Feng Lv^*, Xin Xu, Cheng Yang, Yixuan Tang

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079917 - 08 May 2026

Abstract To ensure an effective disturbance response and maintain continuous production in hybrid flow shops, this paper focuses on the design of a rescheduling method. A rescheduling model is constructed that minimizes the makespan, total tardiness, and scheme deviation degree. A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling. The Whale Optimization Algorithm (WOA) is improved by integrating the good point set theory, nonlinear control parameter strategy, and Differential Evolution (DE) algorithm. Moreover, non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to More >

Yan-Hao Huang^*, Chung-Ming Kao

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079381 - 09 April 2026

Abstract Photovoltaic (PV) equivalent-circuit models are widely used for performance evaluation and diagnostics, but their usefulness relies on both accurate calibration and interpretable understanding of how parameters shape current–voltage (I–V) behavior. For nonlinear and strongly coupled PV models, conventional global sensitivity analysis can be computationally demanding and offer limited insight into effect direction and operating-point dependence. This study presents an method-oriented framework that integrates nature-inspired optimization with surrogate-based explainable global sensitivity analysis under a specified operating condition. The Starfish Optimization Algorithm (SFOA) is first used for parameter identification by searching for the optimal parameter set that… More >

Xiansong Xie^1,*, Xiaoqian Qian²

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.074148 - 31 March 2026

Abstract Correlation function of acceleration responses-based damage identification methods has been developed and employed, while they still face the difficulty in identifying local or minor structural damages. To deal with this issue, a robust structural damage identification method is developed, integrating a modified holistic swarm optimization (MHSO) algorithm with a hybrid objective function. The MHSO is developed by combining Hammersley sequence-based population initialization, chaotic search around the worst solution, and Hooke-Jeeves pattern search around the best solution, thereby improving both global exploration and local exploitation capabilities. A hybrid objective function is constructed by merging acceleration correlation… More >

Md. Mottahir Alam¹, Mohammed K. Al Mesfer^2,3, Haroonhaider Sidhwa⁴, Mohd Danish^2,3, Asif Irshad Khan⁵, Tauheed Khan Mohd^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077236 - 30 March 2026

Abstract Ground water is a crucial ecological resource and source of drinking water to a great percentage of the world population. The quality of groundwater in an area with industrial emission and air pollution is an especially important issue that requires proper evaluation. This paper introduces a spatiotemporal deep learning model that incorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts. The given method is a combination of the Spatial–Temporal-Assisted Deep Belief Network (StaDBN) and a hybrid Whale Optimization Algorithm and Tiki-Taka Algorithms (WOA–TTA) that would model intricate patterns of contamination.… More >

Saeid Sheikhi^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077788 - 12 March 2026

Abstract Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems, yet existing methods often struggle with balancing exploration and exploitation across diverse problem landscapes. This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization (PWO) algorithm. The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves, also known as African wild dogs in the wild, particularly their unique consensus-based voting rally mechanism, a behavior fundamentally distinct from the social dynamics of grey wolves. In this innovative process, pack members explore different areas… More >

Yao-Hsin Chou¹, Cheng-Yen Hua¹, Ru-Wei Tseng¹, Shu-Yu Kuo^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075705 - 12 March 2026

Abstract The rapid growth of mobile and Internet of Things (IoT) applications in dense urban environments places stringent demands on future Beyond 5G (B5G) or Beyond 6G (B6G) networks, which must ensure high Quality of Service (QoS) while maintaining cost-efficiency and sustainable deployment. Traditional strategies struggle with complex 3D propagation, building penetration loss, and the balance between coverage and infrastructure cost. To address this challenge, this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate (GQTS-QNG) framework for 3D base-station deployment optimization. The problem is formulated as a multi-objective model… 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 >

Mehmet Bahadır Çetinkaya^1,*, Sevim Adige^2,3

Journal on Artificial Intelligence, Vol.8, pp. 89-106, 2026, DOI:10.32604/jai.2026.074459 - 13 February 2026

Abstract Math-inspired metaheuristic algorithms stand out with their simple algorithm structures and the inclusion of fewer control parameters. In this study, the recently proposed math-inspired Quadratic Interpolation Optimization (QIO) algorithm was improved as a clustering-based algorithm and then applied to retinal vessel segmentation. Afterwards, its performance was compared with the math-inspired Sine Cosine Algorithm (SCA) and Arithmetic Optimization Algorithm (AOA), which have been frequently applied to engineering problems. First, the performance of the QIO algorithm was analyzed in terms of sensitivity (Se), specificity (Sp), accuracy (Acc), and precision (Prec). An average success rate of approximately 70% or higher… More >

Yu-Xuan Zhou¹, Kai-Qing Zhou^1,*, Wei-Lin Chen¹, Zhou-Hua Liao¹, Di-Wen Kang^1,2

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075099 - 10 February 2026

Abstract The Pigeon-Inspired Optimization (PIO) algorithm constitutes a metaheuristic method derived from the homing behaviour of pigeons. Initially formulated for three-dimensional path planning in unmanned aerial vehicles (UAVs), the algorithm has attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation, coupled with advantages in real-time performance and robustness. Nevertheless, as applications have diversified, limitations in convergence precision and a tendency toward premature convergence have become increasingly evident, highlighting a need for improvement. This review systematically outlines the developmental trajectory of the PIO algorithm, with a particular focus on its core… More >

Hao Chen¹, Tong Xu¹, Yutian Huang², Dabo Xin^1,*, Changting Zhong^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075792 - 29 January 2026

Abstract Optimization problems are prevalent in various fields of science and engineering, with several real-world applications characterized by high dimensionality and complex search landscapes. Starfish optimization algorithm (SFOA) is a recently optimizer inspired by swarm intelligence, which is effective for numerical optimization, but it may encounter premature and local convergence for complex optimization problems. To address these challenges, this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm (MCPSFOA). The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA, which integrates the exploratory mechanisms of SFOA with the diverse search capacity of… More >