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Cardiac arrest prediction in smokers using enhanced artificial bee colony algorithm with stacked autoencoder model

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2023年第10期26卷 1220-1235页

作者： Banu, Umera Vanjerkhede, Kalpana Khaja Bandanawaz Coll Engn Dept Biomed Engn Kalaburagi India Poojya Doddappa Appa PDA Coll Engn Dept Elect & Instrumentat Engn Kalaburagi India

In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in smokers based on the deep learning techniques. First, the input data is collected from MITU Skillogies dataset, which consists of 1562 smoker and non-smoker instances with 19 HRV input attributes/features. After data collection, the enhanced artificial bee colony algorithm (EABC) is developed for feature selection. The EABC algorithm includes two new multi-objective functions for decreasing the number of attributes in the MITU Skillogies dataset. This mechanism superiorly reduces the burden of computational complexity and improves classification accuracy. Further, the selected attributes are given to the stacked autoencoder classifier for non-cardiac arrest and cardiac arrest classification in smokers for early diagnosis. The extensive experiment showed that the EABC with stacked autoencoder model obtained 96.26% of classification accuracy, which is better related to the traditional machine learning models.

关键词： artificial bee colony algorithm cardiac arrest prediction heart rate variability machine learning stacked autoencoder model

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A Modified artificial bee colony algorithm Using Accept-Reject Method: Theory and Application in Virtual Power Plant Planning

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IETE JOURNAL OF RESEARCH 2023年第8期69卷 5364-5379页

作者： Farahbakhsh, Hossein Pourfar, Iman Ara, Afshin Lashkar Islamic Azad Univ Dept Elect Engn Dezful Branch Dezful Iran Jundi Shapur Univ Technol Dept Elect Engn Dezful Iran

In this paper, the modified artificial bee colony optimization algorithm, in which the accept-reject method is used as a part of the solution process in order to limit the random search space, is proposed for solving the optimization problems. The accept-reject method can find points of desirable quality and high accuracy with the help of probabilistic functions and limited the random search space, which increases the speed of the algorithm for the next steps and also reduces the number of iterations of the artificial bee algorithm. Then, the next phases of the bee algorithm will be executed in the interval determined by the accept-reject method. One of the most important issues in intelligent algorithms, especially the bee algorithm, is that in problems with several local optimal points, sometimes the algorithm gets stuck in the trap of local optimal points and does not reach the global optimal points. By using the motion step determination parameter in the employed bee phase, the algorithm can be guided to reach the global optimal points while correctly adjusting the movement of the bees for searching in random food sources, which will help improving the problem-solving process. This algorithm is tested by the basic benchmark functions as well as the benchmark functions of CEC 2005 and CEC2017 and CEC2019 and an engineering problem related to the planning of a virtual power plant. The results show that the proposed algorithm is superior in comparison to other algorithms.

关键词： Accept-reject method artificial bee colony algorithm motion step optimization random search virtual power plant

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A Reinforcement Learning Driven artificial bee colony algorithm for Distributed Heterogeneous No-Wait Flowshop Scheduling Problem With Sequence-Dependent Setup Times

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年第4期20卷 2305-2320页

作者： Zhao, Fuqing Wang, Zhenyu Wang, Ling Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The distributed heterogeneous factory system is a typical scenario in the manufacturing industry. A distributed heterogeneous no-wait flowshop scheduling problem with sequence-dependent setup times (DHNWFSP-SDST) is studied in this paper. The differences in factory configuration and transportation time are considered in DHNWFSP-SDST. A mixed-integer linear programming (MILP) model is constructed and an artificial bee colony algorithm (ABC) with Q-learning (QABC) is proposed to address the DHNWFSP-SDST. Heuristic methods named NEH_H and DHHS are designed to construct potential initial candidates for the population. The neighborhood structures based on the job blocks are introduced in QABC to explore the solution space during the evolution processes. The Q-learning mechanism is employed to select neighborhood structures via empirical knowledge in the operation processes. The speed-up methods to accelerate the evaluation of the obtained neighborhood are designed to reduce the computation time of the QABC. The experimental results show that the QABC is a potential algorithm to address the DHNWFSP-SDST.

