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6th International Conference on Intelligent Networks and Intelligent Systems (ICINIS)

作者： Dong Liyan Zhang Sainan Tian Geng Li Yongli Cai Guanyan NE Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9781479928088

Aim at the clustering result of traditional ant colony clustering algorithm is not accurate and the algorithm operating efficiency lower, many modified algorithm have been proposed. In this paper, we propose an ant colony clustering algorithm based on swarm intelligence. This algorithm not only improved from the method of calculating the similarity measure and enhanced ant memory, and also proposed a new policy of picking and dropping objects, which is picking the objects which have been formation of micro-clustering. Through experiment contrast, this paper presents the ant colony clustering algorithm based on swarm intelligence than the traditional ant colony algorithm in terms of efficiency, the correct rate of the clustering results have significantly improved.

关键词： Ant Colony Clustering algorithm swarm intelligence algorithm Data Mining Micro-Cluster

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Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning

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IEEE ACCESS 2025年 13卷 45964-45984页

作者： Wan, Qingyun Liu, Kai Bo, Yuyang Yuan, Xiya Li, Mufeng Wang, Xiaoqiu Chen, Chuang Liu, Lanying Wu, Wenzhong Nanjing Univ Chinese Med Affiliated Hosp Jiangsu Prov Hosp Chinese Med Nanjing 210029 Peoples R China Shanghai Univ Tradit Chinese Med Shanghai Municipal Hosp Tradit Chinese Med Shanghai 200071 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Insomnia is one of the most prevalent mental health disorders, and acupuncture has shown promise as an effective non-pharmacological treatment with minimal side effects. However, the response to acupuncture varies among individuals, emphasizing the need for personalized approaches to treatment. To address this, an innovative machine learning algorithm, Relief-NDPGWO-WSVM, is developed to predict insomnia response to acupuncture. Data from 51 patients, considering 19 key factors such as age, sleep quality, anxiety level, and insomnia severity, were analyzed to identify the most influential predictors of treatment outcomes. The proposed model combines the Relief algorithm for feature selection, a weighted support vector machine (WSVM) to map these factors to treatment efficacy, and the NDPGWO optimization method, which incorporates a nonlinear convergence factor, dynamic weight, and probability perturbation. Experimental results demonstrate that this model outperforms existing models in terms of precision, recall, F1-score, accuracy, and area under the curve (AUC). These results highlight the model's potential in accurately predicting acupuncture treatment outcomes for insomnia and suggest its broader applicability for enhancing clinical decision-making and optimizing healthcare resources for other related conditions.

关键词： Acupuncture Machine learning Sleep apnea Medical diagnostic imaging Hospitals Predictive models Accuracy Machine learning algorithms Diseases Brain modeling Insomnia disorder acupuncture machine learning efficacy evaluation swarm intelligence algorithm

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Metaheuristic-based weight optimization for robust deep reinforcement learning in continuous control

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swarm AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Ko, Gwang-Jong Huh, Jaeseok Korea Univ Sch Ind & Management Engn 145 Anam Ro Seoul 02841 South Korea Tech Univ Korea Dept Business Adm 237 Sangidaehak Ro Siheung Si 15073 Gyeonggi Do South Korea

In recent studies, the policy-based deep reinforcement learning (DRL) algorithms have exhibited superior performance in addressing continuous control problems, such as machine arms control and robot gait learning. However, these algorithms frequently face challenges inherent in gradient descent-based weight optimization methods, including susceptibility to local optima, slow learning speeds due to saddle points, approximation errors, and suboptimal hyperparameters. This instability leads to significant performance discrepancies among agent instances trained under identical settings, which complicates the practical application of reinforcement learning. To address this, we propose a metaheuristic-based weight optimization framework designed to mitigate learning instability in DRL for continuous control tasks. The proposed framework introduces a twophase optimization process, where an additional search phase using swarm intelligence algorithms is conducted at the end of the learning phase utilizing DRL. In numerical experiments, the proposed framework demonstrated superior and more stable performance compared to conventional DRL algorithms in robot locomotion tasks.

