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Advances in Electronics, Control and Communication Systems (ICAECCS), International Conference on

作者： Ait Sahed Oussama Kara Kamel Benrabah Mohamed Fas Mohamed Lamine Department of Automatics LabSET University of Blida 1 Blida Algeria

It is known that the ABC algorithm has a slow convergence rate, this could be attributed to the fact that new solutions are generated by updating only one optimization parameter. Therefore, updating more optimization parameters could enhance the optimization performances. However, increasing this number arbitrary could decrease the optimization performance, as updating only one optimization parameter could be more efficient for some cases. To this end, we are proposing a new ABC variant that can adaptively control the number of optimization parameters to update during the *** performance of the proposed approach has been evaluated against five other ABC variants using 16 numerical benchmark functions of varying characteristics. The obtained results have demonstrated the efficiency of the proposed algorithm.

关键词： Adaptive systems Communication systems Benchmark testing artificial bee colony algorithm Control systems Optimization Convergence

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Interference Mitigation Using Particle Swarm Optimization algorithm in Television White Space

Interference Mitigation Using Particle Swarm Optimization Al...

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Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Joachim Notcker Emmanuel Adetiba Abdultaofeek Abayomi Kennedy K. Ronoh Oluwadamilola Oshin Kenedy Aliila Greyson Electrical and Information Engineering Covenant University Ota Nigeria Information and Communication Technology Mangosuthu University of Technology Durban South Africa Computing and Engineering Science Strathmore University Nairobi Kenya Electronics and Telecommunication Engineering Dar es Salaam Institute of Science and Technology Dar es Salaam Tanzania

Television white space is a promising technology that addresses the issue of spectrum scarcity in wireless communication. Recent years have seen a rise in the number of studies focusing on the propagation characteristics of the Television White Space (TVWS), which is the frequency range between 54MHz and 790MHz. However, interference is one of the significant issues that limit the utilization of available spectrum in TV bands, lower the quality of services among cognitive(secondary) users, and cause harmful destruction to licensed users. In literature, a lot of work has been done to address the challenge of interference in TVWS networks but many of them focused on interference between primary and secondary users in order to protect the licensed (primary) users while few did among secondary users. As a result, in this work, we employ a particle swarm optimization algorithm to optimize the spectrum and reduce interference among secondary users. Furthermore, we compare the performance of particle swarm optimization with the artificial bee colony algorithm. Simulation results obtained show that particle swarm optimization outperforms the artificial bee algorithm thus indicting its strength in reducing interference among secondary users.

关键词： Wireless communication TV Simulation Interference Quality of service White spaces artificial bee colony algorithm

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Enhancing the effectiveness of green technology in manufacturing industry

Enhancing the effectiveness of green technology in manufactu...

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International Conference on Futuristic Research in Engineering Smart Materials (FRESM)

作者： Jibhakate, Rahul A. Nirwan, Nilesh W. Rambhad, Kishor S. Natl Inst Technol Dept Mech Engn Silchar 788010 India GH Raisoni Coll Engn Dept Mech Engn Nagpur 440016 Maharashtra India JSPM Narhe Tech Campus Dept Mech Engn Pune 411041 Maharashtra India

Manufacturing is the process of transforming raw materials into a finished product. During this transformation, energy, cutting fluids, sound emission, dust particles, scraps, and other waste items contribute an environmental pollution in industries. This paper discussed about various green technology in order to reduce environmental pollution in industries. Manufacturing must employ productive approaches and innovations to realise the dream of a sustainable society. Sustainable manufacturing aims to produce the same amount of product with fewer inputs and reducing product waste during transition. There are several approaches to proposing the realisation of sustainable development that are typically complicated and focused more on resource components. Several variables in industrial units, such as emissions management, waste prevention, and worker environment variables such as noise and aerosol, have yet to be thoroughly investigated. Opportunities for improvement, particularly in manufacturing units, yet to be identified. A systematic study of manufacturing facilities established a practical approach to mathematically explaining the relationships between different process variables and energy consumption, waste reduction, and the workplace environment. Industrial units are modeled and their corresponding process parameters are optimized using the artificial bee colony algorithm (ABC) to increase the greenness of these operations. This study will be used as a reference for the selection and promotion of best practices in green technology in the manufacturing industry. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Manufacturing industry units Green Technology Sustainable Manufacturing

