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Locating abrupt disaster emergency logistics centres using improved artificial bee colony (IABC) algorithm

Science Progress 2021年第2期104卷 368504211016205-368504211016205页

作者： Sun, Qiang Liu, Shupei Shandong University of Technology Zibo China

Emergency management is conceptualized as a complex, multi-objective optimization problem related to facility location. However, little research has been performed on the horizontal transportation of emergency logistics centres. This study makes contributions to the multi-objective locating abrupt disaster emergency logistics centres model with the smallest total cost and the largest customer satisfaction. The IABC algorithm is proposed in this paper to solve the multi-objective emergency logistics centres locating problem. IABC algorithm can effectively calculate the optimal location of abrupt disaster emergency logistics centres and the demand for relief materials, and it can solve the rescue time satisfaction for different rescue sites. (1) IABC has better global search capabilities to avoid premature convergence and provide a faster convergence speed, and it has optimal solution accuracy, solution diversity and robustness. (2) From the three optimal objective function values obtained, the optimal objective function values obtained by IABC algorithm are obviously better than ABC and GABC algorithms. (3) From the convergence curves of three objective functions the global search ability and the stability of IABC algorithm are better than those of ABC and GABC algorithm. The improved ABC algorithm has proven to be effective and feasible. However, emergency relief logistics systems are very complex and involve many factors, the proposed model needs to be refined further in the future. © The Author(s) 2021.

关键词： Abrupt disaster emergency logistics improved artificial bee colony algorithm logistics centre location multi-objective optimization time satisfaction

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A hybrid algorithm for classifying rock joints based on improved artificial bee colony and fuzzy C-means clustering algorithm

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GEOMECHANICS AND ENGINEERING 2022年第4期31卷 353-364页

作者： Ji, Duofa Lei, Weidong Chen, Wenqin Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China Harbin Inst Technol Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disaste Harbin 150090 Peoples R China Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

This study presents a hybrid algorithm for classifying the rock joints, where the improved artificial bee colony (IABC) and the fuzzy C-means (FCM) clustering algorithms are incorporated to take advantage of the artificial bee colony (ABC) algorithm by tuning the FCM clustering algorithm to obtain the more reasonable and stable result. A coefficient is proposed to reduce the amount of blind random searches and speed up convergence, thus achieving the goals of optimizing and improving the ABC algorithm. The results from the IABC algorithm are used as initial parameters in FCM to avoid falling to the local optimum in the local search, thus obtaining stable classifying results. Two validity indices are adopted to verify the rationality and practicability of the IABC-FCM algorithm in classifying the rock joints, and the optimal amount of joint sets is obtained based on the two validity indices. Two illustrative examples, i.e., the simulated rock joints data and the field-survey rock joints data, are used in the verification to check the feasibility and practicability in rock engineering for the proposed algorithm. The results show that the IABC-FCM algorithm could be applicable in classifying the rock joint sets.

关键词： fuzzy C-means clustering algorithm improved artificial bee colony algorithm joint set rock mass

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An improved bearing fault detection strategy based on artificial bee colony algorithm

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CAAI Transactions on Intelligence Technology 2022年第4期7卷 570-581页

作者： Haiquan Wang Wenxuan Yue Shengjun Wen Xiaobin Xu Hans-Dietrich Haasis Menghao Su Ping liu Shanshan Zhang Panpan Du Zhongyuan Petersburg Aviation College Zhongyuan University of TechnologyZhengzhouChina Faculty of Electrical and Engineering Zhongyuan University of TechnologyZhengzhouChina School of Automation Hangzhou Dianzi UniversityHangzhouChina Maritime Business and Logistics University of BremenBremenGermany

The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very *** this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and *** XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of ***,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is *** experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction *** with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy.

关键词： fault diagnosis feature extraction improved artificial bee colony algorithm improved one-dimensional ternary pattern method shapelet transformation

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Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Li, Ping Zhang, Yanru Gu, Jiming Duan, Shiwei Anhui Univ Technol Sch Mech Engn Maanshan 243032 Peoples R China

