and an autoencoder to efficiently denoise and extract features from CSI amplitude to improve the localization accuracy. Additionally, we introduce a new position update strategy, bolstering the optimization efficiency of the PSOGWO algorithm. This refined approach is instrumental in determining the globally optimal hyperparameters in MSVR, leading to enhanced model prediction accuracy. Two indoor scenario experiments were conducted to evaluate our method, yielding average localization errors of 0.59 m and 1.12 m, marking an improvement in localization performance compared to existing methods. Full article

Evapotranspiration (ETo) is one of the most important processes in the hydrologic cycle, with specific application to sustainable water resource management. As such, this study aims to evaluate the predictive ability of a novel method for monthly ETo estimation, using a hybrid model comprising data pre-processing and an artificial neural network (ANN), integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Monthly data from Al-Kut City, Iraq, over the period 1990 to 2020, were used for model training, testing, and validation. The predictive accuracy of the proposed model was compared with other cutting-edge algorithms, including the slime mould algorithm (SMA), the marine predators algorithm (MPA), and the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). A number of graphical methods and statistical criteria were used to evaluate the models, including root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R²), maximum absolute error (MAE), and normalised mean standard error (NMSE). The results revealed that all the models are efficient, with high simulation levels. The PSOGWO–ANN model is slightly better than the other approaches, with an R² = 0.977, MAE = 0.1445, and RMSE = 0.078. Due to its high predictive accuracy and low error, the proposed hybrid model can be considered a promising technique. Full article

Hydrological resource management, including crop watering and irrigation scheduling, relies on reliable estimates of reference evapotranspiration (ETo). However, previous studies of forecasting ETo have not dealt with comparing single and hybrid metaheuristic algorithms in much detail. This study aims to assess the efficiency of a novel methodology to simulate univariate monthly ETo estimates using an artificial neural network (ANN) integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Several state-of-the-art algorithms, including constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA), the slime mould algorithm (SMA), the marine predators algorithm (MPA) and the modified PSO algorithm were used to evaluate PSOGWO’s prediction accuracy. Monthly meteorological data were collected in Al-Kut City (1990 to 2020) and used for model training, testing and validation. The results indicate that pre-processing techniques can improve raw data quality and may also suggest the best predictors scenario. That said, all models can be considered efficient with acceptable simulation levels. However, the PSOGWO-ANN model slightly outperformed the other techniques based on several statistical tests (e.g., a coefficient of determination of 0.99). The findings can contribute to better management of water resources in Al-Kut City, an agricultural region that produces wheat in Iraq and is under the stress of climate change. Full article

Integrating renewable resources into the electrical systems of marine vessels achieves the dual goal of diversifying energy resources and reducing greenhouse gas emissions. The presence of intermittent renewable sources and sudden nonlinear load changes can cause frequency deviations in isolated hybrid marine microgrids. To address this issue, the paper proposes a conventional PID (proportional–integral–derivative)-controller-based LFC (load frequency controller) which is optimized by meta-heuristic optimization algorithms, namely, PSO (particle swarm optimization), GWO (grey wolf optimization) and hybrid PSO-GWO. The proposed LFC was designed using transfer functions of various microgrid components, with ITAE (integral time absolute error) and ITSE (integral time square error) serving as performance indices. The proposed LFC’s validation was performed through HIL (hardware-in-loop) real-time simulation using a DS 1104 R&D controller board, with simulation results showing the better performance of the optimized frequency response compared to the nonoptimized LFC controller in terms of rise time, fall time, slew rate and overshoot. The hybrid PSO-GWO algorithm performs better than the other optimization algorithms. The simulation results demonstrate the stability and robustness of the proposed controller. In summary, the proposed PID-controller-based LFC can regulate frequency deviation in standalone hybrid marine microgrids effectively. Full article

