Chengyu Hou^1,2, Wenzao Li², Hanyun Li³, Kui Liu¹, Zhuoning Zhao¹, Hongping Shu^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081575 - 15 June 2026

Abstract The rapid growth of Internet of Things (IoT) and 5G technologies has led to a sharp increase in computing demands from wireless devices, making efficient task offloading a critical challenge. Key issues include reducing application latency, lowering the energy consumption of terminal devices, and improving overall system performance, all of which directly affect user experience. Traditional genetic algorithms (GA), inspired by biological evolution, have been widely used in task offloading, but they often suffer from slow convergence and a tendency to fall into local optima in complex scenarios, limiting their effectiveness. To address these drawbacks,… More >

Maryem Lachgar^1,*, Mansour Lmkaiti¹, Ibtissam Larhlimi¹, Imad Aattouri², Hicham Ouchitachen¹, Hicham Mouncif¹

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.075234 - 08 May 2026

Abstract This study introduces a hybrid routing protocol, Low Energy Adaptive Clustering Hierarchy—Ant Colony Optimization—Genetic Algorithm (LEACH-ACO-GA), for wireless sensor networks. It combines regional ant colony optimization for cluster head selection with inter-cluster routing based on a genetic algorithm. The proposed method reduces energy consumption from 6.9 J (LEACH Classic) to 5.6 J (LEACH-ACO-GA) and decreases latency from 460 to 390 ms, while maintaining a packet delivery ratio of 0.97. These values are averaged over 70 rounds based on 30 independent simulation runs conducted on networks with 50 and 200 nodes. The hybrid method extends network More >

M. A. El-Shorbagy^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079859 - 27 April 2026

Abstract This paper provides a thorough examination of Genetic Algorithms (GAs), a category of evolutionary computation methods derived from the concepts of natural selection and genetics. The main concept and operational principle of GAs are elucidated, highlighting the evolution of populations of candidate solutions across multiple generations to get optimal or near-optimal solutions for complicated problems. The paper delineates the sequential phases of a conventional GA, encompassing problem formulation, solution encoding, initialization of population, fitness evaluation, selection, crossover, mutation, and termination criteria, so offering a coherent framework for comprehending the algorithm’s functionality. Moreover, numerous prominent genetic… More > Graphic Abstract

Mirna Yunita¹, Xiabi Liu^1,*, Zhaoyang Hai¹, Rachmat Muwardi²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075073 - 12 March 2026

Abstract Designing appropriate loss functions is critical to the success of supervised learning models. However, most conventional losses are fixed and manually designed, making them suboptimal for diverse and dynamic learning scenarios. In this work, we propose an Adaptive Meta-Loss Network (Adaptive-MLN) that learns to generate task-agnostic loss functions tailored to evolving classification problems. Unlike traditional methods that rely on static objectives, Adaptive-MLN treats the loss function itself as a trainable component, parameterized by a shallow neural network. To enable flexible, gradient-free optimization, we introduce a hybrid evolutionary approach that combines Genetic Algorithms (GA) for global More >

Isam Bahaa Aldallal¹, Abdullahi Abdu Ibrahim^1,*, Saadaldeen Rashid Ahmed^2,3

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075212 - 10 February 2026

Abstract The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false 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 >

Gang Xu^1,2, Lefeng Wang¹, Yuwei Huang², Yong Lu³, Xin Liu⁴, Weijie Tan⁵, Zongpeng Li⁶, Xiu-Bo Chen^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074256 - 10 February 2026

Abstract The Intrusion Detection System (IDS) is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities. Clustering algorithms are often incorporated into IDS; however, conventional clustering-based methods face notable drawbacks, including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions. To overcome the performance limitations of existing methods, this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm (SC-QGA) and an improved quantum artificial bee colony algorithm hybrid K-means (IQABC-K). First, the SC-QGA algorithm is constructed based on… More >

Shengkang Zhang¹, Yong Jin^2,*, Soon Poh Yap^1,*, Haoyun Fan¹, Shiyuan Li³, Ahmed El-Shafie⁴, Zainah Ibrahim¹, Amr El-Dieb⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075351 - 29 January 2026

Abstract Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for… More >

Malvinder Singh Bali¹, Weiwei Jiang^2,*, Saurav Verma³, Kanwalpreet Kour⁴, Ashwini Rao³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070866 - 09 December 2025

Abstract In recent years, Blockchain Technology has become a paradigm shift, providing Transparent, Secure, and Decentralized platforms for diverse applications, ranging from Cryptocurrency to supply chain management. Nevertheless, the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency, scalability, and energy consumption. This study proposes an innovative approach to Blockchain network optimization, drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms. Specifically, we explore the application of genetic algorithms, particle swarm optimization, and related evolutionary techniques to enhance the performance of blockchain networks. The proposed methodologies More >

Erping Song^1,*, Zipin Yao²

Energy Engineering, Vol.122, No.12, pp. 5129-5147, 2025, DOI:10.32604/ee.2025.063827 - 27 November 2025

Abstract Wind farm layout optimization is a critical challenge in renewable energy development, especially in regions with complex terrain. Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm, where the wake effect, wind speed, types of wind turbines, etc., have an impact on the output power of the wind farm. To solve the optimization problem of wind farm layout under complex terrain conditions, this paper proposes wind turbine layout optimization using different types of wind turbines, the aim is to reduce the influence of the wake effect… More >