关键词： Deep reinforcement learning Continuous control Metaheuristic swarm intelligence algorithm Particle swarm optimization Grey wolf optimizer

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Online estimation method for extreme learning machine with kernels based on the multi-innovation theory and intelligent optimization strategy

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ISA TRANSACTIONS 2025年 156卷 142-153页

作者： Wang, Yanjiao Liu, Yiting Li, Weidi Deng, Muqing Wang, Kaiwei Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Elect Informat Prod Reliabi Guangzhou 511370 Peoples R China

In order to effectively model data online, a learning model must not only have the high adaptability of dynamic data but also keep the low complexity to meet the online computing requirements. In this paper, a novel multi-innovation online sequential extreme learning machine (MIOSELM) and its kernel version called multi- innovation kernel online sequential extreme learning machine (MIKOSELM) are proposed to establish the online estimation models based on p latest samples using the multi-innovation theory. Besides, a modified whale optimization algorithm (MWOA) is introduced to optimize the execution parameters of our algorithms and is capable of automatically searching a proper p as the practical need, which can further improve the adaptability performance of the online learning models. Finally, two different datasets (the UCI dataset and KDD99 dataset) are used to evaluate the superiority of our methods. Experimental results show that the accuracy, F-score, and G-mean of MIKOSELM are 98.25%, 98.11% and 98.63% on WDBC from the UCI dataset, and are 83.61%, 75.96% and 70.97% on the KDD99 dataset respectively. Besides, our MIKOSELM based on MWOA achieves F-score of 94.28% and 76.73% on Musk from the UCI dataset and the KDD99 dataset. These results validate the effectiveness of our proposed methods.

关键词： Kernel extreme learning machine Online learning model Parameter estimation Multi-innovation theory swarm intelligence algorithm

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Multiobjective Time-Space Grid Optimization of Lithium-Ion Electrochemical Model Based on Stability Analysis and Limit Region Decoupling Strategy

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IEEE ACCESS 2025年 13卷 44260-44277页

作者： Zhang, Libin Zhang, Minghang Shan, Hongying Xu, Guan Dong, Jingsheng Bai, Xuemeng Jilin Univ Sch Transportat Changchun 130022 Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130022 Peoples R China

Lithium-ion batteries have been widely adopted as power sources in automotive and other fields. Balancing voltage prediction accuracy and simulation time is critical for the performance of battery management systems (BMS). This study proposes a time space grid optimization method based on the single particle model with electrolyte dynamics (SPMe) to enhance the computational efficiency and accuracy of electrochemical models while achieving a trade-off between voltage estimation precision and computational time. A second-order finite difference algorithm and Fourier stability analysis were used to determine the convergence constraints for SPMe model concentration calculations. A multi-objective time space grid optimization model was developed and solved using a multi-objective artificial bee colony algorithm. The effectiveness of the proposed method was validated by comparing simulation results under different scenarios. The model provides optimal time space grid configurations for various current profiles, including 1C-rate constant-current charging, pulse current (PC), hybrid current (HC), and urban dynamometer driving schedule (UDDS) current profiles. The SPMe model achieved a voltage estimation root mean square error (RMSE) of less than 5 mV, while maintaining a simulation time of less than 10 ms per second. This work provides a new idea for voltage estimation accuracy and calculation time balance of existing battery management systems under complex operating conditions.

关键词： Computational modeling Cathodes Anodes Voltage Accuracy Optimization Estimation Electrolytes Batteries Integrated circuit modeling Lithium-ion battery electrochemical model stability analysis time-space grid optimization swarm intelligence algorithm

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Detection of phishing URLs with deep learning based on GAN-CNN-LSTM network and swarm intelligence algorithms

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第6-7期18卷 4979-4995页

作者： Albahadili, Abbas Jabr Saleh Akbas, Ayhan Rahebi, Javad Cankiri Karatekin Univ Dept Comp Engn Cankiri Turkiye Univ Surrey Inst Commun Syst Guildford England Istanbul Topkapi Univ Dept Software Engn Istanbul Turkiye

