This study proposes a data-driven optimization and analytical framework based on artificial-intelligence techniques for a hybrid wind and solar energy system designed specifically for urban street-lighting applications in İzmir, Turkey. The system integrates a vertical axis wind turbine with a helical blade configuration and photovoltaic (PV) panels to exploit complementary renewable resources under urban coastal conditions. Long-term meteorological assessment indicates favorable hybrid potential in the study area, characterized by strong wind availability and sufficient solar irradiance. To support reliable short-term power estimation, ensemble-based machine learning (ML) techniques are employed to forecast short-term power generation, including Extreme Gradient Boosting (XGBoost), Gradient Boosting, and Random Forest algorithms. Model performance is evaluated using standard regression metrics, with XGBoost demonstrating the most consistent generalization performance across both wind- and solar-driven outputs. To enhance interpretability and address the limited transparency often associated with data-driven models, SHapley Additive exPlanations (SHAP) analysis is applied to quantify the relative influence of environmental variables. The results reveal that wind speed, solar irradiance, and ambient temperature are the dominant drivers of hybrid energy production. Beyond predictive accuracy, the analysis incorporates urban operating constraints, demonstrating that hybridization substantially mitigates output variability under realistic urban wind conditions. Statistical and distributional analyses show that the hybrid configuration reduces overall power-output variability by approximately thirty-eight percent compared with standalone photovoltaic operation, thereby improving supply continuity for public lighting services. The novelty of this work lies in combining physics-informed energy modeling with explainable ensemble-based forecasting, explicitly tailored to urban street lighting applications rather than generalized power systems. By emphasizing predictive reliability, interpretability, and urban microclimatic relevance, the proposed framework provides a practically oriented basis for decentralized renewable energy planning in similar urban environments.

ABSTRACT In this paper, the improved chameleon swarm algorithm (ICSA) enhances the exploration–exploitation balance while optimizing feature subset selection. The integration of Lévy flight‐based exploration refines ICSA's search strategy, complemented by rotation‐type refinement and adaptive parameter‐setting mechanisms. These modifications ensure that exploration aligns effectively with the feature selection process, leading to a more adaptive and efficient approach. To evaluate ICSA's effectiveness, it is tested on the NSL‐KDD benchmark, a well‐established dataset in intrusion detection systems. Performance is assessed based on key metrics, including accuracy, detection rate, false alarm rate, execution time, and the number of selected features. Comparative analysis against six advanced classifiers demonstrates that ICSA achieves superior results with minimal computational overhead. The algorithm attains the highest accuracy (97.91%) and detection rate (98.75%), the fastest execution time, and the lowest false alarm rate (0.0021), eliminating the need for excessive feature selection. These results confirm that modifying feature selection mechanisms within ICSA significantly enhances computational efficiency and detection performance, as validated through rigorous experimental testing at the classifier level.

Abstract Deep learning and metaheuristic algorithms have recently increased in various sciences, including financial accounting information systems (FAISs). However, the existence of large datasets has dramatically increased the complexity of these hybrid networks, so to address this shortcoming, this paper aims to develop a quantum‐behaved chimp optimization algorithm (QCHOA) and deep neural network (DNN) for the prediction of the profit based on FAISs. Considering that there is no suitable dataset for the challenge, a novel dataset is developed utilizing the 15 features from the Chinese market dataset to compare more. This work designs QCHOA and five DNN‐based predictors to forecast profit. These algorithms include the universal learning CHOA (ULCHOA), the niching CHOA (NCHOA) as the two best‐modified versions of CHOA, the quantum‐behaved whale optimization algorithm (QWOA), and the quantum‐behaved grey wolf optimizer (QGWO) as the two best quantum‐behaved optimizers as well as classic CHOA. The most effective deep learning‐based predictors for forecasting the profit, ranked from highest to lowest, are DNN‐QCHOA, DNN‐NCHOA, DNN‐QWOA, DNN‐QGWO, DNN‐ULCHOA, DNN‐CHOA, and classic DNN, with corresponding ranking scores of 42, 36, 30, 24, 18, 12, and 6. As a final suggestion for profit prediction, the DNN‐CHOA is shown to be the most accurate model.

Abstract This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study.

