Search Results (2,273)

The electrical storm optimization (ESO) algorithm, inspired by the dynamic nature of electrical storms, is a novel population-based metaheuristic that employs three dynamically adjusted parameters: field resistance, field intensity, and field conductivity. Field resistance assesses the spread of solutions within the search space, reflecting strategy diversity. The field intensity balances the exploration of new territories and the exploitation of promising areas. The field conductivity adjusts the adaptability of the search process, enhancing the algorithm’s ability to escape local optima and converge on global solutions. These adjustments enable the ESO to adapt in real-time to various optimization scenarios, steering the search toward potential optima. ESO’s performance was rigorously tested against 60 benchmark problems from the IEEE CEC SOBC 2022 suite and 20 well-known metaheuristics. The results demonstrate the superior performance of ESOs, particularly in tasks requiring a nuanced balance between exploration and exploitation. Its efficacy is further validated through successful applications in four engineering domains, highlighting its precision, stability, flexibility, and efficiency. Additionally, the algorithm’s computational costs were evaluated in terms of the number of function evaluations and computational overhead, reinforcing its status as a standout choice in the metaheuristic field. Full article

In this paper, we propose a new finite-difference method for nonconvex absolute value equations. The nonsmooth unconstrained optimization problem equivalent to the absolute value equations is considered. The finite-difference technique is considered to compose the linear programming subproblems for obtaining the search direction. The algorithm avoids the computation of gradients and Hessian matrices of problems. The new finite-difference parameter correction technique is considered to ensure the monotonic descent of the objective function. The convergence of the algorithm is analyzed, and numerical experiments are reported, indicating the effectiveness by comparison against a state-of-the-art absolute value equations. Full article

In the field of electromagnetic design, it is sometimes necessary to search for the optimal design solution (i.e., the optimal solution) within a large solution space to complete the optimization. However, traditional optimization methods are not only slow in searching for the solution space but are also prone to becoming trapped in local optima, leading to optimization failure. This paper proposes a dual-population genetic algorithm to quickly find the optimal solution for electromagnetic optimization problems in large solution spaces. The method involves two populations: the first population uses the powerful dynamic decision-making ability of reinforcement learning to adjust the crossover probability, making the optimization process more stable and enhancing the global optimization capability of the algorithm. The second population accelerates the convergence speed of the algorithm by employing a “leader dominance” mechanism, allowing the population to quickly approach the optimal solution. The two populations are integrated through an immigration operator, improving optimization efficiency. The effectiveness of the proposed method is demonstrated through the optimization design of an electromagnetic metasurface material. Furthermore, the method designed in this paper is not limited to the electromagnetic field and has practical value in other engineering optimization areas, such as vehicle routing optimization, energy system optimization, and fluid dynamics optimization, etc. Full article

A fixed-time global sliding mode control with prescribed performance is proposed for the varying center of mass parallel robot mobile platform with model uncertainties and external disturbances to improve the global robustness and convergence performance of the model, and reduce overshoots. Firstly, kinematic and dynamic models of the parallel robot mobile platform with a varying center of mass are established. A reference velocity controller for the mobile platform system’s outer loop is designed using the back-stepping method, which provides the expected reference velocity for the inner loop controller. Secondly, to improve the global robustness and convergence performance of the system, a fixed-time global sliding mode control algorithm in the inner loop of the system is designed to eliminate the reaching phase of sliding mode control and ensure that the system converges quickly within a fixed time. Meanwhile, by designing a performance function to constrain the system errors within the performance boundary further, the fixed-time global sliding mode control with prescribed performance is implemented to reduce overshoots of the system. Then, the Lyapunov stability of the proposed method is proved theoretically. Finally, the effectiveness and superiority of the proposed control method are verified by simulation experiments. Full article