关键词： Heterogeneous flowshop scheduling sequence-dependent setup times artificial bee colony algorithm Q-learning, neighborhood structure

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Research of the utilization efficiency of non-flood season flood resources by artificial bee colony algorithm based on multi-strategy hybrid search

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WATER SUPPLY 2023年第5期23卷 1987-2000页

作者： Li, Jie Yang, Zhou Liu, Zhao Liu, Hong-zhi Tian, Yang-jun Changan Univ Sch Water & Environm Xian 710054 Shaanxi Peoples R China Changan Univ Key Lab Subsurface Hydrol & Ecol Effects Arid Reg Minist Educ 126 Yanta Rd Xian 710054 Peoples R China Sichuan Huaneng Baoxing River Power Co Ltd Yaan 625000 Peoples R China Hanjiang To Weihe River Valley Water Divers Projec Xian 710010 Shaanxi Peoples R China

This study aims to investigate the integrated optimization of long-term non-flood season flood dispatching and power generation dispatching and improve the utilization efficiency of non-flood season flood resources. In this study, an artificial bee colony (ABC) algorithm based on a multi-strategy mixed search is supplemented with variable time period characteristics to construct an optimal reservoir operation model with a maximum generating capacity under multiple constraints in a variable time period. The multi-year non-flood season flood operation of the Wanan Reservoir is considered as an example. After optimization, the average annual power generation in the non -flood season was increased by 14.23 million kWh. The average utilization rate of water resources was increased by 3% over the studied period, and the utilization of flood resources was improved. The ABC optimization algorithm based on a multi-strategy hybrid search could alleviate the contradiction between the selection of the time interval step and the calculation accuracy and convergence speed, and it effectively improved the optimization efficiency and development ability of the standard ABC optimization algorithm. These results can provide suggestions for the improvement of the comprehensive benefits of reservoirs with non-flood floods as an important water resource.

关键词： artificial bee colony algorithm multi-strategy mixed search optimal dispatch of flood control power generation variable-period step size Wanan reservoir

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Optimizing a stochastic disassembly line balancing problem with task failure via a hybrid variable neighborhood descent-artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第7期61卷 2307-2321页

作者： Guo, Hongfei Zhang, Linsheng Ren, Yaping Li, Yun Zhou, Zhongwei Wu, Jianzhao Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai 509070 Peoples R China Jinan Univ Inst Phys Internet Zhuhai 509070 Peoples R China Jinan Univ Sch Management Guangzhou Peoples R China i4AI Ltd London England Tianjin Univ Sci & Technol Sch Elect Informat & Automat Tianjin Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China Natl Univ Singapore Dept Mech Engn Singapore Singapore

A disassembly line is an effective disassembly system to recover end-of-life products. In real life, as end-of-life products are subject to varying degrees of wear and tear, task failure may occur in the disassembly process. In this paper, the task failure risks are considered, and an expected profit-based stochastic disassembly line balancing problem is studied. First, a mathematical model is presented to maximise the expected recovering profit with task failures. Then, a hybrid metaheuristic approach is developed to efficiently solve the proposed model, which is integrated with a variable neighbourhood descent method and an artificial bee colony algorithm. Finally, the effectiveness and robustness of the proposed algorithm are verified by three cases, and experiment results show that the solution performance of the proposed approach is superior to the other three existing methods.

关键词： Disassembly line balancing problem task failure expected profit variable neighborhood descent method artificial bee colony algorithm

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artificial bee colony algorithm with Dynamic Dimension Search

Artificial bee Colony Algorithm with Dynamic Dimension Searc...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Ming Zhao Meiyu Wang School of Computer Science and Engineering Shenyang Jianzhu University Liaoning China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Aiming at the shortcomings of the standard artificial bee colony (ABC) algorithm due to weak exploitation capability and slow convergence speed caused by one-dimension search, an ABC algorithm with dynamic dimension search (DDSABC) is proposed. Firstly, a dynamic selection mechanism for dimension search strategy is designed, where one-dimension search is used in the early stage of search to exploit globally diverse individuals, and multi-dimension search is used in the late stage to explore convergent individuals; an elite moderately guided dimension selection mechanism is designed for multi-dimension search, where half of the dimensions are selected under the guidance of the elite mean to accelerate the evolution of potential dimensions, and the other half are selected by a randomized way to maintain a moderate randomness; in addition, suitable search equations are selected for the different characteristics of the two dimension search strategies to ensure the balance between exploration and exploitation capabilities. Finally, on 30 complex problems from the CEC2014 test set, ablation experiments and comparative experiments with the standard ABC and its 5 variants are analyzed, and the results show that the proposed algorithm in this paper performs better in terms of solution accuracy, stability and convergence speed.

