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Numerical Improvement for the Mechanical Performance of Bikes Based on an Intelligent PSO-abc algorithm and WSN Technology

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IEEE ACCESS 2018年 6卷 32890-32898页

作者： Han, Zidong Li, Yufeng Liang, Junyu Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Shenzhen Hopnetworks Ltd Shenzhen 518055 Peoples R China

This paper proposed a novel hybrid optimization algorithm, particle swarm optimization-artificial bee colony (PSO-abc), based on the PSO and abc algorithms. The abc algorithm can offset defects in the PSO algorithm that easily fall into a local optimization;combining the algorithms can improve the optimization ability of the PSO algorithm to a certain extent. Therefore, this paper applied the PSO-abc hybrid algorithm and the finite-element method to systematically optimize the mechanical performance of the disc rotor of a bike and verified the numerical computation model via the wireless sensor network technology. The experimental test was completed with wireless sensor network technologies. To verify the optimized effects of the proposed PSO-abc hybrid algorithm after parameter selection, the algorithm was compared with the traditional PSO and abc models. The PSO, abc, and PSO-abc models adopted the same population to conduct a multi-objective optimization for vibration accelerations of the disc rotor. Comparing the results from these models proved that the proposed PSO-abc method is superior for the optimization of vibration characteristics of the disc rotors.

关键词： PSO algorithm abc algorithm PSO-abc hybrid algorithm WSN technology multi-objective optimization

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Prediction of Ecological Pressure on Resource-Based Cities Based on an RBF Neural Network Optimized by an Improved abc algorithm

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IEEE ACCESS 2019年 7卷 47423-47436页

作者： Jiang, Song Lu, Caiwu Zhang, Sai Lu, Xiang Tsai, Sang-Bing Wang, Cheng-Kuang Gao, Yuan Shi, Yufei Lee, Chien-Hung Xian Univ Architecture & Technol Sch Management Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Resources Engn Xian 710055 Shaanxi Peoples R China China Univ Min & Technol Sch Mines Xuzhou 221116 Jiangsu Peoples R China Univ Elect Sci & Technol China Zhongshan Inst Guangzhou 528400 Guangdong Peoples R China Wuyi Univ Sch Business Wuyishan 354300 Peoples R China Nankai Univ Business Sch Tianjin 300071 Peoples R China Nankai Univ Coll Tourism & Serv Management Tianjin 300071 Peoples R China

Resource-based cities are those where resource-based industries comprise a large proportion of all industries. Sustainable development implies that cities make full use of their own resources to support current development initiatives and take sustainability into account both during and after resource consumption. To promote investment in the sustainable development of resource-based cities and to a provide a decision system for these cities, this paper uses an ecological footprint model to evaluate and analyze the per capita ecological footprint, per capita ecological carrying capacity and per capita ecological deficit of a representative resource-based city, Yulin. The data are collected from 2001 to 2015. In addition, due to the complexity of the influencing factors for ecological carrying capacity and the variety of situations that are difficult to accurately predict, this paper proposes a new urban ecological carrying capacity prediction model, which consists of a radial basis function (RBF) neural network that is optimized by an improved artificial bee colony algorithm. The prediction results show that energy consumption is the major factor affecting the urban ecosystem;moreover, the model precision of the training results and the simulation accuracy of the test results achieved by the RBF neural network model are 97.91% and 94.16%, respectively, and in 2020, the per capita ecological footprint, biocapacity, and ecological deficit of Yulin are predicted to reach 4.892 hm(2), 3.317 hm(2), and 1.575 hm(2), respectively. Accordingly, effective proactive measures should be taken in advance to maintain or reduce the ecological pressure on this resource-dependent city. This paper strives to provide a scientific basis for local government decision-making to realize the healthy, stable, and rapid sustainable development of resource-based cities.

关键词： Resource-based city ecological pressure prediction RBF neural network abc algorithm ecological footprint theory

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Hepatitis C Virus Detection Model by Using Random Forest, Logistic-Regression and abc algorithm

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IEEE ACCESS 2022年 10卷 91045-91058页

作者： Li, Tzuu-Hseng S. Chiu, Huan-Jung Kuo, Ping-Huan Natl Cheng Kung Univ Dept Elect Engn aiRobots Lab Tainan 70101 Taiwan Natl Chung Cheng Univ Dept Mech Engn Chiayi 62102 Taiwan