In the realm of ChatGPT's language capabilities, exploring Arabic Sentiment Analysis emerges as a crucial research focus. This study centers on ChatGPT, a popular machine learning model engaging in dialogues with users, garnering attention for its exceptional performance and widespread impact, particularly in the Arab world. The objective is to assess people's opinions about ChatGPT, categorizing them as positive or negative. Despite abundant research in English, there is a notable gap in Arabic studies. We assembled a dataset from X (formerly known as Twitter), comprising 2,247 tweets, classified by Arabic language specialists. Employing various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB), we implemented hyperparameter optimization techniques such as Bayesian optimization, Grid Search, and random search to select the best hyperparameters that contribute to achieving the best performance. Through training and testing, performance enhancements were observed with optimization algorithms. SVM exhibited superior performance, achieving 90% accuracy, 88% precision, 95% recall, and 91% F1 score with Grid Search. These findings contribute valuable insights into ChatGPT's impact in the Arab world, offering a comprehensive understanding of sentiment analysis through machine learning methodologies.

As electric vehicle (EV) ownership becomes more commonplace, partly due to government incentives, there is a need also to design solutions such as energy allocation strategies to more effectively support sustainable vehicle-to-grid (V2G) applications. Therefore, this work proposes an energy allocation strategy, designed to minimize the electricity cost while improving the operating revenue. Specifically, V2G is abstracted as a three-domain network architecture to facilitate flexible, intelligent, and scalable energy allocation decision-making. Furthermore, this work combines virtual network embedding (VNE) and deep reinforcement learning (DRL) algorithms, where a DRL-based agent model is proposed, to adaptively perceives environmental features and extracts the feature matrix as input. In particular, the agent consists of a four-layer architecture for node and link embedding, and jointly optimizes the decision-making through a reward mechanism and gradient back-propagation. Finally, the effectiveness of the proposed strategy is demonstrated through simulation case studies. Specifically, compared to the used benchmarks, it improves the VNR acceptance ratio, Long-term average revenue, and Long-term average revenue-cost ratio indicators by an average of 3.17%, 191.36, and 2.04%, respectively. To the best of our knowledge, this is one of the first attempts combining VNE and DRL to provide an energy allocation strategy for V2G.

This paper reviews the application of metaheuristics for optimized sustainable supply chain management (SSCM). This paper explores the potential of metaheuristics to improve the supply chain’s sustainability while enhancing its efficiency and competitiveness. The paper provides an overview of the principles of SSCM and the challenges businesses face in achieving sustainable supply chain management. It then introduces the concept of metaheuristics and describes their use in solving complex optimization problems. The paper reviews various metaheuristics algorithms applied to sustainable supply chain management and analyzes their effectiveness in addressing the challenges of SSCM. The paper also identifies the key factors that influence the success of using metaheuristics for SSCM, such as the choice of algorithm, problem complexity, and data quality. Finally, the paper provides recommendations for future research in this area and highlights the potential of metaheuristics to promote sustainable supply chain management. The review suggests that metaheuristics can be a valuable tool for optimizing sustainable supply chain management and improving supply chain operations’ sustainability, efficiency, and competitiveness.

Abstract Hunger Games Search (HGS) is a newly developed swarm-based algorithm inspired by the cooperative behavior of animals and their hunting strategies to find prey. However, HGS has been observed to exhibit slow convergence and may struggle with unbalanced exploration and exploitation phases. To address these issues, this study proposes a modified version of HGS called mHGS, which incorporates five techniques: (1) modified production operator, (2) modified variation control, (3) modified local escaping operator, (4) modified transition factor, and (5) modified foraging behavior. To validate the effectiveness of the mHGS method, 18 different benchmark datasets for dimensionality reduction are utilized, covering a range of sizes (small, medium, and large). Additionally, two Parkinson’s disease phonation datasets are employed as real-world applications to demonstrate the superior capabilities of the proposed approach. Experimental and statistical results obtained through the mHGS method indicate its significant performance improvements in terms of Recall, selected attribute count, Precision, F-score, and accuracy when compared to the classical HGS and seven other well-established methods: Gradient-based optimizer (GBO), Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Harris Hawks Optimizer (HHO), and Ant Lion Optimizer (ALO).

