Optimization Techniques Research Articles

Machine learning insights into scapular stabilization for alleviating shoulder pain in college students

Abstract Non-specific shoulder pain is a common musculoskeletal condition, especially among college students, and it can have a negative impact on the patient’s life. Therapists have used scapular stabilization exercises (SSE) to enhance scapular control and mobility. This study investigates the prediction of the impact of scapular stability exercises in treating non-specific shoulder pain, leveraging advanced machine learning techniques for comprehensive evaluation and analysis. Using a diverse range of regression models, including Gamma Regressor, Tweedie Regressor, Poisson Regressor, and others, the study examines the relationship between the effectiveness of various exercises and their impact on shoulder pain management. Furthermore, the study employs optimization techniques, such as Hyperopt, scikit-optimize, optunity, GPyOpt, and Optuna, to fine-tune the exercise protocols for optimal outcomes. The results reveal that scapular stabilization exercises, when optimized using machine learning algorithms, significantly contribute to reducing shoulder pain in college students. Among the optimization techniques, scikit-optimize demonstrated the best performance, resulting in a mean squared error of 0.0085, a mean absolute error of 0.0712, and an impressive R2 score of 0.8501. This indicates that the scikit-optimize approach yielded the most accurate predictions and effectively captured the relationship between the exercises and shoulder pain management. The findings highlight the critical role of scapular stabilization exercise interventions in ameliorating non-specific shoulder pain and underscore the potential of machine learning techniques in optimizing therapeutic strategies for musculoskeletal health management. The utilization of scikit-optimize, in particular, showcases its effectiveness in fine-tuning the exercise protocols for optimal outcomes. The study’s results serve as a crucial stepping stone in developing personalized rehabilitation programs for non-specific shoulder pain, emphasizing the importance of integrating machine learning methodologies in the assessment and treatment of musculoskeletal disorders among college students.

Exploring optimal sensitivity of lepton flavor violating effective couplings at the e+e− colliders

We analyze lepton flavor violation (LFV) using the Standard Model effective field theory (SMEFT) framework at the future lepton colliders. Our focus is on the associated production of tau lepton with electron/muon at the electron-positron (e+e−) colliders, related to four-Fermi SMEFT effective operators. In accordance with the upper limits on effective couplings from lepton flavor violating tau decays, we conduct a cut-based analysis to achieve sufficient signal significance. We utilize the optimal observable technique (OOT) to estimate the optimal sensitivity of the effective couplings. The impact of electron beam polarization and the interplay of signal and background in enhancing the optimal sensitivity of the effective couplings are discussed in detail. We find that the sensitivity of flavor-violating effective couplings is enhanced by order of one for 3 TeV center-of-mass (CM) energy and 1000 fb−1 integrated luminosity at the e+e− colliders. Published by the American Physical Society 2024

Development of Proposed Model Using Random Forest with Optimization Technique for Classification of Phishing Website

Development of Proposed Model Using Random Forest with Optimization Technique for Classification of Phishing Website

Effects of Coated Carbide Insert on Surface Finish of Multiple Hardened EN31 Alloy Steel Using Regression, ANN, and RSM

Objectives: Surface finish plays an essential role in every machining process. The current study mainly focuses on multiple hardened EN31 alloy steel, using various machining parameters and optimizing using regression, ANN, and RSM. Methods: The experimentation was performed using a commercial CNC machine and a surface roughness tester, using machining parameters such as tool nose radius (re), cutting speed (v), feed (f), depth of cut (d), and hardness (H) of the material ranging from 0.4 to 1.2 mm, 160 to 260 m/min, 0.1 to 0.20 mm/rev, 0.05 to 0.15 mm, and 45 to 49 HRC, respectively. Findings: The study revealed that cutting speed and feed rate significantly affect the surface finish, followed by tool nose radius. With the help of optimization techniques, the surface roughness predicted through RSM was 99.91%, followed by Regression and ANN. The contour plot predicts Ra using the best possible combinations of machining parameters. Novelty: The current research compares the surface finish results of multiple hardened EN31 alloy steels predicted through regression, RSM, and ANN with experimental values. The generated correlations will undoubtedly assist researchers in determining surface finish for the defined ranges of machining parameters. Keywords: Hard Turning, Coated Carbide, EN31 Steel, Surface Roughness, Optimization, RSM

