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Fuzzy proportional integral differential control of magnetorheological clutch torque based on bee colony algorithm optimization

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JOURNAL OF VIBRATION AND CONTROL 2024年第23-24期30卷 5450-5460页

作者： Xiao, Lu Wang, Xiao Chen, Fei Li, Aimin China Univ Min & Technol Sch Mech & Elect Engn Xuzhou 221116 Peoples R China

As an intelligent transmission device, the magnetorheological (MR) clutch cannot realize its accurate torque control because it is difficult to establish its accurate torque control mathematical model. This paper designs a squeeze shear MR clutch. In order to achieve accurate torque control of the MR clutch, this paper proposes a fuzzy proportional integral differential (PID) control strategy based on the optimization of the bee colony algorithm. Based on the mechanical transmission experiment platform of the MR clutch, the performance of the torque control of the MR clutch is experimentally studied by using the real-time operating system Phar Lap ETS built by LabVIEW real-time module, it is found that the fuzzy PID control strategy based on bee colony algorithm can not only improve the torque control accuracy of the MR clutch by 70% compared to traditional PID control strategies but also increase the response speed by more than 50%.

关键词： magnetorheological clutch torque control fuzzy proportional integral differential bee colony algorithm

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Performance based design optimum of CBFs using bee colony algorithm

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STEEL AND COMPOSITE STRUCTURES 2018年第5期27卷 613-622页

作者： Mansouri, Iman Soori, Sanaz Amraie, Hamed Hu, Jong Wan Shahbazi, Shahrokh Birjand Univ Technol Dept Civil Engn Birjand Iran Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran Islamic Azad Univ Roudehen Branch Roudehen Iran Incheon Natl Univ Dept Civil & Environm Engn Incheon 22012 South Korea Incheon Natl Univ Incheon Disaster Prevent Res Ctr Incheon 22012 South Korea TAAT Investment Grp Tehran Iran

The requirement to safe and economical buildings caused to the exploitation of nonlinear capacity structures and optimization of them. This requirement leads to forming seismic design method based on performance. In this study, concentrically braced frames (CBFs) have been optimized at the immediate occupancy (IO) and collapse prevention (CP) levels. Minimizing structural weight is taken as objective function subjected to performance constraints on inter-story drift ratios at various performance levels. In order to evaluate the seismic capacity of the CBFs, pushover analysis is conducted, and the process of optimization has been done by using bee algorithm. Results indicate that performance based design caused to have minimum structural weight and due to increase capacity of CBFs.

关键词： performance-based design CBF optimization bee colony algorithm pushover

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An artificial bee colony algorithm for inverse problems

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2009年第3期31卷 181-192页

作者： Ho, S. L. Yang, Shiyou Hong Kong Polytech Univ Dept Elect Engn Hong Kong Hong Kong Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310003 Zhejiang Peoples R China

Based on the metaphor of the foraging mechanism of honey bee swarms as well as on available works, an artificial bee colony algorithm is proposed for solving difficult inverse electromagnetic problems. In the proposed algorithm, the entire searching process is divided into an intensification and a diversification phase. For intensification searches, some novel formulas are proposed for the employed bees and onlookers to carry out exploiting searches around specific memorized food sources;and scouts are used to generate new food sources to guarantee the diversity of the algorithm in diversification searches. Also, an age variable is introduced to measure the "exhausted" level of a food source and then decide when to abandon a memorized food source. Three numerical examples are reported to validate the robustness and to demonstrate the advantages of the proposed algorithm.

关键词： bee colony algorithm evolutionary programming inverse problem swarm intelligence

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Improved artificial bee colony algorithm and its application in image threshold segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第2期81卷 2189-2212页

作者： Huo, Fengcai Wang, Yuanxiong Ren, Weijian Northeast Petr Univ Dept Elect Informat Engn Daqing 163318 Heilongjiang Peoples R China Heilongjiang Prov Key Lab Networking & Intelligen Daqing 163318 Heilongjiang Peoples R China Bohai Rim Energy Res Inst NEPU Qinhuangdao 066004 Hebei Peoples R China

Image segmentation is a key problem in the field of computer vision, especially in these fields, such as image processing, analysis and understanding. The key of the problem to be solved is how to obtain reasonable threshold according to the different types of images. Based on these, an improved artificial bee colony algorithm based on Tent mapping in chaos theory is proposed and applied to image threshold segmentation. Firstly, a complementary encoding scheme for the artificial bee colony algorithm is constructed from on the Tent map in chaos theory. According to the characteristics between the current solution and the optimal solution, a fixed orientation updating method is proposed in the colony algorithm updating strategy. The difference between 1 and [0, 1] is still in the range of [0, 1], the way adjust the local optimal solution by the complementary properties. All these construct an Improved Artificial bee colony based on Tent Mapping (IABCTM) algorithm. Secondly, according to the characteristics of the algorithm, the algorithm is convergent with probability 1. Finally, the improved algorithm is applied to image threshold segmentation. Through the comparison of multiple images, multiple performance parameters and multiple algorithms, the improved algorithm is proved to have a strong ability to obtain the optimal solution and a good convergence performance.

关键词： Tent mapping Update strategies Image threshold Segmentation bee colony algorithm

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Clustering based on improved bee colony algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第3期46卷 276-282页

作者： Xiao, Yonghao Yu, Weiyu Cao, Yunfei Tan, Haishu Foshan Univ Sch Elect & Informat Engn Foshan 528000 Peoples R China South China Univ Technol Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China

Clustering is concerned with partitioning a dataset into homogeneous groups. One of the most popular clustering methods is k-means clustering because of its simplicity and computational efficiency. K-means clustering involves search and optimisation. The main problem with this clustering method is its tendency to converge to local optima. bee colony algorithm has emerged as one of the robust and efficient global search heuristics of current interest. This paper describes an application of improved bee colony algorithm to the clustering of data and image segmentation. In contrast to most of the existing clustering techniques, the proposed approach requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of partitions of the data 'on the run'.