Phishing attacks are one of the challenges of the Internet and its users. Phishing attacks are an example of social engineering attacks based on deceiving users. In phishing attacks, fake pages that are very similar to legitimate pages are created on the Internet. In phishing attacks, the victim is directed to fake pages, and their valuable information is stolen. Most of the targets of phishing attacks include online payment services, banking, and online sales, so the losses of these attacks are significant. One way to detect phishing attacks is to use machine learning and deep learning methods. The challenge of machine learning and deep learning methods is intelligent feature selection. The lack of feature extraction and intelligent feature selection reduces the accuracy of learning methods in detecting phishing attacks. This paper presents a combined method with deep learning, machine learning, and swarm intelligence algorithms to detect phishing attacks. In the first phase, the dataset is balanced by deep learning based on the GAN. In the second step, the convolutional neural network extracts the primary features from the links and code of web pages. In the third step, the white shark optimizer algorithm selects the essential features. In the last step, the LSTM neural network classifies the samples. The proposed method has been evaluated on ISCX-URL-2016 and Phishtank datasets for feature extraction and selection. The proposed method's accuracy, precision, and sensitivity in the ISCX-URL-2016 dataset are 97.94, 97.82, and 97.76%, respectively. In the Phishtank dataset, the proposed method has accuracy, precision, and sensitivity of 96.78, 95.67, and 95.71%. The proposed method is more accurate than LSTM, CNN, CNN-LSTM, CNN + GA, DNN, VAE-DNN, and AE-DNN methods in detecting phishing.

关键词： Fake pages Phishing attacks Generative adversarial network (GAN) Convolutional neural network (CNN) Feature selection swarm intelligence algorithm

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Multi-Strategy Golden Jackal Optimization for engineering design

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-60页

作者： Yang, Wenbiao Lai, Tingfeng Fang, Yuhui Guangzhou Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Nanyang 639798 Singapore

The Golden Jackal Optimization algorithm (GJO) is a swarm intelligence (SI) algorithm utilized for tackling arduous problems in undefined search spaces. In order to address the issues of slow convergence and difficulty in evading local optima encountered by GJO, the Multi-Strategy Golden Jackal Optimization (MSGJO) algorithm is proposed. Three strategies were implemented to enhance the original GJO. Firstly, the utilization of a Good Point Set, as opposed to random numbers, improves the quality of the initial population. Secondly, the introduction of a Quasi-Opposition-Based Learning strategy facilitates the balancing of algorithmic exploration and exploitation capabilities, leading to enhanced convergence efficiency. Lastly, the maintenance of population diversity during the iterative process is achieved through the adoption of the Cauchy mutation and chaotic circle perturbation strategies. A comparative analysis was conducted on 23 benchmark functions using fourteen SI algorithms. Additionally, MSGJO was applied to the CEC2019 competition suite, successfully solving two distinct engineering design problems. The results demonstrate that MSGJO exhibits significant superiority not only in solving problems involving global optimal solutions but also in addressing challenging constrained problems. The code for this study will be available at https://***/ProfYangPaperCode/MSGJO-code.

关键词： swarm intelligence algorithm Golden Jackal Optimization algorithm Quasi-Opposition-Based Learning strategy Benchmark test function Engineering design

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Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image

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OPEN GEOSCIENCES 2020年第1期12卷 425-442页

作者： Ding Xiaohui Li Huapeng Li Yong Yang Ji Zhang Shuqing Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130012 Peoples R China Guangzhou Inst Geog Guangzhou 510070 Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzho 1119 Haibin Rd Guangzhou 511458 Peoples R China Guangdong Open Lab Geospatial Informat Technol & Guangzhou 510070 Peoples R China Key Lab Guangdong Utilizat Remote Sensing & Geog Guangzhou 510070 Peoples R China