关键词： Cloud computing Accuracy Heuristic algorithms Software algorithms Search problems artificial bee colony algorithm Stability analysis Standards Convergence Software engineering

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A Discrete Multi‑Objective artificial bee colony algorithm for a Real‑World Electronic Device Testing Machine Allocation Problem

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Chinese Journal of Mechanical Engineering 2022年第6期35卷 136-150页

作者： Jin Xie Xinyu Li Liang Gao State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China

With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human *** have higher quality requirements for electronic *** device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the *** the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be *** study investigates the electronic device testing machine allocation problem(EDTMAP),aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine ***,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing ***,we developed a discrete multi-objective artificial bee colony(DMOABC)algorithm to solve EDTMAP.A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm,*** experiments were conducted to evaluate the performance of the proposed *** experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II(NSGA-II)and strength Pareto evolutionary algorithm 2(SPEA2).Finally,the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency *** results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.

关键词： Electronic device Machine allocation Multi-objective optimization artificial bee colony algorithm

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Placement of Edge Servers in Mobile Cloud Computing using artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第2期14卷 621-637页

作者： Zhou, Bing Lu, Bei Zhang, Zhigang Jiaozuo Univ Coll Artificial Intelligence Jiaozuo 454000 Henan Peoples R China

smart mobile devices for entertainment, education, and social networking has grown recently. Even though mobile applications are getting more sophisticated and resource-intensive, the computing power of mobile devices is still constrained. Mobile phone applications can perform better by shifting parts of their functions to cloud servers. However, because the cloud is frequently positioned far from mobile phone users, there may be a significant and unpredictable delay in the data transfer between users and the cloud. It is crucial for mobile applications since customers value rapid responses greatly. Users of mobile phones can get close-up access to information technology and cloud computing services thanks to mobile edge computing. In this article, the main goal is to use an artificial bee colony meta-innovative algorithm to solve the problem of placing edge servers in mobile edge computing. Moreover, load balancing between servers is one of the challenges discussed in this article. To deal with this issue, determination the locations of the servers using considering the distribution of workload between servers as a cost function in the artificial bee colony algorithm is a focused issue in this study. The results of the proposed method are compared with the load balancing criteria. The results of K -means compared to the clustering method show the superiority of the proposed method with regard to the loading criteria compared to this clustering method.

关键词： artificial bee colony algorithm k-means server placement mobile cloud computing

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Indoor evacuation model based on visual-guidance artificial bee colony algorithm

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Building Simulation 2022年第4期15卷 645-658页

作者： Xinlu Zong Aiping Liu Chunzhi Wang Zhiwei Ye Jiayuan Du College of Computer Science and Technology Hubei University of TechnologyNo.28Nanli RoadHong-shan DistrictWuchangWuhanHubei ProvinceChina

Research on evacuation simulation and modeling is an important and urgent issue for emergency *** paper presents an evacuation model based on cellular automata and social force to simulate the evacuation *** force of target position,repulsive forces of individuals and obstacles,as well as congestion are considered in order to simulate the interaction among evacuees and the changing environment.A visual-guidance-based artificial bee colony algorithm is proposed to optimize the evacuation *** evacuee moves toward exits with the guidance of leading bee in his/her visual *** leading bee is selected according to comprehensive factors including distance from the current individual,the number of obstacles and congestion,which avoids the randomness of roulette mechanism used by basic artificial bee colony *** experimental results indicate that the proposed model and algorithm can achieve effective performances for indoor evacuation problems with a large number of evacuees and obstacles,which accords with the actual evacuation situation.

关键词： evacuation model artificial bee colony algorithm visual guidance cellular automata social force

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Optimal Design of Water Distribution Network Using Improved artificial bee colony algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2023年第5期47卷 3123-3136页

作者： Najarzadegan, Mohammad Reza Moeini, Ramtin Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan *** Iran

Urban water distribution networks are the most essential and costly network in each city. Major part of the urban water distribution network costs are related to the purchase of water distribution network accessories. Therefore, the cost of the water distribution network can be reduced by reducing this part. For this purpose, the design of water distribution network should be defined as an optimization model and solved it using an efficient method. Nowadays, meta-heuristics algorithms are the most efficient methods for solving optimization models. In this research, three benchmark problems, mean two-loop, New York, and Go Yang networks, are modeled in EPANET software and the optimization model is solved using an improved version of the artificial bee colony algorithm that is called in MATLAB software. To evaluate the efficiency of the proposed algorithm, the results are presented and compared with the standard version of the artificial bee colony algorithm and other available results. The results show that by using an improved artificial bee colony algorithm for two-loop network, New York, and Go Yang network, the objective function values (construction costs) and computational costs are 419,000 unit, 38.13 M$, and 175.78 MWon and 2500, 3600, and 2600, respectively. In addition, comparison of the results shows that the construction and computational costs are reduced compared with the result of the standard version.

关键词： artificial bee colony algorithm Water distribution network EPANET Optimal design Pipe diameter

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