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soft computing Based Metaheuristic Algorithms for Resource Management in Edge computing Environment

Computers, Materials & Continua 2022年第9期72卷 5233-5250页

作者： Nawaf Alhebaishi Abdulrhman M.Alshareef Tawfiq Hasanin Raed Alsini Gyanendra Prasad Joshi Seongsoo Cho Doo Ill Chul Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Sejong UniversitySeoulKorea School of software Soongsil UniversitySeoul06978Korea Artificial Intelligence Education Hankuk University of Foreign StudiesDongdaemun-guSeoul02450Korea

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end *** computing is considered a method of forwarding the processing and communication resources in the cloud towards the *** of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved *** this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing *** SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource *** goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy *** problem is formulated based on the availability of VMs,task characteristics,and queue *** integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data *** experimental validation,a comprehensive set of simulations were performed using the CloudSim *** experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

关键词： Resource scheduling edge computing soft computing fitness function virtual machines

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Improved Network Validity Using Various soft computing Techniques

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Intelligent Automation & soft computing 2023年第5期36卷 1465-1477页

作者： M.Yuvaraju R.Elakkiyavendan Anna University Regional CampusCoimbatore641046Tamil NaduIndia

Nowadays,when a life span of sensor nodes are threatened by the shortage of energy available for communication,sink mobility is an excellent technique for increasing its *** communicating via a WSN,the use of nodes as a transmission method eliminates the need for a physical *** mobility in a dynamic network topology presents a problem for sensor nodes that have reserved *** the route is revised and changed to reﬂect the location of the mobile sink location,it will be inefﬁcient for delivering data *** the clustering strategy,nodes are grouped together to improve commu-nication,and the cluster head receives data from compactable *** sink receives the aggregated data from the *** cluster head is the central node in the conventional technique.A single node uses more energy than a node that is routed to a dead *** the number of people using a route shortens its *** proposed work demonstrates the effectiveness with which sensor node paths can be modiﬁed at a lower cost by utilising the virtual *** best routes are maintained mostly by sink node communication on routes based on dynamic route adjustment(VGDRA).Only speciﬁc nodes are acquired to re-align data supply to the mobile sink in accordance with new paradigms of route *** to the results,VGDRA schemes have a longer life span because of the reduced number of loops.

关键词： soft computing intelligent systems wireless networks sensor

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soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling

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Underground Space 2021年第4期6卷 353-363页

作者： W.G.Zhang H.R.Li C.Z.Wu Y.Q.Li Z.Q.Liu H.L.Liu Key Laboratory of New Technology for Construction of Cities in Mountain Area Chongqing UniversityChongqing 400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing UniversityChongqing 400045China School of Civil Engineering Chongqing UniversityChongqing 400045China Norwegian Geotechnical Institute(NGI) Sognsveien 720855 OsloNorway

Estimating surface settlement induced by excavation construction is an indispensable task in tunneling,particularly for earth pressure balance(EPB)shield *** this study,predictive models for assessing surface settlement caused by EPB tunneling were established based on extreme gradient boosting(XGBoost),artificial neural network,support vector machine,and multivariate adaptive regression *** from three tunnel construction projects in Singapore were used,with main input parameters of cover depth,advance rate,earth pressure,mean standard penetration test(SPT)value above crown level,mean tunnel SPT value,mean moisture content,mean soil elastic modulus,and grout *** performances of these soft computing models were evaluated by comparing predicted deformation with measured *** demonstrate the acceptable accuracy of the model in predicting ground settlement,while XGBoost demonstrates a slightly higher *** addition,the ensemble method of XGBoost is more computationally efficient and can be used as a reliable alternative in solving multivariate nonlinear geo-engineering problems.

