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Improved rime algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets

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JOURNAL OF BIG DATA 2025年第1期12卷 1-50页

作者： Zhu, Wei Hu, Yuxi Liu, Lei Cai, Zhennao Chen, Huiling Liang, Guoxi Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Cent South Univ Sch Math & Stat Changsha 410083 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China

In recent years, with the rapid development of higher education, China has actively constructed an evaluation framework of education quality. As an important part of higher education, Sino foreign cooperation in running schools plays an important role in the development of higher education. The newly released evaluation criteria for Sino foreign cooperative education cover a series of influencing factors. However, objectively determining which of these factors are crucial for the success of Sino foreign cooperative education is essential for strengthening its future development. To address this challenge, we propose an adaptive hunting mechanism that utilizes the latest rime algorithm and is enhanced through a criss-crossing mechanism, as well as a new ACrime algorithm. We conducted a comparative analysis of ACrime, the original rime, and several other highly acclaimed improved algorithms. The results indicate that ACrime exhibits excellent performance in multiple benchmark tests. Subsequently, we applied the ACrime algorithm to cluster the dataset of Sino foreign cooperative education, and then used the binary version of ACrime (bACrime) for feature selection. In the tenfold cross validation, more than half of the selected features were repeatedly identified, indicating their potential correlation with the development of Sino foreign cooperative education. Therefore, it is necessary to pay more attention to these influential indicators to support future development efforts.

关键词： rime algorithm Adaptive Hunting mechanism Feature selection Criss-crossing mechanism

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TErime: An Improved rime algorithm With Enhanced Exploration and Exploitation for Robust Parameter Extraction of Photovoltaic Models

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JOURNAL OF BIONIC ENGINEERING 2025年 1-22页

作者： Chen, Shi-Shun Jiang, Yu-Tong Chen, Wen-Bin Li, Xiao-Yang Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Parameter extraction of photovoltaic (PV) models is crucial for the planning, optimization, and control of PV systems. Although some methods using meta-heuristic algorithms have been proposed to determine these parameters, the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases. The unstable results will affect the reliable operation and maintenance strategies of PV systems. In response to this challenge, an improved rime optimization algorithm with enhanced exploration and exploitation, termed TErime, is proposed for robust and accurate parameter identification for various PV models. Specifically, the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity. Meanwhile, a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth. The TErime algorithm is applied to estimate the optimal parameters of the single diode model, double diode model, and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France, Photo Watt-PWP 201 and S75. According to the statistical analysis in 100 runs, the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions. All of our source codes are publicly available at https://***/dirge1/TErime.

关键词： Photovoltaic modeling rime algorithm Optimization problems Meta-heuristic algorithms Stability

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Automatic Generation Control of a Hybrid PV-Reheat Thermal Power System Using rime algorithm

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IEEE ACCESS 2024年 12卷 26919-26930页

作者： Ekinci, Serdar Can, Ozay Ayas, Mustafa Sinasi Izci, Davut Salman, Mohammad Rashdan, Mostafa Batman Univ Dept Comp Engn TR-72100 Batman Turkiye Recep Tayyip Erdogan Univ Dept Elect & Automat TR-53100 Rize Turkiye Karadeniz Tech Univ Dept Elect Elect Engn TR-61080 Trabzon Turkiye Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait

This study focuses on the automatic generation control (AGC) system, which is crucial for maintaining balance between power generation and demand in power systems. The implementation of the AGC system needs to be more precise due to the increasing uncertainty surrounding renewable energy sources (RESs) and changes in demand. The objective of this study is to investigate the AGC functions in a two-area hybrid power system that combines a PV system with a reheat thermal system. To improve system performance, we utilize a proportional-integral (PI) controller. We utilized a recently developed optimization method, rime, for tuning controller parameters. This technique has not been studied before in AGC processes. Furthermore, the optimization procedure utilizes a modified version of the integral of time-multiplied absolute error (ITAE) objective function. The study compares the performance of the rime-tuned PI controller under various scenarios, including changes in thermal system load, load variations in both areas, and robustness considerations, with well-known techniques in the literature, such as the black widow optimization algorithm (BWOA), the salp swarm algorithm (SSA), the shuffled frog leaping algorithm (SFLA), the firefly algorithm (FA) and the genetic algorithm (GA). Our comparative study demonstrates that the proposed controller outperforms state-of-the-art approaches in terms of overshoot values and damping durations for both system frequency and tie-line power changes. The study provides valuable information on the effectiveness of the rime-tuned PI controller in controlling AGC processes in complex hybrid power systems.

