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000898833 020__ $$a9783319698892$$q(electronic book)
000898833 020__ $$a3319698893$$q(electronic book)
000898833 020__ $$z9783319698885
000898833 035__ $$aSP(OCoLC)on1108619455
000898833 035__ $$aSP(OCoLC)1108619455
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000898833 049__ $$aISEA
000898833 050_4 $$aQ342
000898833 08204 $$a006.3$$223
000898833 1001_ $$aHerawan, Tutut,$$eauthor.
000898833 24510 $$aAdvances on computational intelligence in energy :$$bthe applications of nature-inspired metaheuristic algorithms in energy /$$cTutut Herawan, Haruna Chiroma and Jemal H. Abawajy.
000898833 264_1 $$aCham, Switzerland :$$bSpringer Nature,$$c[2019]
000898833 264_4 $$c©2019
000898833 300__ $$a1 online resource.
000898833 336__ $$atext$$btxt$$2rdacontent
000898833 337__ $$acomputer$$bc$$2rdamedia
000898833 338__ $$aonline resource$$bcr$$2rdacarrier
000898833 4901_ $$aGreen energy and technology
000898833 504__ $$aIncludes bibliographical references.
000898833 5050_ $$aIntro; Preface; Reviewers; GET Authors; Contents; A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption; 1 Introduction; 2 Computational Intelligent Algorithms; 2.1 Characteristics of Computational Intelligent Algorithms; 3 Big Data Analytics and Energy Consumption by Cluster Computing Systems; 3.1 Big Data Analytics Platforms; 3.2 Energy Consumption Over Big Data Platforms; 3.3 Metrics Used for Measuring Power in Big Data Platforms; 4 Computational Intelligent Algorithms and Big Data Analytics
000898833 5058_ $$a5 Energy Consumption in the Application of Computational Intelligent Algorithms in Big Data Analytics6 A Proposed Framework for Big Data Analytics Using Computational Intelligent Algorithms; 7 Conclusions; References; Artificial Bee Colony for Minimizing the Energy Consumption in Mobile Ad Hoc Network; 1 Introduction; 2 Energy-Aware Routing Protocol; 3 Routing Protocols in MANET; 3.1 Destination-Sequenced Distance-Vector Routing; 3.2 Ad Hoc On-Demand Distance-Vector Routing Protocol; 4 Artificial Bee Colony for AODV and DSDV; 5 Experimental Results; 5.1 Simulation Settings
000898833 5058_ $$a5.2 Performance Metrics5.3 Simulation Results and Performance Comparison; 6 Conclusion; References; A Novel Chicken Swarm Neural Network Model for Crude Oil Price Prediction; 1 Introduction; 2 Artificial Neural Network; 3 Chicken Swarm Optimization; 4 The Proposed Chicken S-NN Algorithm; 5 Results & Discussion; 5.1 Preliminaries; 5.2 Data; 5.3 Discussion; 6 Conclusion; References; Forecasting OPEC Electricity Generation Based on Elman Network Trained by Cuckoo Search Algorithm; 1 Introduction; 2 Elman Network; 3 Cuckoo Search; 4 The Proposed CS Elman Algorithm; 5 Results and Discussion
000898833 5058_ $$a5.1 Discussion6 Conclusions; References; Variable Neighborhood Search-Based Symbiotic Organisms Search Algorithm for Energy-Efficient Scheduling of Virtual Machine in Cloud Data Center; 1 Introduction; 2 Related Works; 3 Energy-Efficient Virtual Machine Scheduling Optimization; 3.1 Problem Definition; 3.2 Basic Concepts of Symbiotic Organisms Search; 4 Performance Evaluation; 4.1 Experimental Setup; 4.2 Results and Discussion; 5 Conclusion and Future Work; References; Energy Savings in Heterogeneous Networks with Self-Organizing Backhauling; 1 Introduction
000898833 5058_ $$a2 Base Station Types in HETNET and Power System Consideration2.1 Base Station Types in HetNet; 2.2 Power System Consideration of BS Sites; 3 Small Cells Deployment and Backhauling Options; 3.1 Wired Backhaul Options for Small Cells; 3.2 Wireless Backhaul Options; 4 System Concept; 5 Backhaul-Energy Model; 6 Results and Discussions; 6.1 Typical Power Consumption of Macro BS and Microwave Backhaul Hub Sites; 6.2 Power Consumption of HetNet and the Break-Even Load; 6.3 Impact of Macro Base Station Load on Power Consumption; 6.4 Energy Savings of Self-Backhauling; 7 Conclusions; References
000898833 506__ $$aAccess limited to authorized users.
000898833 520__ $$aAddressing the applications of computational intelligence algorithms in energy, this book presents a systematic procedure that illustrates the practical steps required for applying bio-inspired, meta-heuristic algorithms in energy, such as the prediction of oil consumption and other energy products. Contributions include research findings, projects, surveying work and industrial experiences that describe significant advances in the applications of computational intelligence algorithms in energy. For easy understanding, the text provides practical simulation results, convergence and learning curves as well as illustrations and tables. Providing a valuable resource for undergraduate and postgraduate students alike, it is also intended for researchers in the fields of computational intelligence and energy.
000898833 588__ $$aOnline resource; title from PDF title page (viewed July 16, 2019).
000898833 650_0 $$aComputational intelligence.
000898833 650_0 $$aMetaheuristics.
000898833 7001_ $$aChiroma, Haruna,$$eauthor.
000898833 7001_ $$aAbawajy, Jemal H.,$$eauthor.
000898833 830_0 $$aGreen energy and technology.
000898833 852__ $$bebk
000898833 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-69889-2$$zOnline Access$$91397441.1
000898833 909CO $$ooai:library.usi.edu:898833$$pGLOBAL_SET
000898833 980__ $$aEBOOK
000898833 980__ $$aBIB
000898833 982__ $$aEbook
000898833 983__ $$aOnline
000898833 994__ $$a92$$bISE