Yuanfei Wei^1,2, Panpan Song³, Qifang Luo^3,4,*, Yongquan Zhou^1,2,3,4

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1235-1265, 2025, DOI:10.32604/cmc.2025.066527 - 29 August 2025

Abstract The combined heat and power economic dispatch (CHPED) problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints. This paper presents a hybrid differential evolution (DE) algorithm combined with an improved equilibrium optimizer (DE-IEO) specifically for the CHPED problem. The DE-IEO incorporates three enhancement strategies: a chaotic mechanism for initializing the population, an improved equilibrium pool strategy, and a quasi-opposite based learning mechanism. These strategies enhance the individual utilization capabilities of the equilibrium optimizer, while differential evolution boosts local exploitation and escape capabilities. The IEO enhances… More >

Wy-Liang Cheng¹, Wei Hong Lim^1,*, Kim Soon Chong¹, Sew Sun Tiang¹, Yit Hong Choo², El-Sayed M. El-kenawy^3,4, Amal H. Alharbi⁵, Marwa M. Eid^6,7

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2021-2050, 2025, DOI:10.32604/cmc.2025.064243 - 29 August 2025

Abstract The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets, particularly in industrial contexts where efficient data handling and process innovation are critical. Feature selection, an essential step in data-driven process innovation, aims to identify the most relevant features to improve model interpretability, reduce complexity, and enhance predictive accuracy. To address the limitations of existing feature selection methods, this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization (APO) algorithm. Specifically, we incorporate a specialized conversion mechanism to effectively adapt APO from continuous… More >

Chuan Yuan¹, Chang Liu^2,3, Shijun Chen¹, Weiting Xu^2,3, Jing Gou¹, Ke Xu^2,3, Zhengbo Li^4,*, Youbo Liu⁴

Energy Engineering, Vol.122, No.9, pp. 3573-3593, 2025, DOI:10.32604/ee.2025.067785 - 26 August 2025

Abstract The increasing penetration of second-life battery energy storage systems (SLBESS) in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries. This paper presents a novel model-free adaptive voltage control-embedded dung beetle-inspired heuristic optimization algorithm for optimal SLBESS capacity configuration and power dispatch. To simultaneously address the computational complexity and ensure system stability, this paper develops a comprehensive bilevel optimization framework. At the upper level, a dung beetle optimization algorithm determines the optimal SLBESS capacity configuration by minimizing total lifecycle costs while incorporating… More >

Jingrui Liu^1,2,*, Zhiwen Hou^1,2, Boyu Wang^1,2, Tianxiang Yin^3,4

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4729-4754, 2025, DOI:10.32604/cmc.2025.066138 - 30 July 2025

Abstract In response to the increasing global energy demand and environmental pollution, microgrids have emerged as an innovative solution by integrating distributed energy resources (DERs), energy storage systems, and loads to improve energy efficiency and reliability. This study proposes a novel hybrid optimization algorithm, DE-HHO, combining differential evolution (DE) and Harris Hawks optimization (HHO) to address microgrid scheduling issues. The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts. The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind, solar, micro-gas turbine, 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 >

Nisa Nacar Çıkan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3279-3327, 2025, DOI:10.32604/cmes.2025.065442 - 30 June 2025

Abstract This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks (UPDNs), focusing on the complex 123-Bus test system. Three scenarios are investigated: (1) simultaneous power loss reduction and voltage profile improvement, (2) minimization of voltage and current unbalance indices under various operational cases, and (3) multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index, active power loss, and current unbalance index. Unlike previous research that oftensimplified system components, this work maintains all equipment, including capacitor banks, transformers, and voltage regulators, to ensure realistic results. The study evaluates twelve metaheuristic More >

Ghada Atteia^1,*, Mohammed Dabboor², Konstantinos Karantzalos³, Maali Alabdulhafith¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1747-1767, 2025, DOI:10.32604/cmc.2025.063363 - 09 June 2025

Abstract This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of… More >

Murad Khan^1,*, Mohammed Faisal¹, Fahad R. Albogamy², Muhammad Diyan³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2027-2052, 2025, DOI:10.32604/cmes.2025.063764 - 30 May 2025

Abstract The rapid advancements in distributed generation technologies, the widespread adoption of distributed energy resources, and the integration of 5G technology have spurred sharing economy businesses within the electricity sector. Revolutionary technologies such as blockchain, 5G connectivity, and Internet of Things (IoT) devices have facilitated peer-to-peer distribution and real-time response to fluctuations in supply and demand. Nevertheless, sharing electricity within a smart community presents numerous challenges, including intricate design considerations, equitable allocation, and accurate forecasting due to the lack of well-organized temporal parameters. To address these challenges, this proposed system is focused on sharing extra electricity… More >

Hui Xu, Jiahui Chen^*, Zhonghao Hu

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4495-4513, 2025, DOI:10.32604/cmc.2025.062189 - 19 May 2025

Abstract Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the Metaheuristic Algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization Algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in… More >

David Conway, Paolina Centonze^*

Journal of Cyber Security, Vol.7, pp. 71-78, 2025, DOI:10.32604/jcs.2025.063711 - 14 May 2025

Abstract Ransomware is a variant of malicious software that aims to encrypt data or whole systems to lock out the intended users. The attackers then demand a ransom for the decryption key to allow the intended users access to their data or system again. Ransomware attacks have the potential to be used against industries like healthcare and finance, as well as against the public sector, have threatened and forced the operations of key infrastructure used by millions to cease, and extorted millions and millions of dollars from victims. Automated methods have been designed and implemented to More >