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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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Load Balancing Of Tasks In Cloud Computing Environment Based On bee colony algorithm 5

Load Balancing Of Tasks In Cloud Computing Environment Based...

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Fifth International Conference on Advances in Computing and Communications (ICACC)

作者： Babu, K. R. Remesh Joy, Amaya Anna Samuel, Philip Govt Engn Coll Idukki Dept IT Painavu Kerala India CUSAT Sch Engn Div IT Kochi Kerala India

ISBN: (纸本)9781467369947

Cloud computing is an emerging computing paradigm in which shared resources are provided according to the customer request at specific time. Load balancing is the process of distributing workload among various nodes of the computing system. The load can be CPU load, memory capacity, or network load. An efficient load balancing avoids a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. When Virtual Machine (VM) is overloaded with multiple tasks, these tasks are removed and migrated to the under loaded VMs of the same or different datacenter. This paper proposes a bee colony based algorithm for efficient load balancing, which is based on the foraging behavior of honey bees to balance load across VMs. In the proposed method, tasks removed from over loaded VMs are treated as honey bees and under loaded VMs are the food sources. The proposed method also considers the priorities of tasks in the waiting queues of VMs and tries to achieve minimum response time and reduced number of task migrations. The experimental result shows that there is significant improvement in the Quality of Service (QoS).

关键词： Cloud computing Load balancing bee colony algorithm QoS

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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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Model Development for Optimized Land Usage in Urban Agglomeration Zone Using bee colony algorithms

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MICROPROCESSORS AND MICROSYSTEMS 2021年 81卷 103739-103739页

作者： Qian, Lu Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210000 Peoples R China

This research work was developed to increase the output of urban zone regions and also to bring out the best use of the space of the urban zone regions. The problems associated with the current research work is basically poor accuracy, slow convergence speed and low rationality of evaluation index. We have proposed to use bee colony algorithm which optimizes the usage areas of land in urban zone regions. The work that we developed a model using the bee colony algorithm for best optimization. The various evaluation units are divided by grid method. By this algorithm principle the weighted summation method of indexes is used to calculate the evaluation units. The the evaluation model is developed by using the above calculated evaluation value of land usage spatial adaptability in the required regions. By the evaluation function of urban land suitability, the cost function and spatial coordination function of urban land usage and the spatial optimization model of land use in urban agglomeration development zone is constructed. The model is developed by the bee colony algorithm and best fitting solution of land usage in urban agglomeration development zone is obtained. The research results prove that the evaluation index of the proposed method is reasonable, the accuracy of the evaluation results is high, and the convergence speed is fast.

关键词： Land Area Optimization Evaluation Units Urban Urban Development Zone Weighted Summation Method bee colony algorithm

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SRA-E-ABCO: terminal task offloading for cloud-edge-end environments

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2024年第1期13卷 1-17页

作者： Jiao, Shun Wang, Haiyan Luo, Jian Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Peoples R China Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210023 Peoples R China

The rapid development of the Internet technology along with the emergence of intelligent applications has put forward higher requirements for task offloading. In Cloud-Edge-End (CEE) environments, offloading computing tasks of terminal devices to edge and cloud servers can effectively reduce system delay and alleviate network congestion. Designing a reliable task offloading strategy in CEE environments to meet users' requirements is a challenging issue. To design an effective offloading strategy, a Service Reliability Analysis and Elite-Artificial bee colony Offloading model (SRA-E-ABCO) is presented for cloud-edge-end environments. Specifically, a Service Reliability Analysis (SRA) method is proposed to assist in predicting the offloading necessity of terminal tasks and analyzing the attributes of terminal devices and edge nodes. An Elite Artificial bee colony Offloading (E-ABCO) method is also proposed, which optimizes the offloading strategy by combining elite populations with improved fitness formulas, position update formulas, and population initialization methods. Simulation results on real datasets validate the efficient performance of the proposed scheme that not only reduces task offloading delay but also optimize system overhead in comparison to baseline schemes.

关键词： Cloud-edge-end Terminal device Service reliability bee colony algorithm Task offloading

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Optimal design of frequency load control system in multi-zone power system using PID-fuzzy controller optimized by bee algorithm

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2023年第2期6卷 291-303页

作者： Liu, Jianhui Xian Univ Architecture &Technol Sch Mech & Elect Engn Xian 710055 Shaanxi Peoples R China

