Soukaina Mjahed¹, Ouail Mjahed^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.081953 - 08 May 2026

Abstract The rapid emergence of sophisticated, dynamic, and rare or previously unseen attack pattern exposes fundamental limitations of conventional intrusion detection systems (IDS) based on static learning architectures. While deep learning (DL) models have demonstrated strong performance by capturing complex spatial and temporal traffic patterns, existing DL-based IDS largely rely on fixed decision structures, restricting adaptability to evolving threats. Furthermore, current hybrid DL-metaheuristic approaches typically use such metaheuristics as offline or auxiliary optimizers, without interacting with the deep model’s internal latent representations. This paper introduces a novel co-evolutionary IDS that establishes a tight, bidirectional coupling between… More >

Mengqi Liu, Haiqing Yao^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078181 - 08 May 2026

Abstract For the wide-coverage application scenarios, wireless rechargeable sensor networks are normally divided into multiple clusters to support the diversity and flexibility for monitoring, and use the mobile charger (MC) to support the sustainable charging of the network. Many efforts focus on optimizing the cluster head selection and mobile charger scheduling to improve the network energy efficiency and reliability. However, the existing work tends to use fixed triggering mechanism for cluster head (CH) rotation, and may trigger the rotation either too early or too late. Besides, the existing charging triggering mechanisms cannot track the changes in… More >

Xincheng Han¹, Hongyan Ma^1,2,3,*, Shuo Meng¹, Chengzhi Ren¹

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072906 - 27 March 2026

Abstract Lithium-ion (Li-ion) batteries stand as the dominant energy storage solution, despite their widespread adoption, precisely determining the state of charge (SOC) continues to pose significant difficulties, with direct implications for battery safety, operational reliability, and overall performance. Current SOC estimation techniques often demonstrate limited accuracy, particularly when confronted with complex operational scenarios and wide temperature variations, where their generalization capacity and dynamic adaptation prove insufficient. To address these shortcomings, this work presents a PSO-TCN-Transformer network model for SOC estimation. This research uses the Particle Swarm Optimization (PSO) method to automatically configure the architectural parameters of… More >

Rui Yao^1,2, Yuye Wang^1,2,*, Fei Yu^1,2,3,*, Hongrun Wu^1,2, Zhenya Diao^1,2

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.073861 - 12 March 2026

Abstract Path planning for Unmanned Aerial Vehicles (UAVs) in complex environments presents several challenges. Traditional algorithms often struggle with the complexity of high-dimensional search spaces, leading to inefficiencies. Additionally, the non-linear nature of cost functions can cause algorithms to become trapped in local optima. Furthermore, there is often a lack of adequate consideration for real-world constraints, for example, due to the necessity for obstacle avoidance or because of the restrictions of flight safety. To address the aforementioned issues, this paper proposes a dynamic weighted spherical particle swarm optimization (DW-SPSO) algorithm. The algorithm adopts a dual Sigmoid-based More >

Zezhong Liu, Jinyu Guo, Xingxu Zhu^*, Junhui Li

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.072679 - 27 February 2026

Abstract With the increasing penetration of renewable energy, the coordination of energy storage with thermal power for frequency regulation has become an effective means to enhance grid frequency security. Addressing the challenge of improving the frequency regulation performance of a thermal-storage primary frequency regulation system while reducing its associated losses, this paper proposes a multi-dimensional cooperative optimization strategy for the control parameters of a combined thermal-storage system, considering regulation losses. First, the frequency regulation losses of various components within the thermal power unit are quantified, and a calculation method for energy storage regulation loss is proposed,… More >

Fazal Ur Rehman¹, Yazeed Alkhrijah², Syed Muhammad Usman³, Muhammad Irfan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.076332 - 26 February 2026

Abstract Automated detection of Motor Imagery (MI) tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation. Prediction of MI tasks can be performed with the help of Electroencephalogram (EEG) signals recorded by placing electrodes on the scalp of subjects; however, accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process, the extraction of a feature vector with high interclass variance, and accurate classification. The proposed method consists of preprocessing, feature extraction, and classification. First, EEG signals are denoised using a… More >

Viorel Mînzu^1,*, Iulian Arama²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.075619 - 10 February 2026

Abstract This work addresses optimality aspects related to composite laminates having layers with different orientations. Regression Neural Networks can model the mechanical behavior of these laminates, specifically the stress-strain relationship. If this model has strong generalization ability, it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem (OP) related to the orientations of composite laminates. To solve OPs, this paper proposes an optimization framework (OFW) that connects the two components, the optimal solution search mechanism and the RNN model. The OFW has two modules: the search mechanism (Adaptive… More >

Le Thi Hong Van^1,*, Le Duc Thuan¹, Pham Van Huong¹, Nguyen Hieu Minh²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075027 - 10 February 2026

Abstract Optimizing convolutional neural networks (CNNs) for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy. This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection. Unlike conventional single-objective approaches, the proposed method formulates a global multi-objective fitness function that integrates accuracy, precision, recall, and model size (speed/model complexity penalty) with adjustable weights. This design enables both single-objective and weighted-sum multi-objective optimization, allowing adaptive selection of optimal CNN configurations for diverse deployment… More >

Fei Yu^1,2,3,*, Zhenya Diao^1,2, Hongrun Wu^1,2,*, Yingpin Chen^1,3, Xuewen Xia^1,2, Yuanxiang Li^2,3,4

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072593 - 10 February 2026

Abstract Feature selection serves as a critical preprocessing step in machine learning, focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms. Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges. However, there are inherent limitations in Particle Swarm Optimization, such as the delicate balance between exploration and exploitation, susceptibility to local optima, and suboptimal convergence rates, hinder its performance. To tackle these issues, this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization, tailored for wrapper-based feature selection. The… More >

Mong-Fong Horng^1,#, Thanh-Lam Nguyen^1,#, Thanh-Tuan Nguyen^2,*, Chin-Shiuh Shieh^1,*, Lan-Yuen Guo³, Chen-Fu Hung⁴, Chun-Chih Lo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075064 - 29 January 2026

Abstract The global population is rapidly expanding, driving an increasing demand for intelligent healthcare systems. Artificial intelligence (AI) applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend. Among these applications, mouth motion tracking and mouth-state detection represent an important direction, providing valuable support for diagnosing neuromuscular disorders such as dysphagia, Bell’s palsy, and Parkinson’s disease. In this study, we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices. The proposed system integrates the Facial… More >