This study addresses the challenge of optimizing deep learning models for IoT network monitoring, focusing on achieving a symmetrical balance between scalability and computational efficiency, which is essential for real-time anomaly detection in dynamic networks. We propose two novel hybrid optimization methods—Hybrid Grey Wolf Optimization with Particle Swarm Optimization (HGWOPSO) and Hybrid World Cup Optimization with Harris Hawks Optimization (HWCOAHHO)—designed to symmetrically balance global exploration and local exploitation, thereby enhancing model training and adaptation in IoT environments. These methods leverage complementary search behaviors, where symmetry between global and local search processes enhances convergence speed and detection accuracy. The proposed approaches are validated using real-world IoT datasets, demonstrating significant improvements in anomaly detection accuracy, scalability, and adaptability compared to state-of-the-art techniques. Specifically, HGWOPSO combines the symmetrical hierarchy-driven leadership of Grey Wolves with the velocity updates of Particle Swarm Optimization, while HWCOAHHO synergizes the dynamic exploration strategies of Harris Hawks with the competition-driven optimization of the World Cup algorithm, ensuring balanced search and decision-making processes. Performance evaluation using benchmark functions and real-world IoT network data highlights superior accuracy, precision, recall, and F1 score compared to traditional methods. To further enhance decision-making, a Multi-Criteria Decision-Making (MCDM) framework incorporating the Analytic Hierarchy Process (AHP) and TOPSIS is employed to symmetrically evaluate and rank the proposed methods. Results indicate that HWCOAHHO achieves the most optimal balance between accuracy and precision, followed closely by HGWOPSO, while traditional methods like FFNNs and MLPs show lower effectiveness in real-time anomaly detection. The symmetry-driven approach of these hybrid algorithms ensures robust, adaptive, and scalable monitoring solutions for IoT networks characterized by dynamic traffic patterns and evolving anomalies, thus ensuring real-time network stability and data integrity. The findings have substantial implications for smart cities, industrial automation, and healthcare IoT applications, where symmetrical optimization between detection performance and computational efficiency is crucial for ensuring optimal and reliable network monitoring. This work lays the groundwork for further research on hybrid optimization techniques and deep learning, emphasizing the role of symmetry in enhancing the efficiency and resilience of IoT network monitoring systems. Full article

With the increasing application of federated learning to medical and image data, the challenges of class distribution imbalances and Non-IID heterogeneity across clients have become critical factors affecting the generalization ability of global models. In the medical domain, the phenomenon of data silos is particularly pronounced, leading to significant differences in data distributions across hospitals, which in turn hinder the performance of global model training. To address these challenges, this paper proposes FedCon, a federated learning method capable of dynamically adjusting aggregation weights, while accurately evaluating client contributions. Specifically, FedCon initializes aggregation weights based on client data volume and class distribution and employs Monte Carlo sampling to effectively simplify the computation of Shapley values. Subsequently, it further optimizes the aggregation weights by comprehensively considering the historical contributions of clients and the similarity between clients and the global model. This approach significantly enhances the ability to generalize and update the stability of the global model. Experimental results demonstrate that, compared to existing methods, FedCon achieved a superior generalization performance on public datasets and significantly accelerated the convergence of the global model. Full article

This article presents two novel mixed-integer nonlinear programming (MINLP) formulations in the complex variable domain to address the optimal phase-balancing problem in asymmetric three-phase distribution networks. The first employs a matrix-based load connection model (M-MINLP), while the second uses a compact vector-based representation (V-MINLP). Both integrate the power flow equations through the current injection method, capturing the nonlinearities of Delta and Wye loads. These formulations, solved via an interior-point optimizer and the branch-and-cut method in the Julia software, ensure global optima and computational efficiency. Numerical validations on 8-, 25-, and 37-node feeders showed power loss reductions of 24.34%, 4.16%, and 19.26%, outperforming metaheuristic techniques and convex approximations. The M-MINLP model was 15.6 times faster in the 25-node grid and 2.5 times faster in the 37-node system when compared to the V-MINLP approach. The results demonstrate the robustness and scalability of the proposed methods, particularly in medium and large systems, where current techniques often fail to converge. These formulations advance the state of the art by combining exact mathematical modeling with efficient computation, offering precise, scalable, and practical tools for optimizing power distribution networks. The corresponding validations were performed using Julia (v1.10.2), JuMP (v1.21.1), and AmplNLWriter (v1.2.1). Full article

