Handbook of Approximation Algorithms and Metaheuristics: Contemporary and Emerging Applications, Volume 2

Handbook of Approximation Algorithms and Metaheuristics, Second Edition reflects the tremendous growth in the field, over the past two decades. Through contributions from leading experts, this handbook provides a comprehensive introduction to the underlying theory and methodologies, as well as the various applications of approximation algorithms and metaheuristics. Volume 1 of this two-volume set deals primarily with methodologies and traditional applications. It includes restriction, relaxation, local ratio, approximation schemes, randomization, tabu search, evolutionary computation, local search, neural networks, and other metaheuristics. It also explores multi-objective optimization, reoptimization, sensitivity analysis, and stability. Traditional applications covered include: bin packing, multi-dimensional packing, Steiner trees, traveling salesperson, scheduling, and related problems. Volume 2 focuses on the contemporary and emerging applications of methodologies to problems in combinatorial optimization, computational geometry and graphs problems, as well as in large-scale and emerging application areas. It includes approximation algorithms and heuristics for clustering, networks (sensor and wireless), communication, bioinformatics search, streams, virtual communities, and more. About the Editor Teofilo F. Gonzalez is a professor emeritus of computer science at the University of California, Santa Barbara. He completed his Ph.D. in 1975 from the University of Minnesota. He taught at the University of Oklahoma, the Pennsylvania State University, and the University of Texas at Dallas, before joining the UCSB computer science faculty in 1984. He spent sabbatical leaves at the Monterrey Institute of Technology and Higher Education and Utrecht University. He is known for his highly cited pioneering research in the hardness of approximation; for his sublinear and best possible approximation algorithm for k-tMM clustering; for introducing the open-shop scheduling problem as well as algorithms for its solution that have found applications in numerous research areas; as well as for his research on problems in the areas of job scheduling, graph algorithms, computational geometry, message communication, wire routing, etc.

Table of Contents:
1. Introduction, Overview and Definitions Part I: Computational Geometry and Graph Applications 2. Approximation Schemes for Minimum-Cost k-Connectivity Problems in Geometric Graphs 3. Dilation and Detours in Geometric Networks 4. TheWell-Separated Pair Decomposition and Its Applications 5. Covering with Unit Balls 6. Minimum Edge Length Rectangular Partitions 7. Automatic Placement of Labels in Maps and Drawings 8. Complexity, Approximation Algorithms, and Heuristics for the Corridor Problems 9. Approximate Clustering 10. Maximum Planar Subgraph 11. Disjoint Paths and Unsplittable Flow 12. The k-Connected subgraph Problem 13. Node-Connectivity Survivable Network Problems 14. Optimum Communication Spanning Trees 15. Activation Network Design Problems 16. Stochastic Local Search Algorithms for the Graph Colouring Problem 17. On Solving the Maximum Disjoint Paths Problem with Ant Colony Optimization 18. Efficient Approximation Algorithms in Random Intersection Graphs 19. Approximation Algorithms for Facility Dispersion Part II: Large-Scale and Emerging Applications 20. Cost-Efficient Multicast Routing in Ad Hoc and Sensor Networks 21. Approximation Algorithm for Clustering in Ad-hoc Networks 22. Topology Control Problems for Wireless Ad hoc Networks 23. QoS Multimedia Multicast Routing 24. Overlay Networks for Peer-to-Peer Networks 25. Scheduling Data Broadcasts on Wireless Channels: Exact Solutions and Time-Optimal Solutions for Uniform Data and Heuristics for Non-Uniform Data 26. Strategies for Aggregating Time-discounted Information in Sensor Networks 27. Approximation and exact algorithms for optimally placing a limited numberof storage nodes in a wireless sensor network 28. Approximation Algorithms for the Primer Selection, PlantedMotif Search, and Related Problems 29. Dynamic and Fractional Programming based Approximation Algorithms for Sequence Alignment with Constraints 30. Approximation Algorithms for the Selection of Robust Tag SNPs 31. Large-Scale Global Placement 32. Histograms,Wavelets, Streams and Approximation 33. A GSO based Swarm Algorithm for Odor Source Localization in Turbulent Environments 34. Color Quantization 35. Digital Reputation for Virtual Communities 36. Approximation for Influence Maximization 37. Approximation and Heuristics for Community Detection