Optimum solution of power flow problem based on search and rescue algorithm

AbstractIn order to solve the optimal power flow (OPF) problem, a unique algorithm based on a search and rescue method is applied in this study. For the OPF problem under three objective functions, the SAR offers a straightforward and reliable solution. The three objective functions are used to minimize the fuel cost, power loss and voltage deviation as a single objective function. The OPF problem for benchmark test system, including the IEEE-14 bus, IEEE-30 bus, and IEEE-57 bus, are solved by the Search and Rescue algorithm (SAR) under specific objective functions that are determined by the operational and economic performance indices of the power system. To demonstrate the efficacy and possibilities of the SAR algorithm, SAR is contrasted with alternative optimization techniques such as harmony search algorithm, gradient method, adaptive genetic algorithm, biogeography-based optimization, Artificial bee colony, gravitational search algorithm, particle swarm optimization, Jaya algorithm, enhanced genetic algorithm, modified shuffle frog leaping algorithm, practical swarm optimizer, Moth flam optimizer, whale and moth flam optimizer, grey wolf optimizer, cheap optimization algorithm and differential evolution algorithm. The value of minimum power losses based on SAR technique is equal to 0.459733441487247 MW for IEEE-14 bus. The value of minimum total fuel cost based on SAR technique is equal to 8051.12225602148 $/h for IEEE-14 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0357680148269292 for IEEE-14 bus. The value of minimum power losses based on SAR technique is equal to 2.71286428848434 MW for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 798.197578585806 $/h for IEEE-30 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0978069572088536 for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 38017.7691758245 $/h for IEEE-57 bus. The acquired results for the OPF compared to all competitor algorithms in every case of fitness function demonstrate the superiority of the SAR method.

A Novel Arithmetic Optimization Technique for Electric Vehicle Charging

With the increased usage of Electric vehicles in recent days, the optimal usage of battery both in terms of electrical and economic perspective is very important. Though several battery optimization techniques are evolving day by day, the method to achieve an efficient control of all parameters like battery current, power and power loss has not yet been achieved. This paper proposes a novel approach of implementing an arithmetic optimization scheme for FTP 75 drive cycle source. In this connect, the details of the arithmetic operators are discussed and their relations with electrical parameters are framed from the fundamental equations. A Pseudo-code has been developed and implemented for identifying the optimum charging point from different search spaces. The simulation results obtained for different electrical parameters with respect to different arithmetic operators, emphasizes the enhanced operation of electric vehicles at optimum charging points.

SSO optimized FOFPID regulator design for performance enhancement of doubly fed induction generator based wind turbine system.

A wind turbine system (WTS) is a highly coupled and nonlinear system where the output power depends upon highly uncertain wind speed. Therefore, the quality of produced power becomes a challenging problem for researchers. Direct Vector Control (DVC) is a powerful and widely utilized power control strategy to deal with winds that vary rapidly and randomly. As a result, this article employed the newly developed Social Spider Optimization (SSO) technique to optimize the design parameters of Fractional-Order Fuzzy Proportional-Integral with Derivative (FOFPID) regulator to maintain the output power of the studied DFIG-based WTS at the rated value under dynamic wind conditions. The suggested FOFPID controller integrates the capabilities of the Fuzzy intelligent regulator and the Fractional-Order controller, enhancing DFIG current control while allowing independent control of active and reactive power. The approach is incorporated within the DVC strategy of the DFIG's rotor-side converter (RSC), replacing the conventional Proportional-Integral (PI) regulator in the internal current loops. Extensive performance evaluations are conducted under various operating conditions, including active power reference changes, parameter uncertainties, and rapid wind speed variations. Comparative analyses with SSO-optimized PID and Fuzzy regulators show that the FOFPID regulator performs better in terms of maximum overshoot, extreme undershoot, settling time, Total Harmonic Distortion (THD), and Weighted Total Harmonic Distortion (WTHD). The suggested FOFPID regulator also displays stronger robustness against parameters mismatch and weather change than other regulator architectures.