Population growth, industrialization, excessive energy consumption, and deforestation have led to climate change and affected water resources like dams intended for public drinking water. Meteorological parameters could be used to understand these effects better to anticipate the water quality of the dam. Artificial neural networks (ANNs) are favored in hydrology due to their accuracy and robustness. This study modeled climatic effects on the water quality of Doğancı dam using a feed-forward neural network with one input, one hidden, and one output layer. Three models were tested using various combinations of meteorological data as input and Doğancı dam’s water quality data as output. Model success was determined by the mean squared error and correlation coefficient (R) between the observed and predicted data. Resilient back-propagation and Levenberg–Marquardt were tested for each model to find an appropriate training algorithm. The model with the least error (1.12–1.68) and highest R value (0.93–0.99) used three meteorological inputs (air temperature, global solar radiation, and solar intensity), six water quality parameters of Doğancı dam as output (water temperature, pH, dissolved oxygen, manganese, arsenic, and iron concentrations), and ten hidden nodes. The two training algorithms employed in this study did not differ statistically (p > 0.05). However, the Levenberg–Marquardt training approach demonstrated a slight advantage over the resilient back-propagation algorithm by achieving reduced error and higher correlation in most of the models tested in this study. Also, better convergence and faster training with a lesser gradient value were noted for the LM algorithm. It was concluded that ANNs could predict a dam’s water quality using meteorological data, making it a useful tool for climatological water quality management and contributing to sustainable water resource planning. Full article

This paper presents a second examination of trigonometric step sizes and their impact on Warm Restart Stochastic Gradient Descent (SGD), an essential optimization technique in deep learning. Building on prior work with cosine-based step sizes, this study introduces three novel trigonometric step sizes aimed at enhancing warm restart methods. These step sizes are formulated to address the challenges posed by non-smooth and non-convex objective functions, ensuring that the algorithm can converge effectively toward the global minimum. Through rigorous theoretical analysis, we demonstrate that the proposed approach achieves an $O\left({\displaystyle \frac{1}{\sqrt{T}}}\right)$ convergence rate for smooth non-convex functions and extend the analysis to non-smooth and non-convex scenarios. Experimental evaluations on FashionMNIST, CIFAR10, and CIFAR100 datasets reveal significant improvements in test accuracy, including a notable $2.14\%$ increase on CIFAR100 compared to existing warm restart strategies. These results underscore the effectiveness of trigonometric step sizes in enhancing optimization performance for deep learning models. Full article

Although food seems abundant in the European Union, challenges related to specific aspects of food security continue to exist and require ongoing attention. A country’s food security depends on various economic, social, environmental, and institutional factors, which are studied using several scientific research methodologies. The role of institutions in determining national success and failure has been increasingly emphasized in recent academic discourse. Our research makes a novel contribution to the literature on institutions and food security by integrating New Institutional Economics with food security metrics. It aims to examine the relationships between food security dimensions and country-specific institutional matrices in the twenty EU member states from 2012 to 2019. How strong were those relationships, and how did they differ between the new and old member states? Food security is proxied by the Global Food Security Index and its three pillars (economic accessibility, physical availability, and quality and safety). The institutional quality of a country is represented by the Worldwide Governance Indicators (regulatory quality, rule of law, and control of corruption). Using the food security indices as the dependent variables, we apply multiple regression models to identify which institutions determined national food security over time. The study revealed that between 2012 and 2019, there was no evidence of sigma convergence or reduction in the dispersion of institutional quality (except for control of corruption) and overall food security within the EU20. The domestic institutions were generally statistically significantly positively related to the GFSI and its elements. The weakest correlations for the EU20 were those linking institutional variables with food quality and safety. The rule of law, incorporating such formal institutions as the quality of contract enforcement and property rights, positively affected food security within the EU20, with the mostgreatest impact on food quality, safety, and availability. The dependence of food security on national institutional factors was stronger in new member states from Central and Eastern Europe. The exploratory results shed some light on the role of institutions in shaping food security. However, further research is required to gain a more detailed understanding of this phenomenon. The research findings suggest that policymakers in the EU countries could enhance national institutions to promote food security and, consequently, achieve the Sustainable Development Goals more effectively. Full article