There are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the basic artificial bee colony algorithm, such as easily falling into local optima and slow convergence speed, this article introduces a Gaussian mutation operator into the basic artificial bee colony algorithm to optimize the initial honey source position and designs an MLP neural network model based on the improved artificial bee colony algorithm (IABC-MLP). Compared with traditional strength prediction models, the ABC-MLP model can better capture the nonlinear relationship of the compressive strength of concrete and achieve higher prediction accuracy when considering the compound effect of multiple factors. The IABC-MLP model built in this study is compared with the ABC-MLP and particle swarm optimization (PSO) coupling algorithms. The research shows that IABC can significantly improve the training and prediction accuracy of MLP. Compared with the ABC-MLP and PSO-MLP coupling models, the training accuracy of the IABC-MLP model is increased by 1.6% and 4.5%, respectively. This model is also compared with common individual learning algorithms such as MLP, decision tree (DT), support vector machine regression (SVR), and random forest algorithms (RF). Based on the comparison of prediction results, the proposed method shows excellent performance in all indicators and demonstrates the superiority of heuristic algorithms in predicting the compressive strength of concrete.

关键词： Concrete Compressive strength prediction improved artificial bee colony algorithm Multilayer perceptron

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An improved artificial bee colony-random forest(IABC-RF)model for predicting the tunnel deformation due to an adjacent foundation pit excavation

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Underground Space 2022年第4期7卷 514-527页

作者： Tugen Feng Chaoran Wang Jian Zhang Bin Wang Yin-Fu Jin Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering Hohai UniversityNanjing 210024China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong KongChina

An improved artificial bee colony-random forest(IABC-RF)model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit.A new search strategy of the artificial bee colony(ABC)algorithm is herein developed and incorporated,with the results showing that a much higher computational efficiency can be achieved with the new model,while high computational accuracy can also be *** improved ABC algorithm is thereafter utilised and combined with the random forest(RF)model,where four important hyper-parameters are optimized,for a tunnel deformation *** are thoroughly compared with those of other prediction methods based on machine learning(ML),as well as the monitored data on the *** the comparisons,the validity and effectiveness of the proposed model are fully demonstrated,and a more promising perspective can be seen of the method for its potential wide applications in geotechnical engineering.

关键词： Tunnel deformation prediction improved artificial bee colony algorithm Random forest Hyper-parametric optimization search

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Prediction of water temperature in prawn cultures based on a mechanism model optimized by an improved artificial bee colony

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 140卷 397-408页

作者： Xu, Longqin Liu, Shuangyin Li, Daoliang Zhongkai Univ Agr & Engn Sch Informat Sci & Technol Guangzhou 510225 Guangdong Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 10083 Peoples R China China Agr Univ Beijing ERC Internet Things Agr Beijing 10083 Peoples R China

To reduce aquaculture risk and optimize water quality management in prawn culture ponds, this paper uses mechanistic and statistical analytic methods to propose a hybrid water temperature forecasting model based on the water temperature mechanism model (WTMM) with optimal parameters selected by an improved artificial bee colony (IABC) algorithm. Because of existing problems with using an artificial bee colony algorithm in modeling, an improved ABC with a dynamically adjusted inertia weight based on the fitness function value was implemented to improve local and global search abilities. Then, IABC was employed to adaptively search for the optimal combinatorial parameters needed in the WTMM model, which overcomes the blindness of and limits to parameter selection for the traditional WTMM model. We adopted an IABC-WTMM algorithm to construct a non-linear mechanical prediction model. The IABC-WTMM was tested and compared to other algorithms by applying it to the prediction of water temperature in prawn culture ponds. Experimental results show that the proposed IABC-WTMM could increase prediction accuracy and execute generalization performance better than the original water temperature mechanism model (O-WTMM) and back-propagation neural network (BP-NN), but was inferior to the standard LSSVR model. Overall, it is a suitable and effective method for predicting water temperature in intensive aquacultures. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Water temperature prediction improved artificial bee colony algorithm Mechanism model Parameter identification

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Image Threshold Segmentation Based on An improved bee colony algorithm 8

Image Threshold Segmentation Based on An Improved Bee Colony...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Huo Fengcai Wang Di Ren Weijian Heilongjiang Prov Key Lab Networking & Intelligen Daqing Peoples R China Northeast Petr Univ Dept Elect Informat Engn Daqing Peoples R China

ISBN: (纸本)9781538682463

Image segmentation means that the image is divided into specific and unique regions. There are many existing image segmentation methods, and the threshold-based segmentation method is widely applied because of its easy implementation, simplicity and high efficiency. In this paper, artificial bee colony algorithm is applied to image threshold segmentation. Kapur entropy is used as a fitness function, the artificial bee colony algorithm is improved through the adaptive scaling factor. The search area is enlarged through the large-scale factor, the neighborhood search scope is reduced through the small-scale factor and the search efficiency is enhanced. Finally, by comparing the PSNR values of the image, the algorithm has a good segmentation effect and good convergence performance.