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A Novel Technique for Evaluating Optimal Power Flow and SVC Performance using the ABC algorithm

A Novel Technique for Evaluating Optimal Power Flow and SVC ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： N. Kalpana Department of EEE MECS Hyderabad

artificial bee colony (ABC) method is explored in this paper to solve optimum power flow (OPF) problems in power systems by using a static VAR compensator (SVC). With the usage of ABC the system can reduces the overall generating cost of a power system by employing SVC devices; in addition to that it also maintains Voltage stability. The ABC is developed which is influenced by honey bees' browsing behavior in the discovery of the appropriate nectars. It is a newly developed optimization algorithm in power systems. The suggested ABC method was compared to existing optimization algorithms on IEEE 11-bus & IEEE 30-bus systems to examine how effectively it functioned. Result shows that to handle nonlinear problems in power systems, ABC can be strongly accepted it is widely used in power systems.

关键词： Renewable energy sources Costs Runtime Sensitivity analysis Static VAr compensators Power system stability artificial bee colony algorithm

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Research of Multipath Routing and Load Balancing Processes in Software Defined Networks Based on Bird Migration algorithm

Research of Multipath Routing and Load Balancing Processes i...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Dmitry Perepelkin Maria Ivanchikova Tin Nguyen Faculty of Computer Engineering Ryazan State Radio Engineering University Ryazan Russian Federation

Technology software defined networks (SDNs) allows managing the network at the software level, expanding the functionality to control the process of data transfer in the network. SDNs provide a new level of network application abstraction to provide a level of quality of service. The purpose of the work is to develop a scientific approach to the study of multipath routing and load balancing in the SDNs based on the bird migration algorithm (BMA). The article studies and analyzes the bird migration algorithm to solve the problem of multipath routing in the SDNs. A visual software system SDNLoadBalancer has been developed and an experimental SDN topology has been designed, which makes it possible to study in detail the processes of multipath routing in the SDNs based on the proposed approach. The article also compares the proposed approach with the results of the genetic algorithm and the artificial bee colony algorithm. The obtained results show the effectiveness of the proposed approach in the SDNs, made it possible to obtain results close to optimal in an acceptable time, and also reduce the transmission delay jitter in the network.

关键词： Visualization Software algorithms Birds Routing Load management Software systems artificial bee colony algorithm

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Rate-Dependent Modeling of Piezoelectric Actuators for Nano Manipulation Based on Fractional Hammerstein Model

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MICROMACHINES 2022年第1期13卷 42-42页

作者： Yang, Liu Zhao, Zhongyang Zhang, Yi Li, Dongjie Harbin Univ Sci & Technol Sch Automat Harbin 150040 Peoples R China Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Complex Intelligent Sys Harbin 150040 Peoples R China

Piezoelectric actuators (PEAs), as a smart material with excellent characteristics, are increasingly used in high-precision and high-speed nano-positioning systems. Different from the usual positioning control or fixed frequency tracking control, the more accurate rate-dependent PEA nonlinear model is needed in random signal dynamic tracking control systems such as active vibration control. In response to this problem, this paper proposes a Hammerstein model based on fractional order rate correlation. The improved Bouc-Wen model is used to describe the asymmetric hysteresis characteristics of PEA, and the fractional order model is used to describe the dynamic characteristics of PEA. The nonlinear rate-dependent hysteresis model can be used to accurately describe the dynamic characteristics of PEA. Compared with the integer order model or linear autoregressive model to describe the dynamic characteristics of the PEA Hammerstein model, the modeling accuracy is higher. Moreover, an artificial bee colony algorithm (DE-ABC) based on differential evolution was proposed to identify model parameters. By adding the mutation strategy and chaos search of the genetic algorithm into the previous ABC, the convergence speed of the algorithm is faster and the identification accuracy is higher, and the simultaneous identification of order and coefficient of the fractional model is realized. Finally, by comparing the simulation and experimental data of multiple sets of sinusoidal excitation with different frequencies, the effectiveness of the proposed modeling method and the accuracy and rapidity of the identification algorithm are verified. The results show that, in the wide frequency range of 1-100 Hz, the proposed method can obtain more accurate rate-correlation models than the Bouc-Wen model, the Hammerstein model based on integer order or the linear autoregressive model to describe dynamic characteristics. The maximum error (Max error) is 0.0915 mu m, and the maximum mean square

