Evolutionary algorithms for mobile ad hoc networks /
Bernab�e Dorronsoro [and others].
- Hoboken : Wiley, 2014.
- 1 online resource (238 pages)
- Nature-Inspired Computing Series .
- Nature-inspired computing series. .
Includes bibliographical references.
SERIES PAGE; TITLE PAGE; COPYRIGHT PAGE; DEDICATION; PREFACE; PART I: BASIC CONCEPTS AND LITERATURE REVIEW; 1 INTRODUCTION TO MOBILE AD HOC NETWORKS; 1.1 MOBILE AD HOC NETWORKS; 1.2 VEHICULAR AD HOC NETWORKS; 1.3 SENSOR NETWORKS; 1.4 CONCLUSION; REFERENCES; 2 INTRODUCTION TO EVOLUTIONARY ALGORITHMS; 2.1 OPTIMIZATION BASICS; 2.2 EVOLUTIONARY ALGORITHMS; 2.3 BASIC COMPONENTS OF EVOLUTIONARY ALGORITHMS; 2.4 PANMICTIC EVOLUTIONARY ALGORITHMS; 2.5 EVOLUTIONARY ALGORITHMS WITH STRUCTURED POPULATIONS; 2.6 MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS; 2.7 CONCLUSION; REFERENCES. 3 SURVEY ON OPTIMIZATION PROBLEMS FOR MOBILE AD HOC NETWORKS3.1 TAXONOMY OF THE OPTIMIZATION PROCESS; 3.2 STATE OF THE ART; 3.3 CONCLUSION; REFERENCES; 4 MOBILE NETWORKS SIMULATION; 4.1 SIGNAL PROPAGATION MODELING; 4.2 STATE OF THE ART OF NETWORK SIMULATORS; 4.3 MOBILITY SIMULATION; 4.4 CONCLUSION; REFERENCES; PART II: PROBLEMS OPTIMIZATION; 5 PROPOSED OPTIMIZATION FRAMEWORK; 5.1 ARCHITECTURE; 5.2 OPTIMIZATION ALGORITHMS; 5.3 SIMULATORS; 5.4 EXPERIMENTAL SETUP; 5.5 CONCLUSION; REFERENCES; 6 BROADCASTING PROTOCOL; 6.1 THE PROBLEM; 6.2 EXPERIMENTS; 6.3 ANALYSIS OF RESULTS; 6.4 CONCLUSION. 10.3 GLOBAL DISCUSSION ON THE PERFORMANCE OF THE ALGORITHMS10.4 CONCLUSION; REFERENCES; INDEX; END USER LICENSE AGREEMENT.
This comprehensive guide describes how evolutionary algorithms (EA) may be used to identify, model, and optimize day-to-day problems that arise for researchers in optimization and mobile networking. It provides efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field. It is an ideal book for researchers and students in the field of mobile networks.