关键词： Automatic generation control rime algorithm hybrid PV-reheat thermal system ITAE frequency deviation

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Optimization configuration method for energy storage capacity of electric vehicles connected to distribution networks based on rime algorithm 6

Optimization configuration method for energy storage capacit...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Shang, Longlong Deng, Hao Ci, Hai Li, Yaqian China Southern Power Grid Shenzhen Power Supply Bureau Co. Ltd Shenzhen518100 China

ISBN: (纸本)9798350377798

In the process of optimizing the configuration of energy storage capacity for electric vehicles connected to the distribution network, it is necessary to consider a balance between economic and environmental benefits. To optimize energy storage capacity, reduce lifecycle costs, and improve energy utilization efficiency, a method based on the rime algorithm for optimizing the configuration of energy storage capacity for electric vehicle access to distribution networks is proposed. Considering the economic benefits of electric vehicle integration into the distribution network, three optimization objective functions are selected: full life cycle cost, renewable energy power change, and tie line utilization rate. Constraints that meet the objective factors are set to achieve the optimal objective factor. A model for optimizing the energy storage capacity of electric vehicle integration into the distribution network is constructed. Using the rime algorithm to solve the optimization configuration model for the energy storage capacity of electric vehicles connected to the distribution network, in order to obtain the optimal configuration results. The experimental results show that the proposed method has a good optimization effect on the energy storage capacity configuration of electric vehicle access to the distribution network, and can effectively improve the energy storage configuration capacity of electric vehicle access to the distribution network. © 2024 IEEE.

关键词： Distribution network Electric vehicle access Energy storage capacity Optimize configuration rime algorithm

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Cross and local optimal avoidance of rime algorithm: A segmentation study for COVID-19 X-ray images

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DISPLAYS 2024年 83卷

作者： Yuan, Chong Zhao, Dong Heidari, Ali Asghar Liu, Lei Chen, Yi Liang, Guoxi Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Tehran Iran Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Univ Key Lab Intelligent Informat Safety Emergency Zhe Wenzhou 325035 Peoples R China Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China

Image segmentation is a crucial technique in analyzing X-ray medical images as it aids in uncovering relevant information concealed within a patient's body, a pivotal aspect of the diagnostic process. The effectiveness of computer-aided diagnosis systems largely depends on the accuracy of the image processing methods. In recent years, multi-threshold image segmentation methods have found widespread application in medical image analysis. Despite the effectiveness of some renowned methods for binary threshold segmentation, the field still faces challenges due to the high cost of threshold computation. Metaheuristic algorithms hold the potential to address this issue as they can produce sufficiently reasonable solutions with manageable computational over-heads. While some similar methods have been proposed, the imbalance between exploration and exploitation results in instability as the number of thresholds increases. Consequently, these solutions suffer from reduced efficiency in computing thresholds. In this study, a variant of the latest rime algorithm, termed SLCrime, is proposed. This paper replaces the pseudo-random method with low-discrepancy Sobol sequences for solution initialization. Additionally, two methods aimed at avoiding local optima and promoting information exchange within the solution set are introduced, further enhancing its capability to search for optimal threshold sets for IS systems. Subsequently, a multi-threshold image segmentation model based on SLCrime is proposed and applied to segment 6 COVID-19 X-ray images. In the experiments, SLCrime is compared with 6 peer algorithms, and the results are evaluated using image segmentation accuracy, feature similarity index metrics (FSIM), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM). The analysis indicates that SLCrime achieves optimal thresholds at reasonable computational costs and outperforms other algorithms in terms of performance.

