KL Vasudev¹, Manish Pandey², Jaan H. Pu^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.7, pp. 1545-1569, 2025, DOI:10.32604/fdmp.2025.065289 - 31 July 2025

Abstract This study presents a comparative analysis of optimisation strategies for designing hull shapes of Autonomous Underwater Vehicles (AUVs), paying special attention to drag, lift-to-drag ratio, and delivered power. A fully integrated optimisation framework is developed accordingly, combining a single-objective Genetic Algorithm (GA) for design parameter generation, Computer-Aided Geometric Design (CAGD) for the creation of hull geometries and associated fluid domains, and a Reynolds-Averaged Navier–Stokes (RANS) solver for evaluating hydrodynamic performance metrics. This unified approach eliminates manual intervention, enabling automated determination of optimal hull configurations. Three distinct optimisation problems are addressed using the proposed methodology. First,… More >

Thi My Binh Nguyen^#, Thi Hoa Hue Nguyen^#, Thi Ngoc Huyen Do^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5063-5076, 2025, DOI:10.32604/cmc.2025.064610 - 30 July 2025

Abstract Efficient warehouse management is critical for modern supply chain systems, particularly in the era of e-commerce and automation. The Multi-Picker Robot Routing Problem (MPRRP) presents a complex challenge involving the optimization of routes for multiple robots assigned to retrieve items from distinct locations within a warehouse. This study introduces optimized metaheuristic strategies to address MPRRP, with the aim of minimizing travel distances, energy consumption, and order fulfillment time while ensuring operational efficiency. Advanced algorithms, including an enhanced Particle Swarm Optimization (PSO-MPRRP) and a tailored Genetic Algorithm (GA-MPRRP), are specifically designed with customized evolutionary operators to More >

Yuming Luo¹, Wei Zhang², Anqi Zhao², Ling Gou¹, Li Chen¹, Yaling Yang¹, Xiaoping Wang¹, Shichang Liu¹, Huiqing Qi³, Shilai Hu^2,*

Energy Engineering, Vol.122, No.8, pp. 3201-3221, 2025, DOI:10.32604/ee.2025.066507 - 24 July 2025

Abstract This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety. Previous research primarily focused on integrating reservoir, wellbore, and surface facility constraints, often resulting in broad constraint ranges and slow model convergence. To solve this problem, the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs, while considering extreme peak-shaving demands. This approach effectively narrows the constraint range. Subsequently, a collaborative optimization model with… More >

Walaa N. Ismail^1,2,#,*, Fathimathul Rajeena P. P.^3,#, Mona A. S. Ali^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3709-3741, 2025, DOI:10.32604/cmes.2025.065564 - 30 June 2025

Abstract Early detection of Alzheimer’s disease (AD) is crucial, particularly in resource-constrained medical settings. This study introduces an optimized deep learning framework that conceptualizes neural networks as computational “sensors” for neurodegenerative diagnosis, incorporating feature selection, selective layer unfreezing, pruning, and algorithmic optimization. An enhanced lightweight hybrid DenseNet201 model is proposed, integrating layer pruning strategies for feature selection and bioinspired optimization techniques, including Genetic Algorithm (GA) and Harris Hawks Optimization (HHO), for hyperparameter tuning. Layer pruning helps identify and eliminate less significant features, while model parameter optimization further enhances performance by fine-tuning critical hyperparameters, improving convergence speed,… More >

Cheng Cheng^*, Xuan-Ping Gong, Xiao-Yu Cheng, Lu Xiao, Xing-Ying Ma

Frontiers in Heat and Mass Transfer, Vol.23, No.3, pp. 1037-1051, 2025, DOI:10.32604/fhmt.2025.065719 - 30 June 2025