Today, with the increasing expansion of power systems and the emergence of frequency as one of the criteria for measuring the quality of delivered power, the need to control it has become inevitable. The main goal of load frequency control (LFC) is to maintain the frequency at the nominal value and the exchange power at the contractual value. In this field, various control methods have been presented, among which PI and PID controllers are among the most common controllers due to their simplicity and implementability. But with the development and increasing penetration of renewable resources and other resources, the use of these controllers has been associated with uncertainty. In this article, the modeling of a two-zone multi-source frequency load control system with the presence of water sources has been discussed. If the system load increases, increase the inlet to Turin by opening the steam or water valve to prevent the generator speed and frequency from decreasing. Conversely, by reducing the consumption of electrical energy, the amount of steam or flow rate of incoming water should be reduced to prevent the increase in network frequency. Due to the nonlinear nature of the power system, it is appropriate to use the nonlinear method in the system. In this paper, a fuzzy logic-based controller (PID-fuzzy) is used in the load-frequency control loop. To control and stabilize this model, classical proportional-derivative-integral controller as well as proportional-derivative-integral controller is designed and compared based on fuzzy logic. The proportional-derivative-integral controller based on fuzzy logic is able to change the gain coefficients based on the interception error and its rate of change, and it will be more resistant and perform better than the classical proportional-derivative-integral controller. In this study, a modified version of the bee colony algorithm and the ISTSE objective function is used to design the proposed controller. The performance o

关键词： Load-frequency control ring PID-fuzzy controller bee colony algorithm

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Production-Routing-Inventory in Post-Disaster Conditions: a Multi-Objective Mathematical Model and Two algorithms

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2022年第4期6卷 1163-1183页

作者： Zargary, Shima Samouei, Parvaneh Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Hamadan Iran

The production-routing-inventory problem is a combination of three classic issues, routing, inventory, and lot sizing. Reviewing these issues together in the integrity of the supply chain is very important. On the other hand, to preserve the lives of affected people, before, during, and after a disaster, considering the crisis is a humanitarian affair. In this paper, we presented a bi-objective mixed-integer linear model. The first objective function minimized the cost of producing relief goods and holding inventory, and the second one minimized the time to get relief goods to the affected people. To solve the model, we used the GAMS software for small-sized problems and the simulated annealing (SA) and the bee colony (BC) algorithms for large-sized problems. Results showed that the SA and BC algorithms provided close answers, but the SA algorithm could achieve the results in less time than the BC algorithm.

关键词： Supply chain Production-routing-inventory Post-disaster condition Simulated annealing algorithm 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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CHEABC-QCRP: A novel QoS-aware cluster routing protocol for industrial IoT

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SIMULATION MODELLING PRACTICE AND THEORY 2024年 134卷

作者： Wang, Fengjiang Rao, Chuchu Fang, Xiaosheng Lan, Yeshen Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China Quzhou Coll Technol Sch Mech & Elect Engn Quzhou 324000 Peoples R China Shantou Univ Coll Engn Dept Elect & Informat Engn Shantou Peoples R China

Clustering routing protocols currently have problems such as Single point of failure of cluster head nodes, poor network dynamics, uneven data transmission, etc., which are critical to the optimization of energy efficiency, network lifespan and network topology control. However, this optimization problem is an NP hard problem that conventional algorithms are difficult to solve. This paper proposes a new multi-objective cluster routing protocol (CHEABC-QCRP) aimed at optimizing network energy consumption, system lifespan, and quality of services (QoS). The protocol is based on a new chaotic hybrid elite artificial bee colony algorithm (CHEABC) proposed in this paper, which has strong search ability and greatly reduces convergence time. At the same time, a new chaotic strategy was designed to effectively prevent falling into local optima and premature convergence. In simulation experiments, compared with multiple routing protocols, a large number of test results show that this protocol significantly reduces network energy consumption, greatly improves system lifespan, and effectively improves QoS in IWSN.

关键词： Industrial internet of things Cluster routing protocol bee colony algorithm Energy consumption Network lifespan

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Research on outdoor AGV precise navigation based on BDS/INS data fusion

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期41卷 4295-4306页

作者： Tang, Yongwei Hao, Huijuan Zhou, Jun Lin, Yuexiang Dong, Zhenzhen Shandong Univ Sch Mech Engn Key Lab High Efficiency & Clean Mech Manufacture Minist Educ Jinan 250061 Peoples R China Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Natl Supercomp Ctr JinanShandong Key Lab Comp Ne Jinan Peoples R China Org Qingdao Agr Univ Coll Mech & Elect Engn Qingdao Peoples R China

AGV (Automated Guided Vehicle) technology has attracted increasing attention. Precise control of AGV position and attitude information in complex operating environment is a key part of smart factories. With outdoor AGV as a platform, this study uses BDS/INS combined navigation system combining Beidou positioning system and inertial navigation system and takes the velocity and position difference between BDS and INS as a model. An integrated navigation method is proposed to improve bee colony algorithm and optimize the BP neural network-assisted Kalman filtering to achieve accurate positioning. Moreover, the optimization of BP neural network navigation using INS navigation, network-assisted navigation and bee colony algorithm is simulated. Results demonstrate that the integrated navigation algorithm has effectiveness and feasibility, and can solve the problems of BDS misalignment and large INS navigation error in complex environments.

关键词： AGV BDS/INS integrated navigation Kalman filter neural network bee colony algorithm

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