关键词： EPB Surface settlement soft computing XGBoost Multivariate adaptive regression spline

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A novel strategy for automatic test data generation using soft computing technique

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Frontiers of Computer Science 2015年第3期9卷 346-363页

作者： Priyanka CHAWLA Inderveer CHANA Ajay RANA Computer Science and Engineering Department Thapar University Patiala 147004 India Amity School of Engineering Amity University Noida 201301 India

software testing is one of the most crucial and analytical aspect to assure that developed software meets pre- scribed quality standards. software development process in- vests at least 50% of the total cost in software testing process. Optimum and efficacious test data design of software is an important and challenging activity due to the nonlinear struc- ture of software. Moreover, test case type and scope deter- mines the quality of test data. To address this issue, software testing tools should employ intelligence based soft comput- ing techniques like particle swarm optimization （PSO） and genetic algorithm （GA） to generate smart and efficient test data automatically. This paper presents a hybrid PSO and GA based heuristic for automatic generation of test suites. In this paper, we described the design and implementation of the proposed strategy and evaluated our model by performing ex- periments with ten container classes from the Java standard library. We analyzed our algorithm statistically with test ad- equacy criterion as branch coverage. The performance ade- quacy criterion is taken as percentage coverage per unit time and percentage of faults detected by the generated test data. We have compared our work with the heuristic based upon GA, PSO, existing hybrid strategies based on GA and PSO and memetic algorithm. The results showed that the test case generation is efficient in our work.

关键词： software testing particle swarm optimization,genetic algorithm soft computing test data generation

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soft computing Based Procurement Planning of Time-variable Demand in Manufacturing Systems

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International Journal of Automation and computing 2007年第1期4卷 80-87页

作者： Kai Leung Yung Wai Hung Ip Ding-Wei Wang Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hung Horn Kowloon Hong Kong PRC School of Information Science and Engineering Northeastern University Shenyang 110004 PRC

Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning （ERP）. It can be described using the non-analytic mathematical programming model proposed in this paper. To solve the model we propose to use a fuzzy decision embedded genetic algorithm. The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones. Then, a fuzzy decision quantification method is used to quantify experience from planning experts. Thus, decision rules can easily be embedded in the computation of genetic operations. This approach is applied to purchase planning problem in a practical machine tool works, where satisfactory results have been achieved.

关键词： Purchase planning Enterprise Resource Planning (ERP) soft computing genetic algorithm fuzzy decision inventory control.

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Approach of hybrid soft computing for agricultural data classification

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International Journal of Agricultural and Biological Engineering 2015年第6期8卷 54-61页

作者： Shi Lei Duan Qiguo Si Haiping Qiao Hongbo Zhang Juanjuan Ma Xinming College of Information and Management Science Henan Agricultural UniversityZhengzhou 450002China Collaborative Innovation Center of Henan Grain Crops Zhengzhou 450002China Zhengzhou Commodity Exchange Zhengzhou 450008China

soft computing is an important computational paradigm,and it provides the capability of flexible information processing to solve real world *** data classification is one of the important applications of computing technologies in agriculture,and it has become a hot topic because of the enormous growth of agricultural data *** vector machine is a powerful soft computing technique and it realizes the idea of structural risk minimization principle to find a partition hyperplane that can satisfy the class *** set theory is another famous soft computing technique to deal with vague and uncertain *** learning is an effective method to learn multiple learners and combine their decisions for achieving much higher prediction *** this study,the support vector machine,rough set and ensemble learning were incorporated to construct a hybrid soft computing approach to classify the agricultural *** experimental evaluation of different methods was conducted on public agricultural *** experimental results indicated that the proposed algorithm improves the performance of classification effectively.

关键词： agricultural data soft computing rough set support vector machine ensemble learning classification

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Use of soft computing techniques for tunneling optimization of tunnel boring machines

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Underground Space 2021年第3期6卷 233-239页

作者： Isam Shahrour Wengang Zhang College of Civil Engineering Tongji UniversityShanghai 200092China Laboratoire Genie Civil et geo-Environnement Universite de LilleFrance School of Civil Engineering Chongqing UniversityChina

