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Evolutionary algorithms for mobile ad hoc networks / / Bernabe Dorronsoro, University of Luxembourg, Patricia Ruiz, University of Luxembourg, Gregoire Danoy, University of Luxembourg, Yoann Pigne, University of Le Havre, Pascal Bouvry, University of Luxembourg
| Evolutionary algorithms for mobile ad hoc networks / / Bernabe Dorronsoro, University of Luxembourg, Patricia Ruiz, University of Luxembourg, Gregoire Danoy, University of Luxembourg, Yoann Pigne, University of Le Havre, Pascal Bouvry, University of Luxembourg |
| Autore | Dorronsoro Bernabé |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Computer society, IEEE, Wiley, , [2014] |
| Descrizione fisica | 1 online resource (238 p.) |
| Disciplina | 621.382/1201519625 |
| Collana | Nature-inspired computing series |
| Soggetto topico |
Mobile communication systems
Evolutionary computation Genetic algorithms |
| ISBN |
1-118-83202-7
1-118-83320-1 1-118-83201-9 |
| Classificazione | COM051300 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xiii -- PART I BASIC CONCEPTS AND LITERATURE REVIEW 1 -- 1 INTRODUCTION TO MOBILE AD HOC NETWORKS 3 -- 1.1 Mobile Ad Hoc Networks 6 -- 1.2 Vehicular Ad Hoc Networks 9 -- 1.2.1 Wireless Access in Vehicular Environment (WAVE) 11 -- 1.2.2 Communication Access for Land Mobiles (CALM) 12 -- 1.2.3 C2C Network 13 -- 1.3 Sensor Networks 14 -- 1.3.1 IEEE 1451 17 -- 1.3.2 IEEE 802.15.4 17 -- 1.3.3 ZigBee 18 -- 1.3.4 6LoWPAN 19 -- 1.3.5 Bluetooth 19 -- 1.3.6 Wireless Industrial Automation System 20 -- 1.4 Conclusion 20 -- References 21 -- 2 INTRODUCTION TO EVOLUTIONARY ALGORITHMS 27 -- 2.1 Optimization Basics 28 -- 2.2 Evolutionary Algorithms 29 -- 2.3 Basic Components of Evolutionary Algorithms 32 -- 2.3.1 Representation 32 -- 2.3.2 Fitness Function 32 -- 2.3.3 Selection 32 -- 2.3.4 Crossover 33 -- 2.3.5 Mutation 34 -- 2.3.6 Replacement 35 -- 2.3.7 Elitism 35 -- 2.3.8 Stopping Criteria 35 -- 2.4 Panmictic Evolutionary Algorithms 36 -- 2.4.1 Generational EA 36 -- 2.4.2 Steady-State EA 36 -- 2.5 Evolutionary Algorithms with Structured Populations 36 -- 2.5.1 Cellular EAs 37 -- 2.5.2 Cooperative Coevolutionary EAs 38 -- 2.6 Multi-Objective Evolutionary Algorithms 39 -- 2.6.1 Basic Concepts in Multi-Objective Optimization 40 -- 2.6.2 Hierarchical Multi-Objective Problem Optimization 42 -- 2.6.3 Simultaneous Multi-Objective Problem Optimization 43 -- 2.7 Conclusion 44 -- References 45 -- 3 SURVEY ON OPTIMIZATION PROBLEMS FOR MOBILE AD HOC NETWORKS 49 -- 3.1 Taxonomy of the Optimization Process 51 -- 3.1.1 Online and Offline Techniques 51 -- 3.1.2 Using Global or Local Knowledge 52 -- 3.1.3 Centralized and Decentralized Systems 52 -- 3.2 State of the Art 53 -- 3.2.1 Topology Management 53 -- 3.2.2 Broadcasting Algorithms 58 -- 3.2.3 Routing Protocols 59 -- 3.2.4 Clustering Approaches 63 -- 3.2.5 Protocol Optimization 64 -- 3.2.6 Modeling the Mobility of Nodes 65 -- 3.2.7 Selfish Behaviors 66 -- 3.2.8 Security Issues 67 -- 3.2.9 Other Applications 67 -- 3.3 Conclusion 68 -- References 69.