Abstract The Chaos Game Optimization (CGO) has only recently gained popularity, but its effective searching capabilities have a lot of potential for addressing single-objective optimization issues. Despite its advantages, this method can only tackle problems formulated with one objective. The multi-objective CGO proposed in this study is utilized to handle the problems with several objectives (MOCGO). In MOCGO, Pareto-optimal solutions are stored in a fixed-sized external archive. In addition, the leader selection functionality needed to carry out multi-objective optimization has been included in CGO. The technique is also applied to eight real-world engineering design challenges with multiple objectives. The MOCGO algorithm uses several mathematical models in chaos theory and fractals inherited from CGO. This algorithm's performance is evaluated using seventeen case studies, such as CEC-09, ZDT, and DTLZ. Six well-known multi-objective algorithms are compared with MOCGO using four different performance metrics. The results demonstrate that the suggested method is better than existing ones. These Pareto-optimal solutions show excellent convergence and coverage.

Abstract Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with remarkable performance when solving diverse optimization problems. However, PSO faces two main problems that degrade its performance: slow convergence and local optima entrapment. In addition, the performance of this algorithm substantially degrades on high-dimensional problems. In the classical PSO, particles can move in each iteration with either slower or faster speed. This work proposes a novel idea called velocity pausing where particles in the proposed velocity pausing PSO (VPPSO) variant are supported by a third movement option that allows them to move with the same velocity as they did in the previous iteration. As a result, VPPSO has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, VPPSO modifies the first term of the PSO velocity equation. In addition, the population of VPPSO is divided into two swarms to maintain diversity. The performance of VPPSO is validated on forty three benchmark functions and four real-world engineering problems. According to the Wilcoxon rank-sum and Friedman tests, VPPSO can significantly outperform seven prominent algorithms on most of the tested functions on both low- and high-dimensional cases. Due to its superior performance in solving complex high-dimensional problems, VPPSO can be applied to solve diverse real-world optimization problems. Moreover, the velocity pausing concept can be easily integrated with new or existing metaheuristic algorithms to enhance their performances. The Matlab code of VPPSO is available at: https://uk.mathworks.com/matlabcentral/fileexchange/119633-vppso .

Path planning is an NP-hard problem that is aimed to satisfy multi-constraint optimization requirements. In autonomous robotics applications , path planning together with collision avoidance presents a challenging task. It necessitates the generation of possible search directions from a designated point to a fixed varying destination location satisfying spatial constraints. This paper presents a framework for the design of an intelligent multi-objective robotic path planning algorithm . The algorithm relies on the generation of way-points by hybridizing two meta-heuristics techniques, namely Grey Wolf Algorithm (GWO) and Particle Swarm Optimization (PSO). A frequency-based modification in GWO search operators is introduced to fasten the search process. An improvised search strategy is employed for collision detection and avoidance, which converts non-desired points into the desired point. Sensors are deployed around the robot vicinity for search optimization. Mutation operators are then introduced to improve path length by smoothing out the trajectory. The proposed algorithm's effectivity is then validated through extensive simulations, in which different condition environments are simulated. To validate the effectiveness of the proposed methodology, the results are compared with contemporary algorithms namely Minimum Angle Artificial Bee Colony (MAABC) algorithms, Hybrid Cuckoo Search-Bat Algorithm (BA-CSA), Bacterial Bolony (BC) and Genetic Algorithm (GA) algorithms. The results conclusively demonstrated that the proposed algorithm ensures effective performance in path smoothness and safety under a wide range of conditions.

The usage of cloud services is growing exponentially with the recent advancement of Internet of Things (IoT)-based applications. Advanced scheduling approaches are needed to successfully meet the application demands while harnessing cloud computing’s potential effectively to schedule the IoT services onto cloud resources optimally. This article proposes an alternative task scheduler approach for organizing IoT application tasks over the CCE. In particular, a novel hybrid swarm intelligence method, using a modified Manta ray foraging optimization (MRFO) and the salp swarm algorithm (SSA), is proposed to handle the problem of scheduling IoT tasks in cloud computing. This proposed method, called MRFOSSA, depends on using SSA to improve the local search ability of MRFO that typically enhances the rate of convergence towards the global solution. To validate the developed MRFOSSA, a set of experimental series is performed using different real-world and synthetic datasets with variant sizes. The performance of MRFOSSA is tested and compared with other metaheuristic techniques. Experiment results show the superiority of MRFOSSA over its competitors in terms of performance measures, such as makespan time and cloud throughput.