Yan Kong¹, Xinpeng Guo², Chih-Hsien Hsia^3,4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071269 - 12 January 2026

Abstract With the development of technology, diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization (CO) problems, particularly in tackling Non-deterministic Polynomial-time hard (NP-hard) problems such as the Traveling Salesman Problem (TSP). However, existing diffusion model-based solvers typically employ a fixed, uniform noise schedule (e.g., linear or cosine annealing) across all training instances, failing to fully account for the unique characteristics of each problem instance. To address this challenge, we present Graph-Guided Diffusion Solvers (GGDS), an enhanced method for improving graph-based diffusion models. GGDS leverages Graph Neural Networks (GNNs) to capture graph structural information… More >

Jalees Ur Rahman¹, Muhammad Hanif¹, Usman Haider^2,*, Saeed Mian Qaisar^3,*, Sarra Ayouni⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071251 - 12 January 2026

Abstract Deep neural networks have achieved excellent classification results on several computer vision benchmarks. This has led to the popularity of machine learning as a service, where trained algorithms are hosted on the cloud and inference can be obtained on real-world data. In most applications, it is important to compress the vision data due to the enormous bandwidth and memory requirements. Video codecs exploit spatial and temporal correlations to achieve high compression ratios, but they are computationally expensive. This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos. However, contrary… More >

Sarah M. Alhammad¹, Diaa Salama AbdElminaam^2,3,*, Asmaa Rizk Ibrahim⁴, Ahmed Taha²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069263 - 12 January 2026

Abstract Accurate parameter extraction of photovoltaic (PV) models plays a critical role in enabling precise performance prediction, optimal system sizing, and effective operational control under diverse environmental conditions. While a wide range of metaheuristic optimisation techniques have been applied to this problem, many existing methods are hindered by slow convergence rates, susceptibility to premature stagnation, and reduced accuracy when applied to complex multi-diode PV configurations. These limitations can lead to suboptimal modelling, reducing the efficiency of PV system design and operation. In this work, we propose an enhanced hybrid optimisation approach, the modified Spider Wasp Optimization… More >

Tsu-Yang Wu¹, Chengyuan Yu¹, Yanan Zhao², Saru Kumari³, Chien-Ming Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069139 - 12 January 2026

Abstract With the increasing complexity of logistics operations, traditional static vehicle routing models are no longer sufficient. In practice, customer demands often arise dynamically, and multi-depot systems are commonly used to improve efficiency. This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand. New customers appear in the delivery process at any time and are periodically optimized according to time slices. Then, we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem. The classical ant colony More >

Zhengyan Chang, Weiwei Wang, Mingliang Yang, Heng Yang, Qing Dong, Keyuan Zhao, Jie Yuwen^*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.066572 - 08 January 2026

Abstract To address the neglect of seismic performance in conventional double-girder bridge crane optimization, this paper introduces a time-history analysis-based seismic optimization methodology for crane structures. Using a 25-t nuclear power crane as a case study, a bridge frame finite element model is established and validated through static analysis, confirming its accurate representation of the physical entity’s mechanical behavior. Furthermore, with bridge mass reduction as the objective and structural strength, stiffness, stability, and seismic mechanical performance as constraints, an optimization model is developed employing the Whale Optimization Algorithm (WOA). More >

Zujun Ding, Qi Xiang, Chengyi Li, Mengyu Ma, Chutong Zhang, Xinfa Gu, Jiaming Shi, Hui Huang, Aoyun Xia, Wenjie Wang, Wan Chen, Ziluo Yu, Jie Ji^*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070442 - 27 December 2025

Abstract In order to address environmental pollution and resource depletion caused by traditional power generation, this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer (IDBO) with Variational Mode Decomposition (VMD). The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations. This study innovatively improves the traditional variational mode decomposition (VMD) algorithm, and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO self-optimization of key parameters K and a. On this basis, Fourier transform technology… More >

Tiankui Sun^*, Shuyi Zhuang, Yongling Lu, Wenqiang Xie, Ning Guo, Sudi Xu

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070213 - 27 December 2025

Abstract To ensure an uninterrupted power supply, mobile power sources (MPS) are widely deployed in power grids during emergencies. Comprising mobile emergency generators (MEGs) and mobile energy storage systems (MESS), MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies, offering advantages such as flexibility and high resilience through electricity delivery via transportation networks. This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator (DWIG) intended for MEG applications, employing an improved particle swarm optimization (PSO) algorithm based on a back-propagation neural network (BPNN). A… More >

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 >

Hongyu Wang¹, Wenwu Cui¹, Kai Cui¹, Zixuan Meng^2,*, Bin Li², Wei Zhang¹, Wenwen Li¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069576 - 27 December 2025

Abstract To achieve low-carbon regulation of electric vehicle (EV) charging loads under the “dual carbon” goals, this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multi-objective optimization. First, a dual-convolution enhanced improved Crossformer prediction model is constructed, which employs parallel 1 × 1 global and 3 × 3 local convolution modules (Integrated Convolution Block, ICB) for multi-scale feature extraction, combined with an Adaptive Spectral Block (ASB) to enhance time-series fluctuation modeling. Based on high-precision predictions, a carbon-electricity cost joint optimization model is further designed to balance economic, environmental, and grid-friendly objectives.… More > Graphic Abstract

Yong Li, Yuxuan Chen^*, Jiahui He, Guowei He, Chenxi Dai, Jingjing Tong, Wenting Lei

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069257 - 27 December 2025

Abstract Ensuring reliable power supply in urban distribution networks is a complex and critical task. To address the increased demand during extreme scenarios, this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants (VPPs). The proposed strategy improves system flexibility and responsiveness by optimizing the power adjustment of flexible resources. In the proposed strategy, the Gaussian Process Regression (GPR) is firstly employed to determine the adjustable range of aggregated power within the VPP, facilitating an assessment of its potential contribution to power supply support. Then, an optimal dispatch model based on More > Graphic Abstract