4 MOBILE NETWORKS SIMULATION 79 -- 4.1 Signal Propagation Modeling 80 -- 4.1.1 Physical Phenomena 81 -- 4.1.2 Signal Propagation Models 85 -- 4.2 State of the Art of Network Simulators 89 -- 4.2.1 Simulators 89 -- 4.2.2 Analysis 92 -- 4.3 Mobility Simulation 93 -- 4.3.1 Mobility Models 93 -- 4.3.2 State of the Art of Mobility Simulators 96 -- 4.4 Conclusion 98 -- References 98 -- PART II PROBLEMS OPTIMIZATION 105 -- 5 PROPOSED OPTIMIZATION FRAMEWORK 107 -- 5.1 Architecture 108 -- 5.2 Optimization Algorithms 110 -- 5.2.1 Single-Objective Algorithms 110 -- 5.2.2 Multi-Objective Algorithms 115 -- 5.3 Simulators 121 -- 5.3.1 Network Simulator: ns-3 121 -- 5.3.2 Mobility Simulator: SUMO 123 -- 5.3.3 Graph-Based Simulations 126 -- 5.4 Experimental Setup 127 -- 5.5 Conclusion 131 -- References 131 -- 6 BROADCASTING PROTOCOL 135 -- 6.1 The Problem 136 -- 6.1.1 DFCN Protocol 136 -- 6.1.2 Optimization Problem Definition 138 -- 6.2 Experiments 140 -- 6.2.1 Algorithm Configurations 140 -- 6.2.2 Comparison of the Performance of the Algorithms 141 -- 6.3 Analysis of Results 142 -- 6.3.1 Building a Representative Subset of Best Solutions 143 -- 6.3.2 Interpretation of the Results 145 -- 6.3.3 Selected Improved DFCN Configurations 148 -- 6.4 Conclusion 150 -- References 151 -- 7 ENERGY MANAGEMENT 153 -- 7.1 The Problem 154 -- 7.1.1 AEDB Protocol 154 -- 7.1.2 Optimization Problem Definition 156 -- 7.2 Experiments 159 -- 7.2.1 Algorithm Configurations 159 -- 7.2.2 Comparison of the Performance of the Algorithms 160 -- 7.3 Analysis of Results 161 -- 7.4 Selecting Solutions from the Pareto Front 164 -- 7.4.1 Performance of the Selected Solutions 167 -- 7.5 Conclusion 170 -- References 171 -- 8 NETWORK TOPOLOGY 173 -- 8.1 The Problem 175 -- 8.1.1 Injection Networks 175 -- 8.1.2 Optimization Problem Definition 176 -- 8.2 Heuristics 178 -- 8.2.1 Centralized 178 -- 8.2.2 Distributed 179 -- 8.3 Experiments 180 -- 8.3.1 Algorithm Configurations 180 -- 8.3.2 Comparison of the Performance of the Algorithms 180. 8.4 Analysis of Results 183 -- 8.4.1 Analysis of the Objective Values 183 -- 8.4.2 Comparison with Heuristics 185 -- 8.5 Conclusion 187 -- References 188 -- 9 REALISTIC VEHICULAR MOBILITY 191 -- 9.1 The Problem 192 -- 9.1.1 Vehicular Mobility Model 192 -- 9.1.2 Optimization Problem Definition 196 -- 9.2 Experiments 199 -- 9.2.1 Algorithms Configuration 199 -- 9.2.2 Comparison of the Performance of the Algorithms 200 -- 9.3 Analysis of Results 202 -- 9.3.1 Analysis of the Decision Variables 202 -- 9.3.2 Analysis of the Objective Values 204 -- 9.4 Conclusion 206 -- References 206 -- 10 SUMMARY AND DISCUSSION 209 -- 10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks 211 -- 10.2 Performance of the Three Algorithmic Proposals 213 -- 10.2.1 Broadcasting Protocol 213 -- 10.2.2 Energy-Efficient Communications 214 -- 10.2.3 Network Connectivity 214 -- 10.2.4 Vehicular Mobility 215 -- 10.3 Global Discussion on the Performance of the Algorithms 215 -- 10.3.1 Single-Objective Case 216 -- 10.3.2 Multi-Objective Case 217 -- 10.4 Conclusion 218 -- References 218 -- INDEX 221. |
| Record Nr. | UNINA-9910139130603321 |