swarm intelligence algorithms have been widely used in the dimensional reduction of hyperspectral remote sensing imagery. The ant colony algorithm (ACA), the clone selection algorithm (CSA), particle swarm optimization (PSO), and the genetic algorithm (GA) are the most representative swarm intelligence algorithms and have often been used as subset generation procedures in the selection of optimal band subsets. However, studies on their comparative performance for band selection have been rare. For this paper, we employed ACA, CSA, PSO, GA, and a typical greedy algorithm (namely, sequential floating forward selection (SFFS)) as subset generation procedures and used the average Jeffreys-Matusita distance (JM) as the objective function. In this way, the band selection algorithm based on ACA (BS-ACA), band selection algorithm based on CSA (BS-CSA), band selection algorithm based on PSO (BS-PSO), band selection algorithm based on GA (BS-GA), and band selection algorithm based on SFFS (BS-SFFS) were tested and evaluated using two public datasets (the Indian Pines and Pavia University datasets). To evaluate the algorithms' performance, the overall classification accuracy of maximum likelihood classifier and the average runtimes were calculated for band subsets of different sizes and were compared. The results show that the band subset selected by BS-PSO provides higher overall classification accuracy than the others and that its runtime is approximately equal to BS-GA's, higher than those of BS-ACA, BS-CSA, and BS-SFFS. However, the premature characteristic of BS-ACA makes it unacceptable, and its average JM is lower than those of other algorithms. Furthermore, BS-PSO converged in 500 generations, whereas the other three swarm-intelligence based algorithms either ran into local optima or took more than 500 generations to converge. BS-PSO was thus proved to be an excellent band selection method for a hyperspectral image.

关键词： swarm intelligence algorithm dimensional reduction band selection hyperspectral remote sensing imagery

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Enhanced Differential Evolution: A Novel Strategy for Global Optimization and Its Application to Optimal Microgrid Planning

Journal of Network Intelligence

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Journal of Network intelligence 2025年第1期10卷 1-19页

作者： Lin, Jin-Yang Shi, Xiang-Yu Zhu, Xi Lin, Jun-De Nguyen, Trong-The Research Center for Microelectronics Technology Fujian University of Technology Fuzhou350118 China Multimedia Communications Lab. VNU-HCM University of Information Technology Vietnam National University Ho Chi Minh City700000 Viet Nam

This study proposes a novel strategy called Enhanced Crossover and Mutation Differential Evolution (ECMDE) to address the limitations of the Differential Evolution (DE) algorithm. DE often suffers from slow convergence speed, low accuracy, and vulnerability to local optima. The ECMDE algorithm aims to overcome these issues by employing adaptive crossover and mutation techniques. A chaotic mapping approach is used to initialize the population in the initialization stage, ensuring that the initial population is well-distributed across the search space. In the improved mutation operation, a new mutation strategy is proposed using a dynamic adaptive factor instead of F to address the late convergence issue. Additionally, an adaptive crossover operator is employed. To evaluate the effectiveness of the ECMDE algorithm, it is compared with other algorithms using the CEC2013 benchmark function. The results of the comparative study demonstrate that the ECMDE algorithm exhibits superior global convergence ability and faster convergence speed compared to the other algorithms tested. Finally, the ECMDE algorithm is applied to optimal microgrid planning issues, providing effective optimization performance and reducing the operating cost of the microgrid scheme. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Crossover and mutation strategy Differential Evolution Energy storage optimization Microgrid planning swarm intelligence algorithm

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Financial Sequence Prediction Based on swarm intelligence algorithms of Internet of Things

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COMPUTATIONAL ECONOMICS 2022年第4期59卷 1465-1480页

作者： Liu, Jinquan Wei, Yupin Xu, Hongzhen Jilin Univ Sch Business Changchun 130000 Peoples R China Northeast Normal Univ Rutgers Univ Newark Inst Changchun 130000 Peoples R China

In order to accurately predict the financial time series and stock price fluctuation, in this study, metadata was used to ensure the objectivity of data for most of the swarm intelligence algorithms of the Internet of Things. The results show that the combination of theoretical methods and financial time series forecasting model can significantly improve the forecasting effect. At the same time, the proposed fuzzy theory also gives a totally different perspective to the modeling of financial time series which has stochastic and uncertain characteristics. Therefore, swarm intelligence algorithm and fuzzy theory are effectively combined, and their combination is very useful for the improvement of two financial fuzzy time series models. Because of their combination, the problem of stock price forecasting with different orders and different factors is well solved. Besides, it also provides two new solutions for such problems respectively as well as additional model choices for investors to avoid risks and improve returns.

关键词： Financial time series swarm intelligence algorithm Stock forecasting Fuzzy theory

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