This study proposes an automatic classifier for detecting the multiclass probabilities of hepatitis C virus (HCV) incidence based on patients' blood attributes. The purpose of this study is to establish an artificial intelligence-based model that can identify HCV patients and detect the disease in early stage for future treatments. This model can be applied by using clinical data and keeps the performance from imbalanced datasets. The innovation in this article lies in considering the "unbalanced data" existing in medical record-based clinical data. Synthetic minority oversampling technique (SMOTE) algorithm was further employed to derive corresponding solutions. This objective was achieved using a cascade two-stage method combining the random forest (RF) and logistic regression (LR) algorithms. Two models were trained by applying the RF (Model 1) and LR (Model 2) to raw and preprocessed data, respectively. The artificial bee colony (abc) algorithm was then used to determine the optimal threshold value required for filtering and separation, that is, the optimal combination of both models. The two-stage mixing algorithm combines algorithms of different search dimensions, thus integrating the strengths of those algorithms. The critical threshold value for separating Model 1 and Model 2 was obtained through an optimized search using the abc algorithm. After conducting 10-fold Monte Carlo cross-validation experiments 50 times (for mean values), data from the recent pandemic were used to verify the proposed method. To evaluate the quantitative results, indicators, such as prediction accuracy, precision, recall, F1-score, and Matthews correlation coefficient, were compared with those of the latest algorithms used in relevant fields. The results indicate that the proposed model, named Cascade RF-LR (with SMOTE), can be used to detect the multiclass probabilities of HCV incidence using the abc algorithm, thereby improving the effectiveness of relevant treatments.

关键词： Liver diseases Classification tree analysis Random forests Data models Classification algorithms Artificial bee colony algorithm Medical diagnostic imaging Monte Carlo methods Sampling methods Random forest logistic regression two-stage mixing abc algorithm 10-fold Monte-Carlo cross-validation synthetic minority oversampling technique

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Improvement of the thrust-torque ratio of an unmanned helicopter by using the abc algorithm

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2020年第8期92卷 1133-1139页

作者： Konar, Mehmet Turkmen, Aydin Oktay, Tugrul Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkey Erciyes Univ Grad Sch Nat & Appl Sci Dept Civil Aviat Kayseri Turkey Erciyes Univ Fac Aeronaut & Astronaut Dept Aeronaut Engn Kayseri Turkey

Purpose The purpose of this paper is to use an abc algorithm to improve the thrust-torque ratio of a rotating-wing unmanned aerial vehicle (UAV) model. Design/methodology/approach The design of UAVs, such as aircraft, drones, helicopters, has become one of the popular engineering areas with the development of technology. This study aims to improve the value of thrust-torque ratio of an unmanned helicopter. For this purpose, an unmanned helicopter was built at the Faculty of Aeronautics and Astronautics, Erciyes University. The maximum thrust-torque ratio was calculated considering the blade length, blade chord width, blade mass density and blade twist angle. For calculation, artificial bee colony (abc) algorithm was used. By using abc algorithm, the maximum thrust-torque ratio was obtained against the optimum input values. For this purpose, a model with four inputs and a single output is formed. In the generated system model, optimum thrust-torque ratio was calculated by changing the input values used in the +/- 5% range. As a result of this study, approximately 31% improvement was achieved. According to these results, the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV. Findings According to these results, approximately 31% improvement was achieved, and the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV. Research limitations/implications It takes a long time to obtain the optimum thrust-torque ratio value through the abc algorithm method. Practical implications Using abc algorithm provides to improve the value of thrust-torque ratio of an unmanned helicopter. With this algorithm, unmanned helicopter flies more than ever. Thus, the presented method based on the abc algorithm is more efficient. Social implications The application of the abc algorithm method can be used effectively to calculate the thrust-torque ratio in UAV. Originality/value Providing an original a

关键词： abc algorithm Thrust-torque ratio Helicopters UAVs Optimization

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Optimization of geometrical parameters in a specific composite lattice structure using neural networks and abc algorithm

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2016年第4期30卷 1763-1771页

作者： Yazdi, M. Sadegh Rostami, S. A. Latifi Kolahdooz, A. Babol Univ Technol Dept Mech Engn Babol Sar Iran Univ Semnan Fac Mech Engn Semnan Iran Islamic Azad Univ Khomeinishahr Branch Young Researchers & Elite Club Khomeinishahr Iran

Due to their light weights and high load carrying capacities, composite structures are widely used in various industrial applications especially in aerospace industry. Strength to weight ratio is known to be as one of the most critical design parameters in these structures. In this paper, geometrical parameters of composite lattice structures are optimized to obtain the desired strength to weight ratio using finite element method, neural networks and abc algorithm. At first, the finite element model is validated by experimental results and neural network is employed as the fitness function. The abc algorithm is also applied to achieve the optimized strength to weight ratio. The results obtained from PSO algorithm on the basis of neural network have shown reasonable agreement with those of the finite element simulation. Increasing the thickness of the outer shell causes the structural strength-to-weight ratio to rise by 50 percent. The next effective parameter is reduction of rib angle which provides an increase of 30 percent in strength-to-weight ratio. Although Stiffeners (ribs) have a major role in load carrying, increasing the rib thickness causes the structural weight to rise. Thus compared with the two previous parameters, they do not have a significant effect on the strength of structures.

