Feng Lv^*, Huili Chu, Cheng Yang, Jiajie Zhang

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072915 - 12 January 2026

Abstract To address the issue that hybrid flow shop production struggles to handle order disturbance events, a dynamic scheduling model was constructed. The model takes minimizing the maximum makespan, delivery time deviation, and scheme deviation degree as the optimization objectives. An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed. An improved multi-objective Grey Wolf (IMOGWO) optimization algorithm is designed by combining the “job-machine” two-layer encoding strategy, the timing-driven two-stage decoding strategy, the opposition-based learning initialization population strategy, the POX crossover strategy, the dual-operation dynamic mutation strategy, and the variable neighborhood search… More >

Mohammed Debakla^1,*, Ali Mezaghrani¹, Khalifa Djemal², Imane Zouaneb¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-21, 2026, DOI:10.32604/cmc.2025.071927 - 09 December 2025

Abstract Magnetic Resonance Imaging (MRI) has a pivotal role in medical image analysis, for its ability in supporting disease detection and diagnosis. Fuzzy C-Means (FCM) clustering is widely used for MRI segmentation due to its ability to handle image uncertainty. However, the latter still has countless limitations, including sensitivity to initialization, susceptibility to local optima, and high computational cost. To address these limitations, this study integrates Grey Wolf Optimization (GWO) with FCM to enhance cluster center selection, improving segmentation accuracy and robustness. Moreover, to further refine optimization, Fuzzy Entropy Clustering was utilized for its distinctive features… More >

Adeel Munawar¹, Muhammad Nadeem Ali², Awais Qasim³, Byung-Seo Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1189-1211, 2025, DOI:10.32604/cmes.2025.071876 - 30 October 2025

Abstract Cyber-physical systems (CPS) represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing, healthcare, and autonomous infrastructure. However, their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats, potentially leading to operational failures and data breaches. Furthermore, CPS faces significant threats related to unauthorized access, improper management, and tampering of the content it generates. In this paper, we propose an intrusion detection system (IDS) optimized for CPS environments using a hybrid approach by combining a nature-inspired feature selection scheme, such as Grey Wolf Optimization (GWO),… More >

Tayfun Abut^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4023-4042, 2025, DOI:10.32604/cmc.2025.066920 - 23 September 2025

Abstract In this study, a dynamic model for an inverted pendulum system (IPS) attached to a car is created, and two different control methods are applied to control the system. The designed control algorithms aim to stabilize the pendulum arms in the upright position and the car to reach the equilibrium position. Grey Wolf Optimization-based Linear Quadratic Regulator (GWO-LQR) and GWO-based Fuzzy LQR (FLQR) control algorithms are used in the control process. To improve the performance of the LQR and FLQR methods, the optimum values of the coefficients corresponding to the foot points of the membership… More >

Changsheng Zhu^1,2,*, Jintao Miao¹, Zihao Gao^3,*, Shuo Liu¹, Jingjie Li¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4313-4340, 2025, DOI:10.32604/cmc.2025.067157 - 30 July 2025

Abstract As the demand for advanced material design and performance prediction continues to grow, traditional phase-field models are increasingly challenged by limitations in computational efficiency and predictive accuracy, particularly when addressing high-dimensional and complex data in multicomponent systems. To overcome these challenges, this study proposes an innovative model, LSGWO-BP, which integrates an improved Grey Wolf Optimizer (GWO) with a backpropagation neural network (BP) to enhance the accuracy and efficiency of quasi-phase equilibrium predictions within the KKS phase-field framework. Three mapping enhancement strategies were investigated–Circle-Root, Tent-Cosine, and Logistic-Sine mappings–with the Logistic mapping further improved via Sine perturbation… More >

Helen Shin Huey Wee, Nur Syazreen Ahmad^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1301-1354, 2025, DOI:10.32604/cmes.2025.063611 - 30 May 2025

Abstract This paper reviews recent advancements in system identification methods for perturbed motorized systems, focusing on brushed DC motors, brushless DC motors, and permanent magnet synchronous motors. It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms, highlighting their advantages, limitations, and applications. The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads, noise, and friction. A comparative performance analysis is also included to assess several widely used optimization methods, including least squares (LS), particle swarm optimization (PSO), grey wolf optimizer (GWO), bat algorithm… More >

Xiangyang Cao^1,2, Yaojie Zheng^1,2, Hanbin Xiao^1,2,*, Min Xiao^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 289-334, 2025, DOI:10.32604/cmes.2025.063954 - 11 April 2025

Abstract This study investigates the Maximum Power Point Tracking (MPPT) control method of offshore wind-photovoltaic hybrid power generation system with offshore crane-assisted. A new algorithm of Global Fast Integral Sliding Mode Control (GFISMC) is proposed based on the tip speed ratio method and sliding mode control. The algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low jitter. An offshore wind power generation system model is presented to verify the algorithm effect. An offshore More >

Yu Zhang^*, Sheng Wang, Fanming Zeng, Yijie Lin

Energy Engineering, Vol.122, No.3, pp. 1137-1151, 2025, DOI:10.32604/ee.2025.060945 - 07 March 2025

Abstract With the development of renewable energy technologies such as photovoltaics and wind power, it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement. To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy, while simultaneously enhancing user satisfaction on the demand side, this paper introduces an improved multi-objective Grey Wolf Optimizer based on Cauchy variation. The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of… More > Graphic Abstract

Jianing Hao¹, Dan Yang², Guanxiong Ren¹, Ying Zhao³, Rangling Cao^4,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.1, pp. 141-159, 2025, DOI:10.32604/fdmp.2024.054469 - 24 January 2025

Abstract This study presents an innovative approach to calculating the failure probability of slopes by incorporating fuzzy limit-state functions, a method that significantly enhances the accuracy and efficiency of slope stability analysis. Unlike traditional probabilistic techniques, this approach utilizes a least squares support vector machine (LSSVM) optimized with a grey wolf optimizer (GWO) and K-fold cross-validation (CV) to approximate the limit-state function, thus reducing computational complexity. The novelty of this work lies in its application to one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) slope models, demonstrating its versatility and high precision. The proposed method consistently achieves… More > Graphic Abstract

Varada Rajkumar Kukkala¹, Surapaneni Phani Praveen², Naga Satya Koti Mani Kumar Tirumanadham³, Parvathaneni Naga Srinivasu^4,5,*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1085-1112, 2024, DOI:10.32604/csse.2024.053603 - 13 September 2024

Abstract This paper investigates the application of machine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely; Z-Score incorporated with Grey Wolf Optimization (GWO) as well as Interquartile Range (IQR) coupled with Ant Colony Optimization (ACO). Using a performance index, it is shown that when compared with the Z-Score and GWO with AdaBoost, the IQR and ACO, with AdaBoost are not very accurate (89.0% vs. 86.0%) and less discriminative (Area Under the Curve (AUC) score of 93.0% vs. 91.0%). The Z-Score and GWO… More >