关键词： COVID-19 X-ray images Multi-threshold image segmentation Metaheuristic algorithm rime algorithm

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Combined ultra-short-term photovoltaic power prediction based on CEEMDAN decomposition and rime optimized AM-TCN-BiLSTM

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ENERGY 2025年 318卷

作者： Zhou, Daixuan Liu, Yujin Wang, Xu Wang, Fuxing Jia, Yan Inner Mongolia Univ Technol Sch Energy & Power Engn Hohhot 010051 Peoples R China Inner Mongolia Univ Technol Sch Elect Power Hohhot 010080 Peoples R China

Photovoltaic power prediction is crucial to the stable operation of the power system. In order to further improve the accuracy of photovoltaic power prediction, a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and rime-ice (rime) optimization algorithm and optimization Attention Mechanism (AM)-Time Convolutional Network (TCN)-Bidirectional Long Short-Term Memory Neural Network (BiLSTM) combined ultra-short-term photovoltaic power prediction model is proposed. First, the original power sequence is decomposed using CEEMDAN to obtain smoother data;then, for the inherent intermittency, variability, and stochasticity of PV power generation, a combined AM-TCN-BiLSTM prediction model is constructed to extract features and learn the PV power, and the rime simulates the growth and crossover behaviors of the mistletoe-particle populations with powerful global optimization functions. The hyperparameters of the prediction model are optimized by the rime algorithm, and the optimized hyperparameter prediction model is used to predict each subsequence obtained from the decomposition. Finally, the prediction results of each subsequence are integrated and reconstructed to obtain the final PV power prediction value. The simulation verification shows that the model can effectively improve the prediction accuracy compared with the comparison algorithm. In the experimental results, the MAE for the first and second predictive steps were recorded as 4.3116 and 5.0342, respectively. The RMSE values for these steps were 6.7357 and 8.5834, respectively. Additionally, the R2 showed a significant improvement, reaching 0.9879 for the first step and 0.9803 for the second step. These outcomes validate the effectiveness of the model proposed in this paper.

关键词： rime algorithm Attention mechanism Artificial intelligence algorithm Power prediction

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Multi-strategy ensemble binary rime optimization for feature selection

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NEUROCOMPUTING 2025年 639卷

作者： Yu, Sudan Chen, Huiling Heidari, Ali Asghar Liang, Guoxi Chen, Yi Chen, Zhiqing Jin, Xiaoxia Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Polytech Sch Intelligent Mfg Wenzhou 325035 Peoples R China

Feature selection is a critical process in machine learning and data mining that selects a subset of relevant features for model construction. This process helps reduce the dimensionality of the data, minimize computational cost, improve model performance, and enhance interpretability. An effective feature selection method can significantly impact the success of predictive modeling tasks. Recently, numerous algorithms have been developed to address the challenges associated with high-dimensional datasets. These algorithms range from traditional statistical methods to advanced metaheuristic methods. The rime approach, enhanced through reinforcement learning, presents a viable answer to the optimization challenge. Through reinforcement learning that switches between the dispersed foraging and hard-rime puncture mechanisms and comprehensive learning strategy, the multi-strategy ensembled rime method, named QCLFrime, reaches a more effective balance between local search and global exploration. This algorithm is validated on the IEEE CEC 2017 function test and compared with traditional and advanced algorithms. Subsequently, we develop a wrapper feature selection model based on the binary QCLFrime model. Besides, the binary QCLFrime-KNN classifier has excellent performance for the feature selection regarding fitness, error rate, the number of selected features and computational time on 14 publicly available high-dimensional datasets compared to the traditional and enhanced meta-heuristic algorithms. Relevant experimental findings highlight the proposed algorithm's outstanding efficacy, which surpasses most existing algorithms. Therefore, the proposed method is a valuable wrapper-mode feature selection tool.

关键词： Binary feature selection Comprehensive learning Dispersed foraging KNN Classifier Machine learning Q -learning rime algorithm

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Improved Snake Optimization algorithm for Global Optimization and Engineering Applications

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Scientific reports 2025年第1期15卷 18171页

作者： Kaiyuan Zheng Huiyong Liu Bopeng Li College of Computer Science Beijing Information Science and Technology University No. 55 Taihang Road Changping District 102206 Beijing China. College of Computer Science Beijing Information Science and Technology University No. 55 Taihang Road Changping District 102206 Beijing China. liuhy@***.