Abstract To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume, which adversely impacts gas utilization efficiency in mines, a gas extraction pure volume prediction model was developed using Support Vector Regression (SVR) and Random Forest (RF), with hyperparameters fine-tuned via the Genetic Algorithm (GA). Building upon this, an adaptive control model for gas extraction negative pressure was formulated to maximize the extracted gas volume within the pipeline network, followed by field validation experiments. Experimental results indicate that the GA-SVR model surpasses comparable models in terms of mean… More >

Salman A. Khan^1,*, Mohamed Mohandes^2,3, Shafiqur Rehman³, Ali Al-Shaikhi^2,4, Kashif Iqbal¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 553-581, 2025, DOI:10.32604/cmc.2025.064560 - 09 June 2025

Abstract Wind energy has emerged as a potential replacement for fossil fuel-based energy sources. To harness maximum wind energy, a crucial decision in the development of an efficient wind farm is the optimal layout design. This layout defines the specific locations of the turbines within the wind farm. The process of finding the optimal locations of turbines, in the presence of various technical and technological constraints, makes the wind farm layout design problem a complex optimization problem. This problem has traditionally been solved with nature-inspired algorithms with promising results. The performance and convergence of nature-inspired algorithms… More >

Atheer Alrammahi^1,2, Hedieh Sajedi^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 887-918, 2025, DOI:10.32604/cmc.2025.063866 - 09 June 2025

Abstract Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution, tampering, and copyright infringement. This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform (DWT), Redundant Discrete Wavelet Transform (RDWT), and Möbius Transformations (MT), with optimization of transformation parameters achieved via a Genetic Algorithm (GA). By combining frequency and spatial domain techniques, the proposed method significantly enhances both the imperceptibility and robustness of watermark embedding. The approach leverages DWT and RDWT for multi-resolution decomposition, enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks. RDWT,… More >

Helen Shin Huey Wee, Nur Syazreen Ahmad^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1301-1354, 2025, DOI:10.32604/cmes.2025.063611 - 30 May 2025

Abstract This paper reviews recent advancements in system identification methods for perturbed motorized systems, focusing on brushed DC motors, brushless DC motors, and permanent magnet synchronous motors. It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms, highlighting their advantages, limitations, and applications. The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads, noise, and friction. A comparative performance analysis is also included to assess several widely used optimization methods, including least squares (LS), particle swarm optimization (PSO), grey wolf optimizer (GWO), bat algorithm… More >

Wencheng Wang^1,2,*, Baoxin Yin^1,2, Lei Li^2,*, Lun Li¹, Hongtao Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1595-1616, 2025, DOI:10.32604/cmes.2025.063595 - 30 May 2025

Abstract In low-light environments, captured images often exhibit issues such as insufficient clarity and detail loss, which significantly degrade the accuracy of subsequent target recognition tasks. To tackle these challenges, this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis. The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image, followed by image segmentation using a quadtree method. To improve the accuracy and robustness of atmospheric light estimation, the algorithm incorporates a genetic algorithm to optimize the… More >

Kun Li¹, Tianfu Li^2,*, Xiuwei Wang¹, Qingchun Meng¹, Zhenjie Wang¹, Jinyang Luo^1,2, Zhaohui Wang¹, Yuedong Yao²

Energy Engineering, Vol.122, No.6, pp. 2215-2238, 2025, DOI:10.32604/ee.2025.062876 - 29 May 2025

Abstract CO₂ flooding for enhanced oil recovery (EOR) not only enables underground carbon storage but also plays a critical role in tertiary oil recovery. However, its displacement efficiency is constrained by whether CO₂ and crude oil achieve miscibility, necessitating precise prediction of the minimum miscibility pressure (MMP) for CO₂-oil systems. Traditional methods, such as experimental measurements and empirical correlations, face challenges including time-consuming procedures and limited applicability. In contrast, artificial intelligence (AI) algorithms have emerged as superior alternatives due to their efficiency, broad applicability, and high prediction accuracy. This study employs four AI algorithms—Random Forest Regression (RFR), Genetic… More >