Refined dimensional accuracy in FDM components via ensemble weight-optimized surrogates and hybrid NSGA-II-TOPSIS optimization

ABSTRACT In this paper, a novel approach was developed to predict the geometrical deviation and density of fused deposition modeling (FDM) parts and to optimize these outcomes by fine-tuning the relevant process variables. The prediction process utilized an ensemble approach, combining the optimized weighted sum of three individual surrogate models. These models correlate the input variables – nozzle temperature (NT), infill fraction (IF), and print speed (PS) – with the output responses of density, internal ovality (IO), and external ovality (EO). According to the results, the ensemble outperformed the individual surrogates, exhibiting enhanced predictive accuracy. Subsequently, non-dominated sorting genetic algorithm II (NSGA-II), a multi-objective optimization technique, was integrated with the technique for order of preference by similarity to ideal solution (TOPSIS), a multi-criteria decision-making method, to optimize the ensemble of surrogates (EoS) and generate a ranked set of Pareto-optimal solutions. The outcomes from the EoS, combined with the NSGA-II-TOPSIS hybrid optimization process, revealed that the most effective optimal control parameters were approximately 195°C for NT, 50% for IF, and a range of 55–66 mm/min for PS. This hybrid approach affirmed the reliability and robustness of the identified processing parameters for the production of top-quality FDM parts with desired dimensional accuracy and density.

Can search-based testing with pareto optimization effectively cover failure-revealing test inputs?

AbstractSearch-based software testing (SBST) is a widely-adopted technique for testing complex systems with large input spaces, such as Deep Learning-enabled (DL-enabled) systems. Many SBST techniques focus on Pareto-based optimization where multiple objectives are optimized in parallel to reveal failures. However, it is important to ensure that identified failures are spread throughout the entire failure-inducing area of a search domain, and not clustered in a sub-region. This ensures that identified failures are semantically diverse and reveal a wide range of underlying causes. In this paper, we present a theoretical argument explaining why testing based on Pareto optimization is inadequate for covering failure-inducing areas within a search domain. We support our argument with empirical results obtained by applying two widely used types of Pareto-based optimization techniques, namely NSGA-II (an evolutionary algorithm) and OMOPSO (a swarm-based algorithm), to two DL-enabled systems: an industrial Automated Valet Parking (AVP) system and a system for classifying handwritten digits. We measure the coverage of failure-revealing test inputs in the input space using a metric, that we refer to as the Coverage Inverted Distance (CID) quality indicator. Our results show that NSGA-II and OMOPSO are not more effective than a naïve random search baseline in covering test inputs that reveal failures. We show that this comparison remains valid for failure-inducing regions of various sizes of these two case studies. Further, we show that incorporating a diversity-focused fitness function as well as a repopulation operator in NSGA-II improves, on average, the coverage difference between NSGA-II and random search by 52.1%. However, even after diversification, NSGA-II still does not outperform random testing in covering test inputs that reveal failures. The replication package for this study is available in a GitHub repository (Replication package. https://github.com/ast-fortiss-tum/coverage-emse-24 2024.

Enhancing water security through advanced modeling: integrating deep learning and a novel metaheuristic optimization algorithm for accurate pan evaporation prediction

ABSTRACT This study evaluates and enhances machine learning models for predicting pan evaporation under diverse climatic conditions. Five fundamental machine learning models were employed and tested across four different stations. Subsequent comparisons were made with advanced techniques, including long short-term memory (LSTM) networks. An innovative approach was introduced, combining LSTM with Binary Al-Biruni Earth Radius (BER–LSTM). This hybrid method was benchmarked against other optimization techniques. The BER–LSTM model consistently outperformed other models across all stations and time scales, achieving up to a 97.54% improvement in root mean square error (RMSE) compared to standard LSTM on daily time scales. Compared to simpler models like Multilayer Perceptron and Support Vector Regressor, BER–LSTM showed even more substantial improvements, with up to a 99.03% reduction in RMSE. The BER–LSTM model demonstrates superior predictive capabilities for pan evaporation across varied climatic conditions, offering significant improvements over both traditional and advanced machine learning techniques. This approach shows promise for enhancing evaporation forecasting in diverse environmental contexts.

Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers

AbstractGendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital platforms into environments that are hostile and inhospitable to women. Despite the severity of this issue, efforts to persuade digital platforms to strengthen their protections against gendered disinformation are frequently ignored, highlighting the difficult task of countering online misogyny in the face of commercial interests. This growing concern underscores the need for effective measures to create safer online spaces, where respect and equality prevail, ensuring that women can participate fully and freely without the fear of harassment or discrimination. This study addresses the challenge of detecting misogynous content in bilingual (English and Italian) online communications. Utilizing FastText word embeddings and explainable artificial intelligence techniques, we introduce a model that enhances both the interpretability and accuracy in detecting misogynistic language. To conduct an in-depth analysis, we implemented a range of experiments encompassing classic machine learning methodologies and conventional deep learning approaches to the recent transformer-based models incorporating both language-specific and multilingual capabilities. This paper enhances the methodologies for detecting misogyny by incorporating incremental learning for cutting-edge datasets containing tweets and posts from different sources like Facebook, Twitter, and Reddit, with our proposed approach outperforming these datasets in metrics such as accuracy, F1-score, precision, and recall. This process involved refining hyperparameters, employing optimization techniques, and utilizing generative configurations. By implementing Local Interpretable Model-agnostic Explanations (LIME), we further elucidate the rationale behind the model’s predictions, enhancing understanding of its decision-making process.