关键词： hammerstein model bouc-wen model fractional model rate correlation artificial bee colony algorithm

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A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance

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BUILDINGS 2022年第7期12卷 919页

作者： Wang, Jiangfeng Mohammed, Ahmed Salih Macioszek, Elzbieta Ali, Mujahid Ulrikh, Dmitrii Vladimirovich Fang, Qiancheng North China Univ Water Resources & Elect Power Coll Geosci & Engn Zhengzhou 450046 Peoples R China Univ Sulaimani Coll Engn Civil Engn Dept Sulaymaniyah 46001 Iraq Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia South Ural State Univ Inst Architecture & Construct Dept Urban Planning Engn Networks & Syst 76 Lenin Prospect Chelyabinsk 454080 Russia Huanghuai Univ Inst Architecture Engn Zhumadian 463000 Peoples R China

Numerous studies have reported the effective use of artificial intelligence approaches, particularly artificial neural networks (ANNs)-based models, to tackle tunnelling issues. However, having a high number of model inputs increases the running time and related mistakes of ANNs. The principal component analysis (PCA) approach was used in this work to select input factors for predicting tunnel boring machine (TBM) performance, specifically advance rate (AR). A reliable and precise forecast of TBM AR is desirable and critical for mitigating risk throughout the tunnel building phase. The developed PCAs (a total of four PCAs) were used with the artificial bee colony (ABC) method to predict TBM AR. To assess the created PCA-ANN-ABC model's capabilities, an imperialist competitive algorithm-ANN and regression-based methods for estimating TBM AR were also suggested. To evaluate the artificial intelligence and statistical models, many statistical evaluation metrics were evaluated and generated, including the coefficient of determination (R-2). The findings indicate that the PCA-ANN-ABC model (with R-2 values of 0.9641 for training and 0.9558 for testing) is capable of predicting AR values with a high degree of accuracy, precision, and flexibility. The modelling approach utilized in this study may be used to other comparable studies involving the solution of engineering challenges.

关键词： PCA ANN artificial bee colony algorithm TBM advance rate hard rock condition

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An Improved Hierarchal Agglomerative Grouping Process and Bi-Objective Fusion Optimization Process for Optimum Source Detection

An Improved Hierarchal Agglomerative Grouping Process and Bi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Dava Srinivas Jyothi N T Raghavendra Gupta Gandla Shivakanth N. Samanvita Department of CSE Jyothishmathi institute of technology & Science Karimnagar India Department of Computer Science & Engineering H. K. E. Society's S. L. N. College of Engineering Raichur India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Telangana India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electrical & Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Computing in the cloud refers to a model that offers inexpensive, scalable computing resources like CPU, storage, and network bandwidth. Allows users to access a shared pool of resources via the internet on an as-needed, pay-per-use basis. In order to organize tools, this chapter suggests using the Hierarchical Agglomerative clustering algorithm. The time spent searching through a pool of available resources can be minimized by categorizing them. Thanks to the categorization of resources, the one needed to fulfill a request can be found and assigned in a flash. The following outline is used for this section. The paper explains the rationale behind the proposed approach and details the resource discovery method that underpins it. Describes and evaluates the proposed system's performance by utilizing a hybrid of the artificial bee colony (ABC) algorithm and the cuckoo search (CS) algorithm to allocate resources to requests. There are many issues with efficient resource allocation that cannot be solved using current methods. The existing research work identified and tackled the problem of optimizing the parameters (make span, execution time, deadline, execution cost, etc.) using optimization algorithms. However, most existing algorithms require more time to allocate resources because of the vast number of resources available in the cloud. The process also depends on the efficiency of the underlying optimization algorithm.