In engineering applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The Snake Optimization algorithm (SO) is a novel metaheuristic method with widespread use. However, SO has limitations, including reduced search efficiency in later stages and a tendency to get trapped in local optima, preventing full exploration of the solution space. To overcome these, this paper introduces the Multi-strategy Improved Snake Optimization algorithm (ISO), which integrates six key strategies. First, the Sobol sequence is used for population initialization, ensuring uniform distribution and enhancing global exploration. Second, the rime algorithm accelerates convergence and improves exploitation. Lens reverse learning further promotes exploration, avoiding local optima. Levy flight facilitates large random steps, balancing exploration and refinement. Adaptive step-size adjustment dynamically tunes the step size based on fitness, optimizing exploration-exploitation. Lastly, the Brownian random walk introduces local perturbations to fine-tune solutions. These strategies collectively improve convergence speed, stability, and optimization capability, ensuring an effective balance between exploration and exploitation. The ISO population distribution was evaluated using three uniformity algorithms: Average Nearest Neighbor Distance, Star Discrepancy, and Sum of Squared Deviations (SSD). ISO demonstrated improvements of 63.08%, 26.09%, and 8.88%, respectively, over SO. Its exploration-exploitation balance and convergence were analyzed on the 30-dimensional CEC-2017 benchmark functions. Additionally, ISO was tested on 23 classic benchmark functions, CEC-2011, and CEC-2017 benchmark functions. Results showed ISO's superior performance in convergence speed, stability, and global optimization. Further

关键词： Engineering applications Global optimization Metaheuristic algorithm rime algorithm Snake optimization algorithm

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An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

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Computers, Materials & Continua 2024年第6期79卷 3977-3999页

作者： Bo Zhu Xiaona Jing Lan Qiu Runbo Li College of Mechanical and Electrical Engineering Kunming University of Science&TechnologyKunming650500China

When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data *** imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the *** FCSSVM is an improved version of the traditional *** considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(rime)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification *** verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced *** experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significan

关键词： Imbalanced data classification Silhouette value Mahalanobis distance rime algorithm CS-SVM

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Arime-DWA: Robot Path Planning Utilizing Advanced rime Optimization and Dynamic Windows Approach

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IEEE ACCESS 2024年 12卷 191480-191497页

作者： Hu, Likun Cai, Chengjie Guangxi Univ Coll Elect Engn Nanning 530004 Guangxi Peoples R China

To overcome the limitations of insufficient optimization ability and the propensity to get stuck in local optima in the standard rime algorithm for global path planning, an advanced rime algorithm integrating the Artemisinin Optimization (AO) algorithm is proposed (Arime). Moreover, by integrating Arime with the strengthened Dynamic Windows Approach (DWA), the Arime-DWA algorithm is formed, which addresses the issue of the rime algorithm's lack of capability for dynamic path planning. Firstly, during the population initialization stage, the good-point set strategy is employed to ensure a uniform overall distribution of the Arime's initial population;in the soft rime search strategy of Arime, by integrating the comprehensive elimination mechanism of the AO, the Arime's global search capability is boosted;in the hard rime puncturing mechanism of Arime, the local clearing strategy of the AO is incorporated to boost the capabilities to get away from local optima;finally, the dynamic reverse learning strategy is adopted to strengthen the diversity of population. Theoretical analysis has proven that the time complexity of the Arime is the same as that of the rime algorithm. To validate the performance of the Arime, global path planning simulation experiments are conducted adopting the Arime and six comparative algorithms. Experimental results demonstrate that Arime outperforms other six algorithms with more robust path planning capabilities. Among them, the average path lengths solved by the Arime are reduced by 7.7%, 24.5%, and 26.9%, respectively. Finally, local path planning experiments with the Arime-DWA reveal its effectiveness in dynamic local path planning.

关键词： Heuristic algorithms Path planning Optimization Robots Standards Particle swarm optimization Prediction algorithms Search problems Inference algorithms Artificial bee colony algorithm rime algorithm artemisinin optimization algorithm dynamic windows approach mobile robot path planning

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