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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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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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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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Design of Requirement Value Evaluation System Based on bee colony algorithm 3

Design of Requirement Value Evaluation System Based on Bee C...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Wu, Fangquan Liu, Yichi Li, Xiong Yin, Zhiyi Guizhou Power Grid Co. Ltd. Guizhou China

ISBN: (纸本)9798350317022

Manual demand value assessment is highly subjective and sometimes lacks theoretical basis, which can easily lead to the loss of high-value demand. Demand value assessment requires digital capabilities to support. This article establishes a demand mining and demand value evaluation system, which effectively manages and scientifically analyzes the demand data of electricity customers. The results indicate that it can provide a basis for user demand information analysis and comprehensive analysis management, customer demand mining and demand value analysis, and make customer demand worth evaluating correctly. It helps customer managers better provide professional and high-quality services for electricity customers. Comparison with bee colony algorithm, the accuracy of the algorithm constructed in this paper is significantly improved. At the same time, the accuracy of the algorithm constructed in this paper is close to that of mobile digital signature technology, both of which is greater than 80%, which is better than ASP technology. © 2023 IEEE.

关键词： bee colony algorithm Evaluation index Power Industry Value Evaluation System

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A bee colony algorithm for Talent Resources Demand Control System 3

A Bee Colony Algorithm for Talent Resources Demand Control S...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Huang, Jingchang College of Management Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798350317022

The traditional talent resources demand control method based on genetic algorithm has problems such as opaque information, uneven resource allocation, and high labor cost. In order to solve these problems, a talent resources demand system by the bee colony algorithm came into being in this paper. It aims to design an efficient and intelligent human resource management system by introducing the features. The results indicate that the system can match human resource needs more accurately and quickly, achieve optimal allocation of resources, and improve work efficiency and employee satisfaction compared with traditional methods. In addition, the system provides employees with personalized learning and development opportunities to improve their career development and performance levels. By combining the advantages of the bee colony algorithm and the actual needs of human resource management, the efficient use of resources and the optimization of decision-making can be achieved, thereby improving the competitiveness and sustainable development of the organization. © 2023 IEEE.

关键词： bee colony algorithm Decision Optimization Performance Evaluation Resource Allocation Talent Recruitment Talent Resources Demand Control System Training Plan

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Optimization of the free vibrations of doubly-curved sandwich panels using the artificial bee colony optimization algorithm

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 607-615页

作者： Du, Zhaofang Henan Ind & Trade Vocat Coll Zhengzhou 450053 Henan Peoples R China

This study examines the optimization of double-curved sandwich panels containing composite layers and magnetorheological (MR) fluid cores to determine the optimal parameters that influence free vibration. The bee optimization algorithm was applied to minimize the mass and maximize the first modal loss factor. Additionally, the improved high-order theory (IHOT) and extended Hamilton's principle were applied for the first time to extract the motion equations of the panel. The layers and core thickness, the fibers angle, and magnetic intensity have been assumed to be optimization parameters. For the first time, the technique for order of preference by similarity to ideal solution (TOPSIS) technique has been applied to obtained the optimal design points from a set of optimal points. This indicates that in a similar situation regarding dimensions and mass, doubly-curved panels exhibit a greater modal loss factor than single-curved panels. The results of the bee and particle swarm optimization algorithms demonstrate that the bee algorithm provides the modal and mass loss factors 50% and 30% better than the particle swarm algorithm, respectively. As a result, this method is highly recommended for analyzing such issues.

关键词： Optimization Sandwich panel Loss factor bee colony algorithm Pareto front

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On Parameter Identification for Reaction-Dominated Pore-Scale Reactive Transport Using Modified bee colony algorithm

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algorithmS 2022年第1期15卷 15页

作者： Grigoriev, Vasiliy V. Iliev, Oleg Vabishchevich, Petr N. North Eastern Fed Univ Multiscale Model Reduct Lab 58 Belinskogo St Yakutsk 677000 Russia Fraunhofer Inst Ind Math ITWM Fraunhofer Pl 1 D-67663 Kaiserslautern Germany Inst Math & Informat Bulgarian Acad Sci 8 Akad G Bonchev St Sofia 1113 Bulgaria Nucl Safety Inst Russian Acad Sci 52 B Tulskaya Moscow 115191 Russia North Eastern Fed Univ Lab Computat Technol Modeling Multiphys & Multisc 58 Belinskogo St Yakutsk 677000 Russia