关键词： Composite lattice structure Finite element Neural network abc algorithm

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Resonance self-shielding calculation using subgroup method and abc algorithm

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PROGRESS IN NUCLEAR ENERGY 2015年 78卷 303-309页

作者： Safarzadeh, O. Shirani, A. S. Minuchehr, A. Shahid Beheshti Univ Tehran Iran

In this study, we propose a new method to optimize subgroup parameters for resonance self-shielding calculation. Our approach integrates the merits of both the subgroup method and abc optimization technique to effectively evaluate self-shielded resonance cross-sections. The abc algorithm is used to obtain subgroup level in a way that guarantees reproduction of shielded effective cross sections in the subgroup formulation. The temperature dependency of the cross-section is included in both subgroup level and subgroup weight. We used the conjugate gradient method based on the normal equations (CGNR) to evaluate the subgroup weights. An iteration technique is also used to consider the resonance interference. The proposed method is verified by analyzing Rowlands benchmark problems and Mosteller benchmark problems and comparing the obtained results with corresponding Monte Carlo solutions. The multiplication factor results show small errors and also good agreement. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Resonance treatment Subgroup method abc algorithm Interference effects HERPAT

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Coverage Optimization of Field Observation Instrument Networking Based on an Improved abc algorithm 8th

Coverage Optimization of Field Observation Instrument Networ...

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8th International Conference on Pioneering Computer Scientists, Engineers and Educators (ICPCSEE)

作者： Deng, Xingyue Huo, Jiuyuan Wu, Ling Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Lanzhou Ruizhiyuan Informat Technol Co LTD Lanzhou 730070 Peoples R China

ISBN: (纸本)9789811952098;9789811952081

The severe conditions of cold and arid areas seriously affect the progress of data collection and analysis for field observation instruments. Therefore, this study adopted the modified artificial bee colony (abc) algorithm to optimize the coverage of nodes and designed an energy-efficient node coverage optimization method. In the coverage optimization, the coverage rate and the number of working nodes are considered comprehensively, and the fitness value calculation is improved. The experimental results reveal that the modified abc algorithm has better coverage optimization performance than the original abc algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm.

关键词： Field observation instrument networking Wireless sensor network abc algorithm Coverage optimization

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An Augmented Automatic Choosing Control of Formal Linearization Filter Type by abc algorithm and Its Application to Electric Power Systems 7

An Augmented Automatic Choosing Control of Formal Linearizat...

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Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Hachino, Tomohiro Takata, Hitoshi Osako, Soichiro Miyajima, Hiromi Komatsu, Kazuo Kawabata, Kimihisa Kagoshima Univ Dept Elect & Elect Engn Kagoshima 8900065 Japan Kumamoto Natl Coll Technol Kumamoto 8611102 Japan Kyushu Sangyo Univ Fukuoka 8138503 Japan

ISBN: (纸本)9781479959556

This paper is concerned with a single nonlinear feedback control for nonlinear systems with noisy measurement. A given nonlinear system is linearized piecewise so as to be able to design the linear optimal controllers, whose states are estimated by a formal linearization filter. By the automatic choosing function, these controllers are smoothly united into a single nonlinear feedback controller, which is called an augmented automatic choosing control of a formal linearization filter type. Since the formula of the proposed control is of a structure-specified type, the design parameters included in the controller and filter are appropriately determined using artificial bee colony algorithm. Simulation results show that the new controller enables to improve the stability of electric power systems well.

关键词： feedback linear programming linearisation techniques nonlinear control systems nonlinear programming power filters power system stability abc algorithm artificial bee colony algorithm augmented automatic choosing control electric power system design parameter formal linearization filter linear optimal controller noisy measurement nonlinear feedback control nonlinear system stability Educational institutions Equations Nonlinear systems Power system stability Radiation detectors Vectors

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Performance Comparison of Two Channel CMFB and QMF Bank Designed via abc algorithm 25

Performance Comparison of Two Channel CMFB and QMF Bank Desi...

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25th Signal Processing and Communications Applications Conference (SIU)

作者： Ozdemir, Gokcen Karaboga, Nurhan Koza, Turker Erciyes Univ Elekt Elekt Muhendisligi Bolumum Kayseri Turkey Bozok Univ Teknik Bilimler Meslek Yuksekokulu Yozgat Turkey

ISBN: (纸本)9781509064946

In this study, Cosine Modulated Filter Bank design and Quadrature Mirror Filter Bank design are performed using prototype filter designed with Artificial Bee Colony algorithm. The same design example parameters were used for both filter banks;the performances of the filter banks designed at different orders are compared in terms of stopband attenuation, mean square error, peak reconstruction error and computational time.

关键词： CMFB QMF Bank abc algorithm stopband attenuation peak reconstruction error mean square error

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An optimization model for aircraft maintenance scheduling based on abc algorithm

An optimization model for aircraft maintenance scheduling ba...

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2nd International Conference on Mechatronics and Intelligent Materials (MIM 2012)

作者： Jiang Ling-ping Civil Aviat Flight Univ China Aviat Engn Coll Guanghan 618307 Peoples R China

ISBN: (纸本)9783037853849

The problem of airline maintenance scheduling is considered in this paper. A maintenance-scheduling model that can determine rational maintenance date is established, in this model, while aircraft materials as well as other factors are taken as constraints, and aircraft air-on-ground (AOG) loss is set as goal function. In order to solve the model, Artificial Bee Colony (abc) algorithm is utilized by setting the appropriate number of the bees, which can find the optimal solution rapidly. Finally, airline's practical data is applied to validate the feasibility and practicality of the model and abc algorithm.

关键词： abc algorithm Aircraft maintenance Optimization

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