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Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy

SCIENTIFIC REPORTS 2025年第1期15卷 1-29页

作者： Tian, Chen-Xu Li, Yu-Xuan Anhui Univ Finance & Econ Sch Stat & Appl Math Bengbu 10378 Anhui Peoples R China

Dung Beetle algorithm is an intelligent optimization algorithm with advantages in exploitation ability. However, due to the high randomness of parameters, premature convergence and other reasons, there is an imbalance between exploration and exploitation ability, and it is easy to fall into the problem of local optimal solution. The purpose of this study is to improve the optimization performance of dung beetle algorithm and explore its engineering application value. A balanced dung beetle optimization algorithm was proposed, and parabolic adaptive parameter R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document} was introduced to broaden the exploration range and slow down premature convergence. Gaussian distributed phase parameter beta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document} is introduced to reduce the randomness of parameters and stimulate the potential of algorithm exploitation. Levy flight escape strategy is introduced to balance the global exploration ability of the algorithm and fully explore the solution space. The effectiveness of the improved strategy is verified by comparing the CEC2017 benchmark function with the single strategy variant. The experimental results show that BDBO algorithm is superior to other algorithms in terms of convergence accuracy and generalization ability, and the accuracy improvement percentage is 35.29% compared with DBO algorithm. Wilcoxon rank sum test was used to evaluate the experimental results, which proved that the improvement strategy was statistically significant. Finally, the BDBO algorithm is applied to the tracking technol

关键词： Dung beetle optimization algorithm swarm intelligence optimization algorithm Parameter substitution Exploitation potential Escape strategy MPPT

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Tourism combination forecasting with swarm intelligence

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ANNALS OF TOURISM RESEARCH 2025年 111卷

作者： Li, Hengyun Guo, Honggang Wang, Jianzhou Wang, Yong Wu, Chunying Hong Kong Polytech Univ Sch Hotel & Tourism Management Hong Kong Peoples R China Dongbei Univ Finance & Econ Sch Data Sci & Artificial Intelligence Dalian Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan Shandong Peoples R China

Combination forecasting is an effective method for improving the accuracy of tourism demand. This study proposes an innovative combination strategy based on a multi-objective swarm intelligence optimization algorithm and, for the first time, examines whether and how this algorithm can enhance the performance of tourism demand combination forecasting. An empirical study conducted under several scenarios demonstrates that the proposed combination strategy enhances the interaction among single forecasts, leading to improved forecast accuracy and stability compared with traditional combination methods. The model remained effective even during the COVID-19 pandemic. The findings have a positive impact on predictive research, offering new insights and methodologies for tourism demand modeling.

关键词： Tourism demand forecasting Combination forecasts swarm intelligence optimization algorithm Multi-objective optimization

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Analysis of the influence of population distribution characteristics on swarm intelligence optimization algorithms

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INFORMATION SCIENCES 2023年第1期645卷

作者： Hu, Rongxin Bao, Liyong Ding, Hongwei Zhou, Dongmin Kong, Yan Yunnan Univ Sch Informat Sci & Engn Kunming 650000 Peoples R China

The distribution characteristic of populations is one of the main characteristics of population. The pattern of distribution characteristics determines the probability law of individual values. At the same time, the initialization of the population lays a foundation for the iterative process of the swarm intelligence optimization algorithm. To reveal the influence of population distribution characteristics on the swarm intelligence optimization algorithm, this paper proposes a variety of search strategies based on the populations with different distribution characteristics and analyzes the influence of population distribution characteristics on optimization process by comparing the test results of the optimization algorithm after implementing these strategies. First, a new population generator is designed that can transform the same initial population into a population with uniform and central peaking distributions. On this basis, the two kinds of populations are applied to the global and local search stages of the optimization process, and four different search strategies are formed. Among them, the global and local search strategies based on a uniformly distributed population are the traditional methods. Finally, the performance of the optimization algorithm using different search strategies is evaluated through 29 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. In addition, the algorithms are applied to solve the TSP problem. For CEC2017 in 100D, only 13 of the 29 test functions achieve the best optimization effect by using the traditional method, while the other 16 test functions achieve better search results by using the other three search strategies. The analysis shows that the population distribution characteristics have a great influence on the population optimization algorithm. The performance of the algorithm with different population distribution combination strategies is statistically superior to the traditional algorithm with