Energy efficiency and throughput are concerns for energy-harvesting cognitive radio networks. However, attaining the maximum level of both requires optimization of sensing duration, harvested energy, and transmission time. To obtain the optimal values of these multiple parameters and to maximize the average throughput and energy efficiency, a new hybrid technique for multi-objective optimization is proposed. This hybrid optimization algorithm incorporates a Shapley value and a game theoretic concept into metaheuristics. Here, particle swarm optimization grey wolf optimization (PSOGWO) is selected as the source for the advanced hybrid algorithm. The concept of the unbiased nature of wolves is also added to PSOGWO to make it more efficient. Multi-objective optimization is formulated by taking a deep look into combined spectrum sensing and energy harvesting in a cognitive radio network (CSSEH). The Pareto optimal solutions for the multi-objective optimization problem of energy efficiency and throughput can be obtained using PSOGWO by updating the velocity with the weights. In the proposed Shapley hybrid multi-objective optimization algorithm, we used Shapley values to set up the weights that, in turn, updated the velocities of the particles. This updated velocity increased the ability of particles to reach a global optimum rather than becoming trapped in local optima. The solution obtained with this hybrid algorithm is the Shapley–Pareto optimal solution. The proposed algorithm is also compared with state-of-the-art PSOGWO, unbiased PSOGWO, and GWO. The results show a significant level of improvement in terms of energy efficiency by 3.56% while reducing the sensing duration and increasing the average throughput by 21.83% in comparison with standard GWO. Full article

Hybrid metaheuristic algorithm (MA), an advanced tool in the artificial intelligence field, provides precise reference evapotranspiration (ETo) prediction that is highly important for water resource availability and hydrological studies. However, hybrid MAs are quite scarcely used to predict ETo in the existing literature. To this end, the prediction abilities of two support vector regression (SVR) models coupled with three types of MAs including particle swarm optimization (PSO), grey wolf optimization (GWO), and gravitational search algorithm (GSA) were studied and compared with single SVR and SVR-PSO in predicting monthly ETo using meteorological variables as inputs. Data obtained from Rajshahi, Bogra, and Rangpur stations in the humid region, northwestern Bangladesh, was used for this purpose as a case study. The prediction precision of the proposed models was trained and tested using nine input combinations and assessed using root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The tested results revealed that the SVR-PSOGWO model outperformed the other applied soft computing models in predicting ETo in all input combinations, followed by the SVR-PSOGSA, SVR-PSO, and SVR. It was found that SVR-PSOGWO decreases the RMSE of SVR, SVR-PSO, and SVR-PSOGSA by 23%, 27%, 14%, 21%, 19%, and 5% in Rangpur and Bogra stations during the testing stage. The RMSE of the SVR, SVR-PSO, and SVR-PSOGSA reduced by 32%, 20%, and 3%, respectively, employing the SVR-PSOGWO for the Rajshahi Station. The proposed hybrid machine learning model has been recommended as a potential tool for monthly ETo prediction in a humid region and similar climatic regions worldwide. Full article

Intrusion detection systems (IDS) are crucial for network security because they enable detection of and response to malicious traffic. However, as next-generation communications networks become increasingly diversified and interconnected, intrusion detection systems are confronted with dimensionality difficulties. Prior works have shown that high-dimensional datasets that simulate real-world network data increase the complexity and processing time of IDS system training and testing, while irrelevant features waste resources and reduce the detection rate. In this paper, a new intrusion detection model is presented which uses a genetic algorithm (GA) for feature selection and optimization algorithms for gradient descent. First, the GA-based method is used to select a set of highly correlated features from the NSL-KDD dataset that can significantly improve the detection ability of the proposed model. A Back-Propagation Neural Network (BPNN) is then trained using the HPSOGWO method, a hybrid combination of the Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Finally, the hybrid HPSOGWO-BPNN algorithm is used to solve binary and multi-class classification problems on the NSL-KDD dataset. The experimental outcomes demonstrate that the proposed model achieves better performance than other techniques in terms of accuracy, with a lower error rate and better ability to detect different types of attacks. Full article

Recently, some criminals have begun to use multimedia steganography to conduct covert communication, such as transmitting stolen trade secrets. After using steganalysis to find covert communication entities, obtaining their locations can effectively help criminal forensics. This paper proposes a novel localization method of wireless covert communication entity for post-steganalysis. The method is based on hybrid particle swarm optimization and gray wolf optimization to improve localization precision (ILP-PSOGWO). In this method, firstly, the relationship model between received signal strength (RSS) and distance is constructed for the indoor environment where the target node exists. Secondly, dichotomy is used to narrow the region where the target node is located. Then, the weighted distance strategy is used to select the reference point locations with strong and stable RSS. Finally, the intersection region of the reference points is taken as the region where the target node is located, and the hybrid PSOGWO is used to locate and optimize the target node location. Experimental results demonstrate that ILP-PSOGWO can maintain high stability, and 90% of the localization errors are lower than 0.9012 m. In addition, compared with the existing methods of PSO, GWO and extended weighted centroid localization (EWCL), the average localization error of ILP-PSOGWO is also reduced by 28.2–49.0%. Full article