Advances on computational intelligence in energy : the applications of nature-inspired metaheuristic algorithms in energy /: the applications of nature-inspired metaheuristic algorithms in energy. ([2019])
- Record Type:
- Book
- Title:
- Advances on computational intelligence in energy : the applications of nature-inspired metaheuristic algorithms in energy /: the applications of nature-inspired metaheuristic algorithms in energy. ([2019])
- Main Title:
- Advances on computational intelligence in energy : the applications of nature-inspired metaheuristic algorithms in energy
- Further Information:
- Note: Tutut Herawan, Haruna Chiroma and Jemal H. Abawajy.
- Authors:
- Herawan, Tutut
Chiroma, Haruna
Abawajy, Jemal H - Contents:
- Intro; 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 5 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 5.2 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;Intro; 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 5 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 5.2 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 5.1 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 2 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 … (more)
- Publisher Details:
- Cham, Switzerland : Springer Nature
- Publication Date:
- 2019
- Copyright Date:
- 2019
- Extent:
- 1 online resource
- Subjects:
- 006.3
Computational intelligence
Metaheuristics
Electronic books
Electronic books - Languages:
- English
- ISBNs:
- 9783319698892
3319698893 - Related ISBNs:
- 9783319698885
- Notes:
- Note: Includes bibliographical references.
Note: Online resource; title from PDF title page (EBSCO, viewed July 16, 2019). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.442388
- Ingest File:
- 02_569.xml