关键词： Cloud computing Computational modeling System performance Sociology Clustering algorithms artificial bee colony algorithm Resource management

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ABC-WRF-Based Gait Recognition for Exoskeleton Control Systems: A Multi-Source Data Fusion Approach

ABC-WRF-Based Gait Recognition for Exoskeleton Control Syste...

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Networking Systems of AI (INSAI), International Conference on

作者： Xi Yang Songbo Pan Yanbin Xu Chenguo Deng Guangdong power grid limited liability company Jiangmen power supply bureau Guangdong China School of Mechanical and Electrical Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

As the demand for exoskeleton robots continues to grow across various domains, the technology surrounding exoskeleton robots has attracted increased attention. The recognition of gait phases plays a crucial role in exoskeleton control systems. However, practical engineering applications have presented significant challenges for plantar pressurebased gait recognition technology. To address this, we have proposed a gait recognition approach that utilizes human joint angle data to mitigate the influence of environmental factors and movement patterns on plantar pressure signals. This study has designed a multi-source data fusion system for human motion sensing to capture motion attitude data across different modes of movement. Furthermore, in order to recognize gait phases, we have explored the optimization of the weighted random forest algorithm using the artificial bee colony algorithm (ABC-WRF). Subsequently, the proposed method was evaluated through tests conducted on various terrains, including flat ground, stairs, slopes, and special terrain. The results demonstrated an average gait recognition accuracy of over 90%, with a maximum accuracy of 96%. This research contributes to the advancement of exoskeleton engineering by introducing the ABC-WRF-based gait recognition technology and fostering the development of related technologies.

关键词： Accuracy Roads Exoskeletons Data integration Stairs Control systems artificial bee colony algorithm Sensor systems Sensors Gait recognition

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A Hybrid Modified ABC-PSO algorithm for Optimal Robotic Path Planner

A Hybrid Modified ABC-PSO Algorithm for Optimal Robotic Path...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Nadia I. Khalil Hadeel N. Abdullah Layth A. Hassnawi Communication Engineering Department University of Technology-Iraq Baghdad Iraq Electrical Engineering Department University of Technology-Iraq Baghdad Iraq Research and Development Department Ministry of Science and Technology Baghdad Iraq

Path planning is one of the most fundamental problems that must be dealt with before the mobile robot can navigate and explore autonomously in any environment. A good path-planning algorithm can save time and reduce mobile robot wear and capital investment. Path computing time and average path length are important factors over cost functions that reflect the algorithm's effectiveness, such as power consumption or average trip time. The artificial bee colony (ABC) represents one of the most important global search algorithms. The main problem with ABC is that it suffers from a slow convergence rate due to lousy exploitation and tends to get trapped in the local minima. This paper proposes and evaluates a new robot path-planning algorithm named Modified artificial bee colony (MABC). MABC algorithm design is based on modifying the ABC algorithm by cross-layer design between ABC and Particle Swarm Optimization (PSO) algorithms. The MABC is different from the original ABC algorithm in that it modifies the original one to use PSO's exploitation rather than its exploitation. On the other hand, the PSO algorithm has better exploitation but poor exploration characteristics. The evaluation and analysis were performed for several performance metrics and under different evaluation scenarios. It has been observed from the results that the MABC-PSO outperforms the original ABC with respect to average path length and convergence time which leads to improving the planning of the path.

关键词： Measurement Power demand Navigation artificial bee colony algorithm Path planning Planning Mobile robots

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