Jian Wang, Hui Qi, Lingyi Ji^*, Zhengya Tang, Hui Qian

Energy Engineering, Vol.122, No.11, pp. 4635-4651, 2025, DOI:10.32604/ee.2025.068667 - 27 October 2025

Abstract This paper proposes a cost-optimal energy management strategy for reconfigurable distribution networks with high penetration of renewable generation. The proposed strategy accounts for renewable generation costs, maintenance and operating expenses of energy storage systems, diesel generator operational costs, typical daily load profiles, and power balance constraints. A penalty term for power backflow is incorporated into the objective function to discourage undesirable reverse flows. The Bald Eagle Search (BES) meta-heuristic is adopted to solve the resulting constrained optimization problem. Numerical simulations under multiple load scenarios demonstrate that the proposed method effectively reduces operating cost while preventing More >

Xu Chen^1,*, Shuai Wang¹, Kaixun He²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1779-1806, 2025, DOI:10.32604/cmc.2025.066543 - 29 August 2025

Abstract Accurate and reliable photovoltaic (PV) modeling is crucial for the performance evaluation, control, and optimization of PV systems. However, existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency. To address these challenges, we propose an adaptive multi-learning cooperation search algorithm (AMLCSA) for efficient identification of unknown parameters in PV models. AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises. It enhances the original cooperation search algorithm in two key aspects: (i) an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights, allowing better individuals to More >

Hui Lv^1,#, Yuer Yang^2,3,4,#, Yifeng Lin^2,3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 925-953, 2025, DOI:10.32604/cmc.2025.065709 - 29 August 2025

Abstract It is evident that complex optimization problems are becoming increasingly prominent, metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional, nonlinear problems. However, the traditional Sparrow Search Algorithm (SSA) suffers from limited global search capability, insufficient population diversity, and slow convergence, which often leads to premature stagnation in local optima. Despite the proposal of various enhanced versions, the effective balancing of exploration and exploitation remains an unsolved challenge. To address the previously mentioned problems, this study proposes a multi-strategy collaborative improved SSA, which systematically integrates four complementary strategies: (1) the Northern Goshawk Optimization (NGO) mechanism… 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 >

Chaoliang Wang^1,*, Qi Fu², Zhaohui Li¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3707-3729, 2025, DOI:10.32604/cmc.2025.062795 - 03 July 2025

Abstract Cluster-based models have numerous application scenarios in vehicular ad-hoc networks (VANETs) and can greatly help improve the communication performance of VANETs. However, the frequent movement of vehicles can often lead to changes in the network topology, thereby reducing cluster stability in urban scenarios. To address this issue, we propose a clustering model based on the density peak clustering (DPC) method and sparrow search algorithm (SSA), named SDPC. First, the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads (CHs). More >

Pei Hu¹, Yibo Han¹, Jeng-Shyang Pan^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4485-4501, 2025, DOI:10.32604/cmc.2024.059773 - 06 March 2025

Abstract Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images. This paper approaches the topic as an optimization problem and uses the bald eagle search (BES) algorithm to achieve optimal results. In our proposed model, gamma correction and Retinex address color cast issues and enhance image edges and details. The final enhanced image is obtained through color balancing. The BES algorithm seeks the optimal solution through the selection, search, and swooping stages. However, it is prone to getting stuck in local optima and converges slowly. To overcome these limitations, we propose… More >

Kai Liu¹, Meizhao Liu¹, Ming Tang¹, Chen Zhang^2,*, Junwu Zhu^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2893-2908, 2025, DOI:10.32604/cmc.2024.057074 - 17 February 2025

Abstract The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to More >

Mohamad Khairulamirin Md Razali^1,*, Masri Ayob², Abdul Hadi Abd Rahman², Razman Jarmin³, Chian Yong Liu³, Muhammad Maaya³, Azarinah Izaham³, Graham Kendall^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1233-1288, 2025, DOI:10.32604/cmes.2025.060481 - 27 January 2025

Abstract The Cross-domain Heuristic Search Challenge (CHeSC) is a competition focused on creating efficient search algorithms adaptable to diverse problem domains. Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process. Numerous selection hyper-heuristics have different implementation strategies. However, comparisons between them are lacking in the literature, and previous works have not highlighted the beneficial and detrimental implementation methods of different components. The question is how to effectively employ them to produce an efficient search heuristic. Furthermore, the algorithms that competed in the inaugural CHeSC have not been collectively reviewed. This… More >

Yu Zhang, Daoyu Zhang^*, Tiezhou Wu

Energy Engineering, Vol.122, No.1, pp. 203-220, 2025, DOI:10.32604/ee.2024.056244 - 27 December 2024

Abstract Precisely estimating the state of health (SOH) of lithium-ion batteries is essential for battery management systems (BMS), as it plays a key role in ensuring the safe and reliable operation of battery systems. However, current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation. Additionally, the Elman neural network, which is commonly employed for SOH estimation, exhibits several drawbacks, including slow training speed, a tendency to become trapped in local minima, and the initialization of weights and thresholds using pseudo-random numbers, leading to unstable model performance.… More >

Yaren Aydın¹, Gebrail Bekdaş^1,*, Sinan Melih Nigdeli¹, Zong Woo Geem^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2471-2499, 2024, DOI:10.32604/cmes.2024.056693 - 31 October 2024

Abstract Dynamic impacts such as wind and earthquakes cause loss of life and economic damage. To ensure safety against these effects, various measures have been taken from past to present and solutions have been developed using different technologies. Tall buildings are more susceptible to vibrations such as wind and earthquakes. Therefore, vibration control has become an important issue in civil engineering. This study optimizes tuned mass damper inerter (TMDI) using far-fault ground motion records. This study derives the optimum parameters of TMDI using the Adaptive Harmony Search algorithm. Structure displacement and total acceleration against earthquake load More >