关键词： Population distribution characteristics swarm intelligence optimization algorithm Chaotic mapping CEC2017 TSP

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Product quality prediction based on RBF optimized by firefly algorithm

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Journal of Systems Engineering and Electronics 2024年第1期35卷 105-117页

作者： HAN Huihui WANG Jian CHEN Sen YAN Manting College of Electronics and Information Engineering Tongji UniversityShanghai 201804China

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used *** traditional product quality prediction models have many disadvantages,such as high complexity and low *** overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data *** the RBFFALFM is used to predict product *** expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

关键词： product quality prediction data pre-processing radial basis function swarm intelligence optimization algorithm

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Development of a hybrid VRF system energy consumption prediction model based on data partitioning and swarm intelligence algorithm

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JOURNAL OF BUILDING ENGINEERING 2023年 74卷

作者： He, Yuxuan Gong, Qijian Zhou, Zhenxin Chen, Huanxin Huazhong Univ Sci & Technol Dept Refrigerat & Cryogen Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Energy & Power Engn Dept Refrigerat & Cryogen Engn Wuhan Peoples R China

Accurately forecasting energy consumption is beneficial and pivotal for effectively managing variable refrigerant flow (VRF) systems. Changes in energy consumption provide an intuitive representation of the operating condition and the impact of possible faults. Energy consumption prediction is fundamental in energy conservation tasks such as fault diagnosis. Currently, many energy consumption prediction model have been applied for HVAC system widely. However black-box energy prediction models applied to VRF systems are still relatively few and crude. This study therefore proposed a hybrid energy consumption prediction method for VRF systems based on data partitioning and swarm intelligence algorithm. The model was based on back propagation neural network (BPNN), and utilized data partitioning techniques to identify the energy consumption patterns of the VRF system. For each pattern, a BPNN sub-model was trained using operating and energy consumption data of a VRF system. Then, swarm intelligence algorithm is adopted to determine the optimal architecture of each BPNN sub-model. The results demonstrate that the proposed hybrid models can achieve better prediction performance than single models like BP Neural Network. Root mean square error and mean absolute percentage error of the proposed predictive model is 202.49 and 1.95%, which has a 31.3% and 45.5% decrease compared to single BPNN model. At the same time, it is enlightening for other researchers to study the potential of data partitioning algorithm and swarm intelligence algorithm in the field of VRF system energy consumption prediction.

关键词： Back propagation neural network Data partitioning swarm intelligence optimization algorithm Energy consumption prediction VRF system

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Three-learning strategy particle swarm algorithm for global optimization problems

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INFORMATION SCIENCES 2022年 593卷 289-313页

作者： Zhang, Xinming Lin, Qiuying Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China

Social Learning Particle swarm optimization (SL-PSO) greatly improves the optimization performance of PSO. In solving complex optimization problems, however, it still has some deficiencies, such as poor search ability and low search efficiency. Hence, an improved SL-PSO, namely, Three-Learning Strategy PSO (TLS-PSO) is proposed in this paper. Firstly, a med-point-example learning strategy and a random learning strategy are proposed to replace the imitation component and social influence component of SL-PSO to enhance the exploitation and exploration, respectively. Secondly, the two learning strategies are combined cleverly into an updating equation to balance exploration and exploitation. Finally, a worst-best example learning strategy is merged skillfully to construct TLS-PSO with hybrid learning mechanism and further enhance the search ability. The experimental results on the complex functions from CEC2013 and CEC2017 test sets indicate that TLS-PSO has better performance compared with state-of-the-art PSO variants and other algorithms. For example, TLS-PSO has an advantage over SL-PSO on 50 of the 56 functions from CEC2013, its running time is less than SL-PSO's and it has higher search efficiency. Simulation results on the 10 engineering problems also show that TLS-PSO outperforms 7 excellent algorithms, such as IUDE and iLSHADE,. It is expected to solve practical problems better. (C) 2022 Elsevier Inc. All rights reserved.

