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Training Multi-Layer Perceptron with Enhanced brain storm optimization Metaheuristics

Computers, Materials & Continua 2022年第2期70卷 4199-4215页

作者： Nebojsa Bacanin Khaled Alhazmi Miodrag Zivkovic K.Venkatachalam Timea Bezdan Jamel Nebhen Singidunum University Danijelova11000BelgradeSerbia National Center for Robotics and IoT Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh12371Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia

In the domain of artificial neural networks,the learning process represents one of the most challenging *** the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at ***,to a very large search space,it is very difficult to find the proper values of connection weights and *** traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local *** commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient *** an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network *** thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural *** the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization *** results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local *** proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on *** mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

关键词： Artificial neural network optimization metaheuristics algorithm hybridization brain storm optimization

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RFID network planning based on improved brain storm optimization algorithm

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 30-39页

作者： Lin Zihan Zheng Jiali Xie Xiaode Feng Minyu He Siyi School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In order to improve the service quality of radio frequency identification(RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization(BSO) algorithm, was proposed to solve the problem of RFID network planning(RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm’s tag coverage was increased by 9.62% over the Cuckoo search(CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.

关键词： radio frequency identification(RFID) RFID network planning(RNP) brain storm optimization golden sine operator

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brain storm optimization Based Clustering for Learning Behavior Analysis

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Computer Systems Science & Engineering 2021年第11期39卷 211-219页

作者： Yu Xue Jiafeng Qin Shoubao Su Adam Slowik School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China Department of Electronics and Computer Science Koszalin University of Technology75-453KoszalinPoland

Recently,online learning platforms have proven to help people gain knowledge more *** the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its *** platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study *** such,an intelligent analysis method is needed to process efficiently this high volume of *** is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning *** study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their *** enables teaching to be tailored to taken into account those results,thereby,improving the education quality over ***,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and *** experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.

关键词： Online learning learning behavior analysis big data brain storm optimization cluster

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A Multi-Objective Scheduling and Routing Problem for Home Health Care Services via brain storm optimization

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Complex System Modeling and Simulation 2023年第1期3卷 32-46页

作者： Xiaomeng Ma Yaping Fu Kaizhou Gao Lihua Zhu Ali Sadollah School of Business Qingdao UniversityQingdao 266071China the Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China the College of Public Health Shanghai University of Medicine and Health SciencesShanghai 201318China Department of Mechanical Engineering University of Science and CultureTehran146899-5513Iran

At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the *** HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and industrial *** work proposes an HHCSRP considering several care centers,where a group of customers(i.e.,patients and the elderly)require being assigned to care ***,various kinds of services are provided by caregivers for customers in different *** considering the skill matching,customers’appointment time,and caregivers’workload balancing,this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay *** handle it,we then introduce a brain storm optimization method with particular multi-objective search mechanisms(MOBSO)via combining with the features of the investigated ***,we perform experiments to test the effectiveness of the designed *** comparing the MOBSO with two excellent optimizers,the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.

关键词： home health care multi-center service multi-objective optimization scheduling and routing problems brain storm optimization

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OBSO Based Fractional PID for MPPT-Pitch Control of Wind Turbine Systems

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Computers, Materials & Continua 2022年第5期71卷 4001-4017页

作者： Ibrahim M.Mehedi Ubaid M.Al-Saggaf Mahendiran T.Vellingiri Ahmad H.Milyani Nordin Bin Saad Nor Zaihar Bin Yahaya Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical&Electronics Engineering Universiti Teknologi PETRONASMalaysia

In recent times,wind energy receives maximum attention and has become a significant green energy source *** wind turbine(WT)entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and *** turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power *** pitch control angle is employed to effectively operate the WT at the above nominal wind ***,the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep WTs in a safe operating *** achieve this,proportional–integral–derivative(PID)controllers are widely used and the choice of optimal parameters in the PID controllers needs to be properly *** this motivation,this paper designs an oppositional brain storm optimization(OBSO)based fractional order PID(FOPID)design for sustainable and secure energy in WT *** proposed model aims to effectually extract the maximum power point(MPPT)in the low range of weather conditions and save the WT in high wind regions by the use of pitch *** OBSO algorithm is derived from the integration of oppositional based learning(OBL)concept with the traditional BSO algorithm in order to improve the convergence rate,which is then applied to effectively choose the parameters involved in the FOPID *** performance of the presented model is validated on the pitch control of a 5 MW WT and the results are examined under different *** simulation outcomes ensured the promising characteristics of the proposed model over the other methods.

关键词： Wind turbine wind energy pitch control brain storm optimization PID controller maximum power point

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