Dorronsoro Bernabé
|
||
| Hoboken, New Jersey : , : Computer society, IEEE, Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Evolutionary algorithms for mobile ad hoc networks / / Bernabe Dorronsoro, University of Luxembourg, Patricia Ruiz, University of Luxembourg, Gregoire Danoy, University of Luxembourg, Yoann Pigne, University of Le Havre, Pascal Bouvry, University of Luxembourg
| Evolutionary algorithms for mobile ad hoc networks / / Bernabe Dorronsoro, University of Luxembourg, Patricia Ruiz, University of Luxembourg, Gregoire Danoy, University of Luxembourg, Yoann Pigne, University of Le Havre, Pascal Bouvry, University of Luxembourg |
| Autore | Dorronsoro Bernabé |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Computer society, IEEE, Wiley, , [2014] |
| Descrizione fisica | 1 online resource (238 p.) |
| Disciplina | 621.382/1201519625 |
| Collana | Nature-inspired computing series |
| Soggetto topico |
Mobile communication systems
Evolutionary computation Genetic algorithms |
| ISBN |
1-118-83202-7
1-118-83320-1 1-118-83201-9 |
| Classificazione | COM051300 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xiii -- PART I BASIC CONCEPTS AND LITERATURE REVIEW 1 -- 1 INTRODUCTION TO MOBILE AD HOC NETWORKS 3 -- 1.1 Mobile Ad Hoc Networks 6 -- 1.2 Vehicular Ad Hoc Networks 9 -- 1.2.1 Wireless Access in Vehicular Environment (WAVE) 11 -- 1.2.2 Communication Access for Land Mobiles (CALM) 12 -- 1.2.3 C2C Network 13 -- 1.3 Sensor Networks 14 -- 1.3.1 IEEE 1451 17 -- 1.3.2 IEEE 802.15.4 17 -- 1.3.3 ZigBee 18 -- 1.3.4 6LoWPAN 19 -- 1.3.5 Bluetooth 19 -- 1.3.6 Wireless Industrial Automation System 20 -- 1.4 Conclusion 20 -- References 21 -- 2 INTRODUCTION TO EVOLUTIONARY ALGORITHMS 27 -- 2.1 Optimization Basics 28 -- 2.2 Evolutionary Algorithms 29 -- 2.3 Basic Components of Evolutionary Algorithms 32 -- 2.3.1 Representation 32 -- 2.3.2 Fitness Function 32 -- 2.3.3 Selection 32 -- 2.3.4 Crossover 33 -- 2.3.5 Mutation 34 -- 2.3.6 Replacement 35 -- 2.3.7 Elitism 35 -- 2.3.8 Stopping Criteria 35 -- 2.4 Panmictic Evolutionary Algorithms 36 -- 2.4.1 Generational EA 36 -- 2.4.2 Steady-State EA 36 -- 2.5 Evolutionary Algorithms with Structured Populations 36 -- 2.5.1 Cellular EAs 37 -- 2.5.2 Cooperative Coevolutionary EAs 38 -- 2.6 Multi-Objective Evolutionary Algorithms 39 -- 2.6.1 Basic Concepts in Multi-Objective Optimization 40 -- 2.6.2 Hierarchical Multi-Objective Problem Optimization 42 -- 2.6.3 Simultaneous Multi-Objective Problem Optimization 43 -- 2.7 Conclusion 44 -- References 45 -- 3 SURVEY ON OPTIMIZATION PROBLEMS FOR MOBILE AD HOC NETWORKS 49 -- 3.1 Taxonomy of the Optimization Process 51 -- 3.1.1 Online and Offline Techniques 51 -- 3.1.2 Using Global or Local Knowledge 52 -- 3.1.3 Centralized and Decentralized Systems 52 -- 3.2 State of the Art 53 -- 3.2.1 Topology Management 53 -- 3.2.2 Broadcasting Algorithms 58 -- 3.2.3 Routing Protocols 59 -- 3.2.4 Clustering Approaches 63 -- 3.2.5 Protocol Optimization 64 -- 3.2.6 Modeling the Mobility of Nodes 65 -- 3.2.7 Selfish Behaviors 66 -- 3.2.8 Security Issues 67 -- 3.2.9 Other Applications 67 -- 3.3 Conclusion 68 -- References 69.