关键词： bee colony algorithm clustering global optimisation swarm intelligence

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Optimization of the Temperature Regime of Phosphorite Pellet Roasting Using the Coevolutional bee colony algorithm

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第3期34卷 530-534页

作者： Bobkov, V. I. Bulygina, O. V. Branch Natl Res Univ Moscow Power Engn Inst Smolen Smolensk 214013 Russia

The article examines the scientific and practical problem of increasing the energy and resource efficiency of chemical and technological processes to produce phosphorite pellets from waste apatite-nepheline ores. A promising way to solve this problem is to find the optimal temperature regime for heating the charge in a roasting conveyor machine. The specifics of the optimization problem posed (nonlinearity of the objective function, large dimension of the search space, high computational complexity) determine the advisability of using stochastic methods, particularly the bee colony algorithm. To determine the values of "free" parameters that significantly affect the convergence of the algorithm, it was proposed to use the idea of coevolution, which consists in parallel running of several canonical bee colony algorithms with different "settings" for each subpopulation. Control of the chemical-technological system based on the results obtained will ensure energy-resource-saving operating conditions for the roasting conveyor machine.

关键词： energy and resource efficiency phosphorite pellets roasting conveyor machine temperature regime coevolution bee colony algorithm fuzzy logic

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Human-Robot Collaboration Multi-objective Disassembly Line Balancing Subject to Task Failure via Multi-objective Artificial bee colony algorithm 3rd

Human-Robot Collaboration Multi-objective Disassembly Line B...

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3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS)

作者： Xu, Chengcheng Wei, Haiping Guo, XiWang Liu, ShiXin Qi, Liang Zhao, ZiYan Liaoning Shihua Univ Coll Comp & Commun Engn Fushun 113001 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shandong Univ Sci & Technol Dept Intelligent Sci & Tech Qingdao 266590 Peoples R China

Disassembly is a crucial process of remanufacturing end-of-life products. It can be done by either humans or robots. Manual disassembly has a high cost and low disassembly efficiency, while a robot is not flexible to perform different dynamic, complicated tasks. The combination of them can improve the disassembly efficiency. Because it is hard to know the detailed information about the wear and tear of the used products, there is a risk of disassembly failure. In this work, disassembly failure risks, constraints of disassembly priority, cycle time, and actual cost are taken into full consideration based on an AND/OR graph of used products. Besides, a mathematical model is constructed with the objectives of maximizing the profit and minimizing consumption, disassembly difficulties, and the number of workstations. Then, a new multi-objective artificial bee colony algorithm is proposed, which is combined with stochastic simulation. The effectiveness and feasibility of the proposed algorithm are verified and experimental results show that the proposed algorithm outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimization algorithm. Copyright (C)2020 The Authors.

关键词： Human-robot cooperation bee colony algorithm Disassembly line balancing Remanufacturing Failure risk Stochastic simulation Multi-objective disassembly

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A Parallel bee colony algorithm for Resource Allocation Application in Cloud Computing Environment

A Parallel Bee Colony Algorithm for Resource Allocation Appl...

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IEEE International Conference on Data Science and Data Intensive Systems

作者： Wen, Tingxi Zhang, Zhongnan Wang, Meihong Xiamen Univ Software Sch Xiamen Peoples R China

ISBN: (纸本)9781509002146

Cloud computing has been widely used in every social field. The problem of energy consumption in a cloud computing environment has brought cost pressure to service providers and affected the natural environment. However, the reasonable and efficient scheduling of resources could save a lot of energy for cluster. Meanwhile, it's necessary for us to take into account of the emergent needs of every consumer. So the resource scheduling is often regarded as a multi-objective problem with the optimization of energy consumption and time cost. We redefine the problem in this paper and set up a multi-objective optimization model, and the parallel computing is improved on the basis of bee colony algorithm. Furthermore, multi-objective problem optimization based on fast non-dominated sorting method is used in parallel environment. Experimental results show that the proposed algorithm can save energy, reduce the execution time of tasks and have very good stability in parallel environment.

关键词： cloud computing bee colony algorithm resource allocation multi-objective parallel computing

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Parameters Identification Method of Interval Discrete Dynamic Models of Air Pollution Based on Artificial bee colony algorithm 10

Parameters Identification Method of Interval Discrete Dynami...

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10th International Conference on Advanced Computer Information Technologies (ACIT)

作者： Dyvak, Mykola Ternopil Natl Econ Univ Dept Comp Sci Ternopol Ukraine

ISBN: (纸本)9781728167602

A new method for parametric identification of interval discrete dynamic models based on artificial bee colony algorithm has been proposed and justified. Proposed method is based on a new mathematical description of a searching process of a global minimum of discrete non-linear objective function under given constraints for the model parameters values. An example of the method application for identification of interval model of Carbon monoxide distribution process on a straight section of a street due to uniform vehicle traffic with a constant emission intensity is given. Higher efficiency of the proposed method compared to the existing methods of random search is shown.

关键词： interval discrete dynamic model bee colony algorithm parametric identification difference equation interval data

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Dynamic Function Optimization by Improved Artificial bee colony algorithm

Dynamic Function Optimization by Improved Artificial Bee Col...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Jiang, Shuo Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China

ISBN: (纸本)9783038351153

In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and original artificial bee colony algorithm (ABC), the result of dynamic function optimization shows that the IABC can obtain satisfactory solutions and good tracing performance for dynamic function in time.

关键词： bee colony algorithm ABC dynamic function optimization

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