Thanks to advances in tunnel boring machine(TBM)and monitoring,significant progress has been achieved in the application of soft computing techniques for the optimization of TBM tunneling and the reduction of disturbance related to tunneling in urban *** experimental,analytical,and numerical methods have limitations in solving problems related to TBM tunneling,engineers can use soft computing techniques in analyzing the relationship between the target tunneling responses and influential design inputs parameters,including the geometrical,geological,and TBM operational *** techniques are useful in achieving robust and low-cost ***,engineers face difficulties in making an optimal choice of the soft computing technique to solve the complex problems related to TBM *** help with this choice,this study presents state of the art about the use of soft computing techniques in TBM tunneling through practical *** study proposes recommendations for the optimal use of these techniques,in particular(i)the importance of preliminary analyses for the selection and reduction of input parameters,(ii)the necessity to complete insufficient data using laboratory tests and numerical modeling,(iii)the selection of reduced number of hidden layers and nodes to avoid overfitting,(iv)the use of recurrent neural networks to deal with time-series data,and(v)the association of soft computing methods with hybrid optimization techniques to reduce the risk of convergence to local minima.

关键词： soft computing Tunneling Tunnel boring machine Artificial neural network Machine learning Optimization Settlement Convergence Artificial intelligence

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Application of soft computing techniques in coastal study–A review

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Journal of Ocean Engineering and Science 2016年第4期1卷 247-255页

作者： G.S.Dwarakish B.Nithyapriya National Institute of Technology Karnataka SurathkalIndia

Coastal zone is the triple interface of air,water and land and it is so dynamic in nature which requires expeditious management for its *** change in shoreline and submergence of low lying areas due to sea level rise are the solemn issues that need to be *** coastline of about 7516 km is under threat due to global warming and related human *** sensing data products provide synoptic and repetitive view of the earth in various spatial,spectral,temporal and radiometric ***,it can be used in monitoring coastal areas on a temporal *** Erosion hotspots have to be given proper protection measures to avoid further *** images serve in delineating shoreline and extracting the hotspots to plan the mitigation *** inundation maps can be created using remote sensing and geospatial technologies by assuming different sea level *** maps can serve as a base for planning management *** computing techniques like Fuzzy Logic,Artificial Neural Network,Genetic Algorithm and Support Vector Machine are upcoming soft computing algorithms that find its application in classification,regression,pattern recognition,etc.,across multi-disciplinary *** can be used in classifying remote sensing images which in turn can be used for studying the coastal *** present paper reviews the works carried out for coastal study using conventional remote sensing techniques and the pertinency of soft computing techniques for the same.

关键词： Artificial neural network Coastal inundation Remote sensing Shoreline soft computing Support vector machine

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soft computing: New Trends and Applications 1

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丛书名： Advanced Textbooks in Control and Signal Processing

2001年

作者： Professor Luigi Fortuna Doctor Gianguido Rizzotto Doctor Mario Lavorgna Professor Giuseppe Nunnari Professor M. Gabriella Xibilia Doctor Riccardo Caponetto (auth.)

来源：畅想之星图书光盘版（南... 评论

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A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture

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Information Processing in Agriculture 2021年第1期8卷 64-74页

作者： Huihui Yu Ling Yang Daoliang Li Yingyi Chen School of Information Science and Technology Beijing Forestry UniversityBeijing 100083PR China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083PR China Key Laboratory of Agricultural Information Acquisition Technology Ministry of AgricultureBeijing 100083PR China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing 100083PR China

Ammonia nitrogen is one of the key parameters in determining the aquaculture water quality condition in *** high level of ammonia nitrogen is likely to cause deterioration of water quality and mass death of cultured ***,accurate detection of the cultured water ammonia nitrogen content is crucially important for aquaculture ***,at present,the accuracy of equipment for measuring the ammonia nitrogen content of aquaculture water in real time cannot meet the requirements for *** this paper,the soft computing method is firstly proposed to predict the ammonia nitrogen content in aquaculture water in real *** method includes empirical mode decomposition(EMD),improved particle swarm optimization(IPSO)and extreme learning machine(ELM).To evaluate the performance of the soft computing techniques,three different statistic indicators were used,including root mean square error(RMSE),the mean absolute error(MAE),and the mean absolute percentage error(MAPE)to compare three artificial soft computing *** showed that the EMD-IPSO-ELM soft computing method showed the best performance among other studied methods in the ammonia nitrogen real time *** EMD-IPSO-ELM model provides moderately and roughly accurately real time prediction value of ammonia nitrogen in aquaculture water.

关键词： Ammonia nitrogen prediction Extreme learning machine soft computing Aquaculture

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