Multi-objective optimization problems often face challenges in balancing solution accuracy, computational efficiency, and convergence speed. Many existing methods struggle with achieving an optimal trade-off between exploration and exploitation, leading to premature convergence or excessive computational costs. To address these issues, this paper proposes a chaotic decomposition-based approach that leverages the ergodic properties of chaotic maps to enhance optimization performance. The proposed method consists of three key stages: (1) chaotic sequence initialization, which generates a diverse population to enhance the global search while reducing computational costs; (2) chaos-based correction, which integrates a three-point operator (TPO) and a local improvement operator (LIO) to refine the Pareto front and balance the exploration–exploitation trade-offs; and (3) Tchebycheff decomposition-based updating, ensuring efficient convergence toward optimal solutions. To validate the effectiveness of the proposed method, we conducted extensive experiments on a suite of benchmark problems and compared its performance with several state-of-the-art methods. The evaluation metrics, including inverted generational distance (IGD), generational distance (GD), and spacing (SP), demonstrated that the proposed method achieves competitive optimization accuracy and efficiency. While maintaining computational feasibility, our approach provides a well-balanced trade-off between exploration and exploitation, leading to improved solution diversity and convergence stability. The results establish the proposed algorithm as a promising alternative for solving multi-objective optimization problems. Full article

The alternating direction method is one of the attractive approaches for solving convex optimization problems with linear constraints and separable objective functions. Experience with applications has shown that the number of iterations depends significantly on the penalty parameter for the linear constraint. The penalty parameters in the classical alternating direction method are a constant. In this paper, an improved alternating direction method is proposed, which not only adaptively adjusts the penalty parameters per iteration based on the iteration message but also adds relaxation factors to the Lagrange multiplier update steps. Preliminary numerical experiments show that the technique of adaptive adjusting of the penalty parameters per iteration and attaching relaxation factors in the Lagrange multiplier updating steps are effective in practical applications. Full article

Metabolic syndrome (MetS)—a cluster of conditions including obesity, hypertension, dyslipidemia, and insulin resistance—is increasingly recognized as a key risk factor for the development of various eye diseases. The metabolic dysfunctions associated with this syndrome contribute to vascular and neurodegenerative damage within the eye, influencing disease onset and progression. Understanding these links highlights the importance of early diagnosis and management of metabolic syndrome to prevent vision loss and improve ocular health outcomes. This review explores the intricate interplay between metabolic syndrome, chronic low-grade inflammation, and eye diseases such as diabetic retinopathy, age-related macular degeneration, glaucoma, and dry eye syndrome. It highlights how inflammatory mediators, oxidative damage, and metabolic dysregulation converge to compromise ocular structures, including the retina, optic nerve, and ocular surface. We discuss the molecular and cellular mechanisms underpinning these associations and examine evidence from clinical and experimental studies. Given the rising global prevalence of metabolic syndrome, addressing this connection is crucial for improving overall patient outcomes and quality of life. Future research should focus on delineating the precise mechanisms linking these diseases as well as exploring targeted interventions that address both metabolic and ocular health. Full article

In this paper, a dynamic energy-efficient hybrid flow shop (TDEHFSP) scheduling model is proposed, considering random arrivals of new jobs and transport by transfer vehicles. To simultaneously optimise the maximum completion time and the total energy consumption, a co-evolutionary approach (DDQCE) using a double deep Q-network (DDQN) is introduced, where global and local search tasks are assigned to different populations to optimise the use of computational resources. In addition, a multi-objective NEW heuristic strategy is implemented to generate an initial population with enhanced convergence and diversity. The DDQCE incorporates an energy-efficient strategy based on time interval ‘left shift’ and turn-on/off mechanisms, alongside a rescheduling model to manage dynamic disturbances. In addition, 36 test instances of varying sizes, simplified from the excavator boom manufacturing process, are designed for comparative experiments with traditional algorithms. The experimental results demonstrate that DDQCE achieves 40% more Pareto-optimal solutions compared to NSGA-II and MOEA/D while requiring 10% less computational time, confirming that this algorithm efficiently solves the TDEHFSP problem. Full article

Under the uniform Hörmander hypothesis, we study the smoothness and exponential bounds of the density of the law of the solution of a stochastic differential equation (SDE) with locally Lipschitz drift that satisfies a monotonicity condition. We extend the approach used for SDEs with globally Lipschitz coefficients and obtain estimates for the Malliavin covariance matrix and its inverse. Based on these estimates and using the Malliavin differentiability of any order of the solution of the SDE, we prove exponential bounds of the solution’s density law. These results can be used to study the convergence of implicit numerical schemes for SDEs. Full article