4 MOBILE NETWORKS SIMULATION 79 -- 4.1 Signal Propagation Modeling 80 -- 4.1.1 Physical Phenomena 81 -- 4.1.2 Signal Propagation Models 85 -- 4.2 State of the Art of Network Simulators 89 -- 4.2.1 Simulators 89 -- 4.2.2 Analysis 92 -- 4.3 Mobility Simulation 93 -- 4.3.1 Mobility Models 93 -- 4.3.2 State of the Art of Mobility Simulators 96 -- 4.4 Conclusion 98 -- References 98 -- PART II PROBLEMS OPTIMIZATION 105 -- 5 PROPOSED OPTIMIZATION FRAMEWORK 107 -- 5.1 Architecture 108 -- 5.2 Optimization Algorithms 110 -- 5.2.1 Single-Objective Algorithms 110 -- 5.2.2 Multi-Objective Algorithms 115 -- 5.3 Simulators 121 -- 5.3.1 Network Simulator: ns-3 121 -- 5.3.2 Mobility Simulator: SUMO 123 -- 5.3.3 Graph-Based Simulations 126 -- 5.4 Experimental Setup 127 -- 5.5 Conclusion 131 -- References 131 -- 6 BROADCASTING PROTOCOL 135 -- 6.1 The Problem 136 -- 6.1.1 DFCN Protocol 136 -- 6.1.2 Optimization Problem Definition 138 -- 6.2 Experiments 140 -- 6.2.1 Algorithm Configurations 140 -- 6.2.2 Comparison of the Performance of the Algorithms 141 -- 6.3 Analysis of Results 142 -- 6.3.1 Building a Representative Subset of Best Solutions 143 -- 6.3.2 Interpretation of the Results 145 -- 6.3.3 Selected Improved DFCN Configurations 148 -- 6.4 Conclusion 150 -- References 151 -- 7 ENERGY MANAGEMENT 153 -- 7.1 The Problem 154 -- 7.1.1 AEDB Protocol 154 -- 7.1.2 Optimization Problem Definition 156 -- 7.2 Experiments 159 -- 7.2.1 Algorithm Configurations 159 -- 7.2.2 Comparison of the Performance of the Algorithms 160 -- 7.3 Analysis of Results 161 -- 7.4 Selecting Solutions from the Pareto Front 164 -- 7.4.1 Performance of the Selected Solutions 167 -- 7.5 Conclusion 170 -- References 171 -- 8 NETWORK TOPOLOGY 173 -- 8.1 The Problem 175 -- 8.1.1 Injection Networks 175 -- 8.1.2 Optimization Problem Definition 176 -- 8.2 Heuristics 178 -- 8.2.1 Centralized 178 -- 8.2.2 Distributed 179 -- 8.3 Experiments 180 -- 8.3.1 Algorithm Configurations 180 -- 8.3.2 Comparison of the Performance of the Algorithms 180. 8.4 Analysis of Results 183 -- 8.4.1 Analysis of the Objective Values 183 -- 8.4.2 Comparison with Heuristics 185 -- 8.5 Conclusion 187 -- References 188 -- 9 REALISTIC VEHICULAR MOBILITY 191 -- 9.1 The Problem 192 -- 9.1.1 Vehicular Mobility Model 192 -- 9.1.2 Optimization Problem Definition 196 -- 9.2 Experiments 199 -- 9.2.1 Algorithms Configuration 199 -- 9.2.2 Comparison of the Performance of the Algorithms 200 -- 9.3 Analysis of Results 202 -- 9.3.1 Analysis of the Decision Variables 202 -- 9.3.2 Analysis of the Objective Values 204 -- 9.4 Conclusion 206 -- References 206 -- 10 SUMMARY AND DISCUSSION 209 -- 10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks 211 -- 10.2 Performance of the Three Algorithmic Proposals 213 -- 10.2.1 Broadcasting Protocol 213 -- 10.2.2 Energy-Efficient Communications 214 -- 10.2.3 Network Connectivity 214 -- 10.2.4 Vehicular Mobility 215 -- 10.3 Global Discussion on the Performance of the Algorithms 215 -- 10.3.1 Single-Objective Case 216 -- 10.3.2 Multi-Objective Case 217 -- 10.4 Conclusion 218 -- References 218 -- INDEX 221. |
| Record Nr. | UNINA-9910828431103321 |
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Dorronsoro Bernabé
|
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| Hoboken, New Jersey : , : Computer society, IEEE, Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimization and Learning : 6th International Conference, OLA 2023, Malaga, Spain, May 3–5, 2023, Proceedings / / edited by Bernabé Dorronsoro, Francisco Chicano, Gregoire Danoy, El-Ghazali Talbi
| Optimization and Learning : 6th International Conference, OLA 2023, Malaga, Spain, May 3–5, 2023, Proceedings / / edited by Bernabé Dorronsoro, Francisco Chicano, Gregoire Danoy, El-Ghazali Talbi |
