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 >

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 >

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 >

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 >

Geetanjali Dei^1,2, Deepak Kumar Gupta¹, Binod Kumar Sahu², Amitkumar V. Jha³, Bhargav Appasani^3,*, Nicu Bizon^4,5,*

Energy Engineering, Vol.122, No.8, pp. 3399-3431, 2025, DOI:10.32604/ee.2025.067357 - 24 July 2025

Abstract This paper presents an innovative and effective control strategy tailored for a deregulated, diversified energy system involving multiple interconnected area. Each area integrates a unique mix of power generation technologies: Area 1 combines thermal, hydro, and distributed generation; Area 2 utilizes a blend of thermal units, distributed solar technologies (DST), and hydro power; and Third control area hosts geothermal power station alongside thermal power generation unit and hydropower units. The suggested control system employs a multi-layered approach, featuring a blended methodology utilizing the Tilted Integral Derivative controller (TID) and the Fractional-Order Integral method to enhance… More >

Yuan Zou¹, Chao Lu^1,*, Lvjiang Yin², Xiaoyu Wen³

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2305-2330, 2025, DOI:10.32604/cmc.2025.065972 - 03 July 2025

Abstract The shop scheduling problem with limited buffers has broad applications in real-world production scenarios, so this research direction is of great practical significance. However, there is currently little research on the hybrid flow shop scheduling problem with limited buffers (LBHFSP). This paper deeply investigates the LBHFSP to optimize the goal of the total completion time. To better solve the LBHFSP, a multi-level subpopulation-based particle swarm optimization algorithm (MLPSO) is proposed, which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO (particle swarm optimization) algorithm. In MLPSO, firstly, considering the… More >

Jafar Aminu^1,2,*, Rohaya Latip^1,*, Zurina Mohd Hanafi¹, Shafinah Kamarudin¹, Danlami Gabi²

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2989-3011, 2025, DOI:10.32604/cmc.2025.062451 - 03 July 2025

Abstract As mobile edge computing continues to develop, the demand for resource-intensive applications is steadily increasing, placing a significant strain on edge nodes. These nodes are normally subject to various constraints, for instance, limited processing capability, a few energy sources, and erratic availability being some of the common ones. Correspondingly, these problems require an effective task allocation algorithm to optimize the resources through continued high system performance and dependability in dynamic environments. This paper proposes an improved Particle Swarm Optimization technique, known as IPSO, for multi-objective optimization in edge computing to overcome these issues. To this… More >