Parameter identification is an important research topic with a variety of applications in industrial and environmental problems. Usually, a functional has to be minimized in conjunction with parameter identification;thus, there is a certain similarity between the parameter identification and optimization. A number of rigorous and efficient algorithms for optimization problems were developed in recent decades for the case of a convex functional. In the case of a non-convex functional, the metaheuristic algorithms dominate. This paper discusses an optimization method called modified bee colony algorithm (MBC), which is a modification of the standard bees algorithm (SBA). The SBA is inspired by a particular intelligent behavior of honeybee swarms. The algorithm is adapted for the parameter identification of reaction-dominated pore-scale transport when a non-convex functional has to be minimized. The algorithm is first checked by solving a few benchmark problems, namely finding the minima for Shekel, Rosenbrock, Himmelblau and Rastrigin functions. A statistical analysis was carried out to compare the performance of MBC with the SBA and the artificial bee colony (ABC) algorithm. Next, MBC is applied to identify the three parameters in the Langmuir isotherm, which is used to describe the considered reaction. Here, 2D periodic porous media were considered. The simulation results show that the MBC algorithm can be successfully used for identifying admissible sets for the reaction parameters in reaction-dominated transport characterized by low Pecklet and high Damkholer numbers. Finite element approximation in space and implicit time discretization are exploited to solve the direct problem.

关键词： pore-scale model of reactive transport adsorption isotherm parameter identification Langmuir isotherm bee colony algorithm swarm intelligence metaheuristics numerical optimization

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The Optimal Route Selection Model of Fresh Agricultural Products Transportation Based on bee colony algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第12期13卷 489-499页

作者： Ren, Qingqing Hainan Vocat Univ Coll Econ & Management Haikou 570100 Peoples R China

In order to optimize the distribution route of fresh agricultural products and reduce the distribution cost, the optimal route selection model of fresh agricultural products transportation based on bee colony algorithm is constructed. After establishing the transportation road network model of fresh agricultural products and analyzing the transportation road information, the transportation road network zoning is realized by the dynamic zoning method of transportation road network based on spectral clustering algorithm, taking the unblocked area and congested area as the zoning purpose. Based on the traffic zoning information, the transportation route optimization model of fresh agricultural products with time window is constructed. Solved by bee colony algorithm to obtain the distribution route of fresh agricultural products with the lowest distribution cost and the highest customer satisfaction. The experimental results show that the model can choose the fresh agricultural products distribution route with the lowest distribution cost and the highest customer satisfaction under the two working conditions of smooth traffic and congestion.

关键词： bee colony algorithm fresh agricultural products transportation optimal route selection road network model spectral clustering

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A self-learning bee colony and genetic algorithm hybrid for cloud manufacturing services

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COMPUTING 2022年第9期104卷 1977-2003页

作者： Li, Tianhua Yin, Yongcheng Yang, Bo Hou, Jialin Zhou, Kai Shandong Agr Univ Coll Mech & Elect Engn Tai An 271018 Shandong Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

At present, cloud services and cloud manufacturing are developing rapidly, speed and accuracy have become the themes of cloud manufacturing development. The core component of cloud manufacturing is resource portfolio optimization. In cloud manufacturing today, the scale of service portfolios is expanding rapidly, and the number of candidate services which in the service pool is increasing gradually. To adapt to the development of cloud services, an optimization algorithm with faster speed, greater precision and higher stability is required to solve the problem of cloud service composition and optimization (CSCO). To increase the convergence rate and avoid falling into local optima with the artificial bee colony algorithm, a self-learning artificial bee colony genetic algorithm (SLABC-GA) is proposed in this paper, which is based on reinforcement learning (RL), and the RL is used to intelligently select the number of dimensions of each update of a feasible solution. A global optimal individual is used to search and guide the search equation to avoid obtaining local optima and improve algorithm development and the precision of the traditional artificial bee colony algorithm (ABC). A genetic algorithm (GA) is introduced in a later stage of the algorithm to further improve its accuracy and convergence speed. Additionally, this paper analyzes and constructs the self-learning model in SLABC, the optimal learning method is Q-learning algorithm, and designs a reward method and state determination method of RL in the environment of the bee colony algorithm. Finally, a large number of comparative experiments have been carried out, the results show that the accuracy and speed of the SLABC-GA outperform for CSCO problems, and the performance of the SLABC-GA for large-scale CSCO problems is better than that of genetic algorithm (GA) and the traditional artificial bee colony algorithm (ABC).

关键词： Cloud manufacturing Reinforcement learning (RL) Service composition and optimization bee colony algorithm Genetic algorithm Quality of service

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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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