| Autore | Dorronsoro Bernabé |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (433 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
ChicanoFrancisco
DanoyGregoire TalbiEl-Ghazali |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Numerical analysis Algorithms Artificial Intelligence Computer Engineering and Networks Numerical Analysis Design and Analysis of Algorithms Optimització matemàtica Enginyeria d'ordinadors Xarxes d'ordinadors Algorismes computacionals Intel·ligència artificial |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031340208
3031340205 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Advanced Optimization -- A comparative study of fractal-based decomposition optimization -- Diagonal Barzilai-Borwein rules in stochastic gradient-like methods -- Algorithm Selection for Large-Scale Multi-objective Optimization -- Solving a Multi-Objective Jop Shop Scheduling Problem With An Automatically Configured Evolutionary Algorithm -- Solving the Nurse Scheduling Problem using the Whale Optimization Algorithm -- A hierarchical Cooperative Coevolutionary approach to solve Very Large-scale Traveling Salesman Problem -- Tornado: An Autonomous Chaotic Algorithm For High Dimensional Global Optimization Problems -- Learning -- Neural Network Information Leakage through Hidden Learning -- Mixing Data Augmentation Methods for Semantic Segmentation -- Real-time elastic partial shape matching using a neural network-based adjoint method -- We won’t get fooled again: when performance metric malfunction affects the landscape of hyperparameter optimization problems -- Condition-based maintenance optimization under large action space with deep reinforcement learning method -- Learning methods to enhance optimization tools -- An Application of Machine Learning Tools to Predict the Number of Solutions for a Minimum Cardinality Set Covering Problem -- Adaptative Local Search for a Pickup and Delivery Problem Applied to Large Parcel Distribution -- Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic -- Multi-objective optimization of adhesive bonding process in constrained and noisy settings -- Evaluating Surrogate Models for Robot Swarm Simulations -- Interactive Job Scheduling with Partially Known Personnel Availabilities -- Multi-Armed Bandit-based Metaheuristic Operator Selection: The Pendulum Algorithm binarization case -- Optimization applied to learning methods -- Binary Black Widow with Hill Climbing Algorithm for Feature Selection -- Optimization of Fuzzy C-Means with Alternating Direction Method of Multipliers -- Partial K-means with M outliers: Mathematical programs and complexity results -- An optimization approach for optimizing PRIM’s randomly generated rules using the Genetic Algorithm -- Real-world Applications -- A fast methodology to find Decisively Strong Association Rules (DSR) -- Characterization and categorization of software programs on x86 architectures -- Robot-Assisted Delivery problems and Their Exact Solutions -- Modeling and analysis of organizational network analysis graphs based on employee data -- Time Series Forecasting for Parking Occupancy: Case Study of Malaga and Birmingham Cities -- E-scooters Routes Potential: Open Data Analysis in Current Infrastructure. Malaga Case -- Automatic Generation of Subtitles for Videos of the Government of La Rioja -- Estimation of the distribution of Body Mass Index (BMI) with sparse and low-quality data. The case of the Chilean adult population -- A New Automated Customer Prioritization Method. . |
| Record Nr. | UNINA-9910728386603321 |
|
Dorronsoro Bernabé
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||