Dynamic Lexicon-Based Sentiment Analysis Architecture Using Nonlinear Feature Optimization

Dynamic Lexicon-Based Sentiment Analysis (DLSA) improves sentiment detection by using adaptive, nonlinear feature optimization techniques. The model targets gaps in traditional methods, which often miss complex and subtle sentiment patterns. DLSA integrates modules for text preprocessing, lexicon and phrase extraction, and feature optimization with genetic algorithms. The system refines feature sets by selecting, combining, and adjusting features based on real-time performance data. Key findings show DLSA outperforms traditional models like SLPCF in accuracy, precision, and recall, especially in analyzing nuanced text data. This approach provides a flexible and efficient solution, enhancing the accuracy of sentiment classification in varied applications.

Offline supervisory control synthesis: taxonomy and recent developments

AbstractThis paper surveys recent advances in supervisory control theory since its 30th anniversary in 2017. We performed a systematic literature search and selected 272 relevant papers, with a focus on automata-based approaches. These were fitted into coherent narratives, based in part on a taxonomy of supervisor synthesis methods. The obtained papers show the fast and vast progress of the field, where recent research covers a wide range of new synthesis optimization techniques, different aspects such as fault tolerance, security, and timing, and new and diverse application domains. This survey refrains from in-depth descriptions of techniques, instead focusing on high-level contributions of recent works, how they relate to each other, and future directions for research.

Promoted Osprey Optimizer: a solution for ORPD problem with electric vehicle penetration.

This paper proposes a new optimization technique to make an integration between the Optimal Reactive Power Dispatch (ORPD) problem and Electric Vehicles (EV). Here, a modified metaheuristic algorithm, called the Promoted Osprey Optimizer (POO) is used for this purpose. Inspired by the hunting behavior of ospreys, a predatory bird species, the POO algorithm employs various strategies like diving, soaring, and gliding to effectively explore the search space and avoid local optima. To evaluate its performance, the POO-based model has been applied to the IEEE 118-bus and IEEE 57-bus systems, considering different scenarios of EV penetration. The experimental findings demonstrate that the POO algorithm can effectively optimize the reactive power dispatch problem with EV integration, achieving significant reductions in active power losses and voltage deviations toward several existing metaheuristic optimization techniques in different terms. The POO algorithm demonstrates a significant reduction in power loss, achieving up to 22.2% and 16.2% in the 57-bus and 118-bus systems, respectively. This improvement is accompanied by reductions in voltage deviation of up to 20.6% and 15.7%. In the 57-bus system, power loss is reduced from 2.35MW to 1.93MW, while voltage deviation decreases from 0.034 p.u. to 0.027 p.u. For the 118-bus system, power loss is lowered from 4.21MW to 3.53MW, and voltage deviation is reduced from 0.051 p.u. to 0.043 p.u. Furthermore, the POO algorithm surpasses other optimization methods in minimizing voltage deviation, achieving reductions of up to 0.056 p.u. in the 57-bus system and up to 0.163 p.u. in the 118-bus system. Consequently, the POO algorithm holds great potential as a valuable tool for power system operators and planners to optimize reactive power dispatch and enhance power system performance with EV integration.

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.

Phase-field based shape optimization of uni- and multiaxially loaded nature-inspired porous structures while maintaining characteristic properties

AbstractTriply periodic minimal surfaces (TPMS) are highly versatile porous formations that can be defined by formulas. Computationally based, load-specific shape optimization enables tailoring these structures for their respective application areas and thereby enhance their potential. In this investigation, individual sheet-based gyroid structures with varying porosities are specifically optimized with respect to their stiffness. A modified phase-field method is employed to establish a simulation framework for the shape optimization process. Despite constant volume and the preservation of the periodicity of the unit cells, volume redistribution occurs through displacement of the interfaces. The phase-field-based optimization process is detailed using unidirectional loading on three gyroidal unit cells with porosities of 75 %, 80 %, and 85 %. Subsequently, the gyroidal unit cell with a porosity of 85 % is shape-optimized under multidirectional loading. A subsequent experimental validation of the unidirectionally loaded cells confirms that the shape-optimized structures exhibit, on average, higher stiffness than the non-optimized structures. The highest increase of 40 % in effective modulus is achieved with the gyroid structure having a porosity of 75 %, while maintaining minimal alteration to the surface-to-volume ratio and preserving periodicity. Additionally, the experimental data show that the optimization process resulted in a shift in the linear elasticity and plasticity range. In summary, the phase-field method proves to be a valid optimization technique for complex porous structures, allowing the preservation of characteristic properties.

Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

Variable resolution machine learning optimization of antennas using global sensitivity analysis.

The significance of rigorous optimization techniques in antenna engineering has grown significantly in recent years. For many design tasks, parameter tuning must be conducted globally, presenting a challenge due to associated computational costs. The popular bio-inspired routines often necessitate thousands of merit function calls to converge, generating prohibitive expenses whenever the design process relies on electromagnetic (EM) simulation models. Surrogate-assisted methods offer acceleration, yet constructing reliable metamodels is hindered in higher-dimensional spaces and systems with highly nonlinear characteristics. This work suggests an innovative technique for global antenna optimization embedded within a machine-learning framework. It involves iteratively refined kriging surrogates and particle swarm optimization for generating infill points. The search process operates within a reduced-dimensionality region established through fast global sensitivity analysis. Domain confinement enables the creation of accurate behavioral models using limited training data, resulting in low CPU costs for optimization. Additional savings are realized by employing variable-resolution EM simulations, where low-fidelity models are utilized during the global search stage (including sensitivity analysis), and high-fidelity ones are reserved for final (gradient-based) tuning of antenna parameters. Comprehensive verification demonstrates the consistent performance of the proposed procedure, its superiority over benchmark techniques, and the relevance of the mechanisms embedded into the algorithm for enhancing search process reliability, design quality, and computational efficiency.

Comparison of different surgical strategies for cervical dystonia: Evidence from Bayesian network analysis.

Several surgical techniques have been used to treat cervical dystonia (CD), however, to date, the optimal surgical technique for CD remains controversial. We therefore conducted the first network meta-analysis to compare different surgical strategies for CD to inform clinical practice. Electronic databases were searched for surgical strategies for treating CD. The primary outcome was improvement in total Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) score. Subgroup analysis was performed to compare short-term (< 1 year) and long-term (≥ 1 year) outcomes. Safety outcomes included surgery-related adverse events (AEs). A total of 55 trials with 2032 patients employing five surgical strategies were identified, including globus pallidus internus (GPi)/subthalamic nucleus (STN)-deep brain stimulation (DBS), selective peripheral denervation (SPD), microvascular decompression (MVD) and pallidotomy. All strategies led to significant improvement in total TWSTRS score (mean improvement range 18.65-28.22). GPi-DBS showed significantly greater enhancement than SPD for the whole dataset (mean difference [MD] 7.03, 95% credible interval [Crl] 1.53-12.56), while both GPi-DBS (MD 8.05, 95% Crl 2.35-13.80) and STN-DBS (MD 10.71, 95% Crl 2.22-19.20) exhibited more long-term improvement than SPD. Regarding safety outcomes, GPi/STN-DBS and MVD were associated with fewer surgery-related AEs than SPD (ln odds ratio range -1.68 to -1.41). We conclude that DBS should be the preferred surgical option for CD, and the STN is a promising alternative target choice due to its comparable efficacy with the GPi. However, more direct evidence is still required.

A Nonconvex Approach with Structural Priors for Restoring Underwater Images

Underwater image restoration is a crucial task in various computer vision applications, including underwater target detection and recognition, autonomous underwater vehicles, underwater rescue, marine organism monitoring, and marine geological survey. Among other categories, the physics-based methods restore underwater images by improving the transmission map through optimization or regularization techniques. Conventional optimization-based methods often do not consider the effect of structural differences between guidance and transmission maps. To address this issue, in this paper, we present a regularization-based method for restoring underwater images that uses coherent structures between the guidance map and the transmission map. The proposed approach models the optimization of transmission maps through a nonconvex energy function comprising data and smoothness terms. The smoothness term includes static and dynamic structural priors, and the optimization problem is solved using a majorize-minimize algorithm. We evaluate the proposed method on benchmark datasets, and the results demonstrate the superiority of the proposed method over state-of-the-art techniques in terms of improving transmission maps and producing high-quality restored images.