关键词： optimization algorithm swarm intelligence optimization algorithm Particle swarm optimization Learning strategy Global optimization problems

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Two-stage feature selection for classification of gene expression data based on an improved Salp swarm algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第12期19卷 13747-13781页

作者： Qin, Xiwen Zhang, Shuang Yin, Dongmei Chen, Dongxue Dong, Xiaogang Changchun Univ Technol Sch Math & Stat Changchun 130012 Peoples R China

Microarray technology has developed rapidly in recent years, producing a large number of ultra-high dimensional gene expression data. However, due to the huge sample size and dimension proportion of gene expression data, it is very challenging work to screen important genes from gene expression data. For small samples of high-dimensional biomedical data, this paper proposes a two-stage feature selection framework combining Wrapper, embedding and filtering to avoid the curse of dimensionality. The proposed framework uses weighted gene co-expression network (WGCNA), random forest and minimal redundancy maximal relevance (mRMR) for first stage feature selection. In the second stage, a new gene selection method based on the improved binary Salp swarm algorithm is proposed, which combines machine learning methods to adaptively select feature subsets suitable for classification algorithms. Finally, the classification accuracy is evaluated using six methods: lightGBM, RF, SVM, XGBoost, MLP and KNN. To verify the performance of the framework and the effectiveness of the proposed algorithm, the number of genes selected and the classification accuracy was compared with the other five intelligent optimization algorithms. The results show that the proposed framework achieves an accuracy equal to or higher than other advanced intelligent algorithms on 10 datasets, and achieves an accuracy of over 97.6% on all 10 datasets. This shows that the method proposed in this paper can solve the feature selection problem related to high-dimensional data, and the proposed framework has no data set limitation, and it can be applied to other fields involving feature selection.

关键词： high-dimensional data feature selection swarm intelligence optimization algorithm gene expression data cancer classification

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VLEO Satellite Constellation Design for Regional Aviation and Marine Coverage

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 1188-1201页

作者： Chen, Guoquan Wu, Shaohua Deng, Yajing Jiao, Jian Zhang, Qinyu Harbin Inst Technol Shenzhen Dept Elect & Informat Engn Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

Recently, the Space-Air-Ground-Sea Integrated Network (SAGSIN) attracts great attention due to its ability to provide high-speed communication services to aviation users (AUs) and marine users (MUs), with Low Earth Orbit (LEO) satellites play an essential role. However, available LEO space is nearly saturated and full of massive space junks, which, combined with the ultra-low latency requirements for future 6G, presents a significant challenge. To address this issue, we propose designing a Very Low Earth Orbit (VLEO)-based satellite network that efficiently serves AUs and MUs. We first create heat maps based on the actual collected data and generate a benchmark observation point model with grid point method. Then we propose an implicit multi-objective continuous multivariate optimization problem to achieve the maximum coverage with minimum VLEO satellites. To solve this problem, we build a constellation simulation system, using the idea of decomposition and polymerization combined with the elite strategic genetic algorithm (ESGA) of swarm intelligence optimization algorithm. Many simulation results are obtained, including the indication that the optimal VLEO constellation has the deployment features of large altitude and low inclination, and has better coverage performance for longitudinal distributed business. The constellation design process is also highly migratory for other non-terrestrial networks.