关键词： image threshold segmentation artificial bee colony algorithm improved artificial bee colony algorithm Kapur entropy

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Traffic accident prediction based on an artificial bee colony algorithm and a self-adaptive fuzzy wavelet neural network

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2023年第3期17卷 254-265页

作者： Li, Zhicheng Anhui Commun Vocat & Tech Coll Dept Urban Rail Transit & Informat Engn Hefei 230051 Peoples R China

This study combines an artificial bee colony algorithm with a fuzzy wavelet neural network to establish a new traffic accident prediction model. It introduces the self-adaptive mutation operation of differential evolution algorithms and the selection operator, crossover operator, and mutation operator of a genetic algorithm into the traditional artificial bee colony algorithm. These actions aim to improve the shortcomings of the slow convergence rate and the weak local search ability of the artificial bee colony algorithm in the later period. This study also uses the improved artificial bee colony algorithm to optimise the weights and thresholds of the fuzzy wavelet neural network and to make up for the randomness of the weights and threshold selection of the wavelet neural network. It combines the good nonlinear fitting ability of the self-adaptive fuzzy wavelet neural network with the strong robustness of the artificial bee colony algorithm to build a model for predicting traffic death tolls. Computer simulation shows that the prediction accuracy of the proposed method is higher.

关键词： traffic accidents prediction improved artificial bee colony algorithm fuzzy wavelet neural network differential evolution algorithm

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Research on Bi-Level Optimized Operation Strategy of Microgrid Cluster Based on IABC algorithm

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IEEE ACCESS 2021年 9卷 15520-15529页

作者： Zhang, Zhiyu Wang, Zhijie Wang, Hong Zhang, Hongwei Yang, Wanhao Cao, Rongbin Shanghai Dianji Univ Coll Elect Engn Shanghai 201306 Peoples R China Tongji Univ Coll Econ & Management Shanghai 200092 Peoples R China Univ Shanghai Sci & Technol Coll Elect Engn Shanghai 200093 Peoples R China

This paper considers the economy and reliability of the microgrid cluster system, and proposes a bi-level optimized operation strategy for the microgrid cluster, which aims to improve the economic benefits of the microgrid cluster and reduce the operating risk of microgrid. The upper layer takes the microgrid cluster as the research object, with the goal of minimizing the total operating cost of the system, and establishes the optimal model of the microgrid cluster. The lower layer takes the sub-microgrid as the research object, and establishes the microgrid optimization model with the goal of optimal operating cost and operating risk index. The method of combining mixed integer linear programming and IABC algorithm is used to solve the multi-objective bi-level optimization model, and to provide decision makers with two microgrid dispatching schemes with different focuses. Finally, an example is used to verify the effectiveness of the bi-level optimization strategy and improved algorithm proposed in this paper, as well as the practicability of the two dispatching schemes.

关键词： Microgrids Optimization Power generation Mathematical model Clustering algorithms Reliability State of charge Microgrid microgrid cluster multi-objective improved artificial bee colony algorithm bi-level optimization model

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Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

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The Journal of China Universities of Posts and Telecommunications 2018年第3期25卷 33-44页

作者： Tian Zhongda Li Shujiang Wang Yanhong Wang Xiangdong School of Information Science and Engineering Shenyang University of Technology

In order to overcome the poor generalization ability and low accuracy of traditional network traffic prediction methods, a prediction method based on improved artificial bee colony （ABC） algorithm optimized error minimized extreme learning machine （EM-ELM） is proposed. EM-ELM has good generalization ability. But many useless neurons in EM-ELM have little influences on the final network output, and reduce the efficiency of the algorithm. Based on the EM-ELM, an improved ABC algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons. Network complexity is reduced. The efficiency of the algorithm is improved. The stability and convergence property of the proposed prediction method are proved. The proposed prediction method is used in the prediction of network traffic. In the simulation, the actual collected network traffic is used as the research object. Compared with other prediction methods, the simulation results show that the proposed prediction method reduces the training time of the prediction model, decreases the number of hidden layer nodes. The proposed prediction method has higher prediction accuracy and reliable performance. At the same time, the performance indicators are improved.

关键词： error minimized extreme learning machine improved artificial bee colony algorithm network traffic prediction

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