Binjiang Hu^1,*, Yihua Zhu², Liang Tu^1,2, Zun Ma³, Xian Meng³, Kewei Xu³

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069777 - 27 December 2025

Abstract This paper proposes an equivalent modeling method for photovoltaic (PV) power stations via a particle swarm optimization (PSO) K-means clustering (KMC) algorithm with passive filter parameter clustering to address the complexities, simulation time cost and convergence problems of detailed PV power station models. First, the amplitude–frequency curves of different filter parameters are analyzed. Based on the results, a grouping parameter set for characterizing the external filter characteristics is established. These parameters are further defined as clustering parameters. A single PV inverter model is then established as a prerequisite foundation. The proposed equivalent method combines the… More >

Xiaoyu Wen^1,2, Haohao Liu^1,2, Xinyu Zhang^1,2, Haoqi Wang^1,2, Yuyan Zhang^1,2, Guoyong Ye^1,2, Hongwen Xing³, Siren Liu³, Hao Li^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-27, 2026, DOI:10.32604/cmc.2025.069492 - 10 November 2025

Abstract Aircraft assembly is characterized by stringent precedence constraints, limited resource availability, spatial restrictions, and a high degree of manual intervention. These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling. To address this challenge, this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem (APALSP) under skilled operator allocation, with the objective of minimizing assembly completion time. A mathematical model considering skilled operator allocation is developed, and a Q-Learning improved Particle Swarm Optimization algorithm (QLPSO) is proposed. In the algorithm design, a reverse scheduling strategy is adopted to effectively… More >

Songsong Zhang¹, Huazhong Jin^1,2,*, Zhiwei Ye^1,2, Jia Yang^1,2, Jixin Zhang^1,2, Dongfang Wu^1,2, Xiao Zheng^1,2, Dingfeng Song¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068044 - 10 November 2025

Abstract Multi-label feature selection (MFS) is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels. However, traditional centralized methods face significant challenges in privacy-sensitive and distributed settings, often neglecting label dependencies and suffering from low computational efficiency. To address these issues, we introduce a novel framework, Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization (DHBCPSO-MSR). Leveraging the federated learning paradigm, Fed-MFSDHBCPSO allows clients to perform local feature selection (FS) using DHBCPSO-MSR. Locally selected feature subsets are encrypted with differential privacy (DP) and transmitted… More >

Inga Telksniene¹, Tadas Telksnys², Romas Marcinkevičius³, Zenonas Navickas², Raimondas Čiegis¹, Minvydas Ragulskis^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2131-2156, 2025, DOI:10.32604/cmes.2025.072938 - 26 November 2025

Abstract Fractional differential equations (FDEs) provide a powerful tool for modeling systems with memory and non-local effects, but understanding their underlying structure remains a significant challenge. While numerous numerical and semi-analytical methods exist to find solutions, new approaches are needed to analyze the intrinsic properties of the FDEs themselves. This paper introduces a novel computational framework for the structural analysis of FDEs involving iterated Caputo derivatives. The methodology is based on a transformation that recasts the original FDE into an equivalent higher-order form, represented as the sum of a closed-form, integer-order component G(y) and a residual… More >

Meshari D. Alanazi¹, Gehan Elsayed^2,*, Turki M. Alanazi³, Anis Sahbani⁴, Amr Yousef^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2277-2309, 2025, DOI:10.32604/cmes.2025.070726 - 26 November 2025

Abstract Traffic congestion plays a significant role in intelligent transportation systems (ITS) due to rapid urbanization and increased vehicle concentration. The congestion is dependent on multiple factors, such as limited road occupancy and vehicle density. Therefore, the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment. Conventional prediction systems face difficulties in identifying highly congested areas, which leads to reduced prediction accuracy. The problem is addressed by integrating Graph Neural Networks (GNN) with the Lion Swarm Optimization (LSO) framework to tackle the congestion prediction problem. Initially, the traffic information is… More >

Hang Zhou^*, Xiaojun Ding, Song Chen, Qijun Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1907-1933, 2025, DOI:10.32604/cmes.2025.069198 - 31 August 2025

Abstract Structural Reliability-Based Topology Optimization (RBTO), as an efficient design methodology, serves as a crucial means to ensure the development of modern engineering structures towards high performance, long service life, and high reliability. However, in practical design processes, topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions, which traditional methods often struggle accommodate. Therefore, this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization (DHPSO) algorithm. By leveraging… More >

Essam H. Houssein^1,*, Eman Saber¹, Nagwan Abdel Samee²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2445-2480, 2025, DOI:10.32604/cmes.2025.069184 - 31 August 2025

Abstract The diagnosis of Dry Eye Disease (DED), however, usually depends on clinical information and complex, high-dimensional datasets. To improve the performance of classification models, this paper proposes a Computer Aided Design (CAD) system that presents a new method for DED classification called (IAOO-PSO), which is a powerful Feature Selection technique (FS) that integrates with Opposition-Based Learning (OBL) and Particle Swarm Optimization (PSO). We improve the speed of convergence with the PSO algorithm and the exploration with the IAOO algorithm. The IAOO is demonstrated to possess superior global optimization capabilities, as validated on the IEEE Congress on More >

Puneet Sharma¹, Dhirendra Prasad Yadav¹, Bhisham Sharma^2,*, Surbhi B. Khan^3,4,*, Ahlam Almusharraf ⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2179-2193, 2025, DOI:10.32604/cmc.2025.063190 - 29 August 2025

Abstract The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems. This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network (QT-DNN) with Binary Bird Swarm Optimization (BBSO) to enhance resource allocation while preserving Quality of Service (QoS). In contrast to conventional approaches, the QT-DNN accurately predicts task-resource mappings using tensor-based task representation, significantly minimizing computing overhead. The BBSO allocates resources dynamically, optimizing energy efficiency and task distribution. Experimental results from extensive simulations indicate the efficacy of the suggested strategy; the… More >

Yu-Shan Cheng¹, Bo-Zheng Luo¹, Guan-Hao Su¹, Yi-Hua Liu^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5791-5809, 2025, DOI:10.32604/cmc.2025.066125 - 30 July 2025

Abstract Underwater charging stations allow Autonomous Underwater Vehicles (AUVs) to recharge batteries, extending missions and reducing surface support. However, efficient wireless power transfer requires overcoming alignment challenges and environmental variations in conductive seawater. This paper employs Particle Swarm Optimization (PSO) to design coupling coils specifically applied for underwater wireless charging station systems. The establishment of underwater charging stations enables Autonomous Underwater Vehicles (AUVs) to recharge batteries underwater, extending mission duration and reducing reliance on surface-based resupply operations. The proposed charging system is designed to address the unique challenges of the underwater environment, such as alignment disruptions… More >

Roseline Oluwaseun Ogundokun^*, Pius Adewale Owolawi, Chunling Tu

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4869-4885, 2025, DOI:10.32604/cmc.2025.064944 - 30 July 2025

Abstract Breast cancer is among the leading causes of cancer mortality globally, and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification. Existing machine learning (ML) methods struggle with intra-class heterogeneity and inter-class similarity, necessitating more robust classification models. This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning (DL) and Bat Swarm Optimization (BSO) hyperparameter optimization to improve breast cancer histopathology (BCH) image classification. A dataset of 804 Hematoxylin and Eosin (H&E) stained images classified as Benign, in situ, Invasive, and Normal categories (ICIAR2018_BACH_Challenge) has… More >