关键词： Satellites Low earth orbit satellites Satellite constellations Business Aircraft optimization Data models ESGA SAGSIN satellite constellation swarm intelligence optimization algorithm VLEO

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RFID Network Planning of Smart Factory Based on swarm Intelligent optimization algorithm: A Review

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IEEE ACCESS 2024年 12卷 64980-64996页

作者： Yejiao, Wang Kamaludin, Hazalila Mohammed Alduais, Nayef Abdulwahab Zuraidin Mohd Safar, Noor Zhongchao, Hao Univ Tun Hussein Onn Malaysia UTHM Fac Comp Sci & Informat Technol FSKTM Batu Pahat 86400 Johor Malaysia Changzhou Vocat Inst Ind Technol Changzhou 213164 Jiangsu Peoples R China

With the development of intelligent manufacturing in China, Radio Frequency Identification (RFID), a key technology for smart factories, has received widespread attention. As RFID applications expand, so does the size of their networks. It makes it more difficult to ensure RFID signal coverage, leads to communication problems, and increases equipment energy consumption and costs, thereby posing challenges in the realm of RFID network planning (RNP). The RNP problem needs to consider multiple objectives and constraints such as coverage, conflicts, economic benefit, and load balance which have been proven to be optimized by swarm intelligent optimization algorithms. Therefore, this study reviews smart factories, RFID technology, swarm intelligence optimization algorithms and RFID network planning. The improvement direction of swarm intelligence optimization algorithms and factors affecting RFID network performance are also explored. In addition, it reviews and analyzes the applications of swarm intelligence algorithms to RNP problems and discusses the innovations and drawbacks of these approaches. Finally, some research limitations and directions are identified.

关键词： Radiofrequency identification Smart manufacturing Manufacturing optimization Planning Production facilities Reviews Particle swarm optimization Smart factory RFID network planning swarm intelligence optimization algorithm

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Development of near-infrared spectroscopy calibration model and monitoring software: For monitoring hexamethylenetetramine concentration in hexamethylenetetramine-acetic acid solution

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INFRARED PHYSICS & TECHNOLOGY 2024年 139卷

作者： Qian, Shichuan Wang, Zhi Chao, Hui Sheng, Xin Zhao, Xinping Lu, Zhiyan Xing, Ruoting Xu, Yinguang Jin, Shaohua Li, Lijie Chen, Kun Beijing Inst Technol Sch Mat Sci & Engn Beijing 100081 Peoples R China Gansu Yin Guang Chem Ind Grp Co Ltd Baiyin Peoples R China

In recent years, near -infrared spectroscopy has been increasingly used in chemical engineering processes. However, accurately and in real-time monitoring concentrations remains highly challenging. This study presents a method for accurately monitoring the hexamethylenetetramine concentration in hexamethylenetetramineacetic acid solution by integrating near -infrared spectroscopy with software techniques. Compared to the methods of zero mean standardization (Z -score), standard normal variate (SNV), first derivative (D1), second derivative (D2), and vector normalization (VN), the Savitzky-Golay first derivative (SGFD) method effectively addresses issues related to baseline drift, noise, and other interferences. This results in clearer identification of characteristic absorption peaks of different components and a significant improvement in the accuracy of the analysis. The study combined the uninformative variable elimination (UVE), differential evolution (DE), and moth flame optimization (MFO) algorithms to optimize a feature wavenumber selection method, which reduced the number of variables by 78.74 %, and the values of root mean square error of calibration set (RMSEC) and root mean square error of prediction (RMSEP) by 18.86 % and 30.38 %, respectively. Additionally, the analytical method was integrated with real-time monitoring software, enabling the monitoring of hexamethylenetetramine concentration. This integration supports the improvement of the quality and safety of the manufacturing process. The results demonstrate the potential application of near -infrared spectroscopy in real-time monitoring and automated control of chemical processes in the chemical industry.

关键词： Near infrared spectroscopy swarm intelligence optimization algorithm Wavenumber variable selection Real time monitoring

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