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 >

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 >

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 >

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 >

Tengfei Li¹, Wenhui Zhang¹, Ke Mi¹, Qingming Lin¹, Shuangwei Zhao^2,*, Jiayi Song²

Energy Engineering, Vol.121, No.7, pp. 1991-2007, 2024, DOI:10.32604/ee.2024.049460 - 11 June 2024

Abstract Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers (LVCBs). A fault diagnosis algorithm based on an improved Sparrow Search Algorithm (ISSA) optimized Backpropagation Neural Network (BPNN) is proposed to improve the operational safety of LVCB. Taking the 1.5kV/4000A/75kA LVCB as an example. According to the current operating characteristics of the energy storage motor, fault characteristics are extracted based on Empirical Wavelet Transform (EWT). Traditional BPNN has problems such as difficulty adjusting network weights and thresholds, being sensitive to initial weights, and quickly falling into More >

Qi Zhuang^1,*, Dong Liu², Zhuo Chen³

Energy Engineering, Vol.121, No.3, pp. 821-834, 2024, DOI:10.32604/ee.2023.044054 - 27 February 2024

Abstract Oil and gas pipelines are affected by many factors, such as pipe wall thinning and pipeline rupture. Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management. Aiming at the shortcomings of the BP Neural Network (BPNN) model, such as low learning efficiency, sensitivity to initial weights, and easy falling into a local optimal state, an Improved Sparrow Search Algorithm (ISSA) is adopted to optimize the initial weights and thresholds of BPNN, and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established. Taking 61 sets More >

Xiaoge Wei^1,2,*, Yuming Zhang^1,2, Huaitao Song^1,2, Hengjie Qin^1,2, Guanjun Zhao³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1295-1316, 2024, DOI:10.32604/cmes.2023.045096 - 29 January 2024

Abstract Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years. As part of this effort, an enhanced sparrow search algorithm (MSSA) was proposed. Firstly, the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm. Secondly, the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima. Finally, the local search mechanism based on the mountain climbing method was incorporated into… More >

Xiangyan Tang^1,2, Dengfang Feng^2,*, KeQiu Li¹, Jingxin Liu², Jinyang Song³, Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2789-2802, 2023, DOI:10.32604/cmc.2023.031304 - 31 October 2022

Abstract Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends. Back propagation (BP) neural network is a widely used prediction method. To reduce its probability of falling into local optimum and improve the prediction accuracy, we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm (MSSA). The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm (SSA). Three strategies are designed to improve the SSA to enhance its optimization-seeking ability, leading More >

G. Ignisha Rajathi¹, R. Ramesh Kumar², D. Ravikumar³, T. Joel⁴, Seifedine Kadry^4,5, Chang-Won Jeong⁶, Yunyoung Nam^7,*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1793-1806, 2023, DOI:10.32604/csse.2023.024674 - 15 June 2022

Abstract Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using medical imaging becomes an essential task, automated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning models. With this motivation, this paper introduces a novel IoMT and cloud enabled BT diagnosis model, named IoMTC-HDBT. The IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging (MRI) brain images and transmit them to the cloud server. Besides,… More >

Minxi Rong¹, Yong Li^1,*, Xiaoli Guo^1,*, Tao Zong², Zhiyuan Ma², Penglei Li²

Oncologie, Vol.24, No.2, pp. 309-327, 2022, DOI:10.32604/oncologie.2022.021256 - 29 June 2022

Abstract Objectives: The ERα biological activity prediction model is constructed by the compound molecular data of the anti-breast cancer therapeutic target ERα and its biological activity data, which improves the screening efficiency of anti-breast cancer drug candidates and saves the time and cost of drug development. Methods: In this paper, Ridge model is used to screen out molecular descriptors with a high degree of influence on the biological activity of Erα and divide datasets with different numbers of the molecular descriptors by screening results. Random Forest (RF) is trained by Root Mean Square Error (RMSE) and Coefficient of… More >