|
" Multi-objective optimization in computer networks using metaheuristics / "
Yezid Donoso, Ramon Fabregat.
| Document Type
|
:
|
BL
|
| Record Number
|
:
|
1035275
|
| Doc. No
|
:
|
b789645
|
| Main Entry
|
:
|
Donoso, Yezid.
|
| Title & Author
|
:
|
Multi-objective optimization in computer networks using metaheuristics /\ Yezid Donoso, Ramon Fabregat.
|
| Publication Statement
|
:
|
Boca Raton :: Auerbach Publications,, ©2007.
|
| Page. NO
|
:
|
1 online resource (xvi, 449 pages) :: illustrations
|
| ISBN
|
:
|
1420013629
|
|
|
:
|
: 1466526521
|
|
|
:
|
: 9781420013627
|
|
|
:
|
: 9781466526525
|
|
|
:
|
0849380847
|
|
|
:
|
9780849380846
|
| Bibliographies/Indexes
|
:
|
Includes bibliographical references (pages 435-440) and index.
|
| Contents
|
:
|
Cover -- TOC36;Contents -- CH36;1 Optimization Concepts -- 146;1 Local Minimum -- 146;2 Global Minimum -- 146;3 Convex and Nonconvex Sets -- 146;4 Convex and Concave Functions -- 146;5 Minimum Search Techniques -- 146;546;1 Breadth First Search -- 146;546;2 Depth First Search -- 146;546;3 Best First Search -- CH36;2 Multi45;Objective Optimization Concepts -- 246;1 Single45;Objective versus Multi45;Objective Optimization -- 246;2 Traditional Methods -- 246;246;1 Weighted Sum -- 246;246;2 38;35;94959;45;Constraint -- 246;246;3 Distance to a Referent Objective Method -- 246;246;4 Weighted Metrics -- 246;246;5 The Benson Method -- 246;3 Metaheuristics -- 246;346;1 Convergence Toward Optimal -- 246;346;2 Optimal Solutions Not Withstanding Convexity of the Problem -- 246;346;3 Avoiding Local OptimaL -- 246;346;4 Polynomial Complexity of Metaheuristics -- 246;346;5 Evolutionary Algorithms -- 246;346;6 Ant Colony -- 246;346;7 Memetic Algorithm -- 246;346;8 Tabu Search -- 246;346;9 Simulated Annealing -- 246;4 Multi45;Objective Solution Applying Metaheuristics -- 246;446;1 Procedure to Assign Fitness to Individuals -- 246;446;2 Reducing the Nondominated Set Using Clustering -- CH36;3 Computer Network Modeling -- 346;1 Computer Networks58; Introduction -- 346;146;1 Reference Models -- 346;146;2 Classification of Computer Networks Based on Size -- 346;146;3 Classification of Computer Networks Based on Type of Transmission -- 346;2 Computer Network Modeling -- 346;246;1 Introduction to Graph Theory -- 346;246;2 Computer Network Modeling in Unicast Transmission -- 346;246;3 Computer Networks Modeling in Multicast Transmission -- CH36;4 Routing Optimization in Computer Networks -- 446;1 Concepts -- 446;146;1 Unicast Case -- 446;146;2 Multicast Case -- 446;2 Optimization Functions -- 446;246;1 Hop Count -- 446;246;2 Delay -- 446;246;3 Cost -- 446;246;4 Bandwidth Consumption -- 446;246;5 Packet Loss Rate -- 446;246;6 Blocking Probability -- 446;246;7 Maximum Link Utilization -- 446;246;8 Other Multicast Functions -- 446;3 Constraints -- 446;346;1 Unicast Transmission -- 446;346;2 Multicast Transmission -- 446;4 Functions and Constraints -- 446;446;1 Unicast Transmissions -- 446;446;2 Multicast Transmissions -- 446;5 Single45;Objective Optimization Modeling and Solution -- 446;546;1 Unicast Transmission Using Hop Count and Delay -- 446;546;2 Multicast Transmission Using Hop Count and Delay -- 446;546;3 Unicast Transmission Using Hop Count44; Delay44; and Bandwidth Consumption -- 446;546;4 Multicast Transmission Using Hop Count44; Delay44; and Bandwidth Consumption -- 446;546;5 Unicast Transmission Using Hop Count44; Delay44; Bandwidth Consumption44; and Maximum Link Utilization -- 446;546;6 Multicast Transmission Using Hop Count44; Delay44; Bandwidth Consumption44; and Maximum Link Utilization -- 446;6 Multi45;Objective Optimization Modeling -- 446;646;1 Unicast Transmission -- 446;646;2 Multicast Transmission -- 446;7 Obtaining a Solution Using Metaheuristics -- 446;746;1 Unicast for the Hop Count and Delay Functions -- 446;746;2 Multicast for the Hop Count and Delay Functions.
|
| Abstract
|
:
|
Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet - such as TV over the Internet, radio over the Internet, and multipoint video streaming - require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality.; "Multi-Objective Optimization in Computer Networks Using Metaheuristics" provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the computer networks presented within the text.; The book then focuses on the multi-objective models in computer networks, optical networks, and wireless networks and the applied way they can be solved. This resource also contains annexes that present the source code to solve the mathematical model problems present in the book through solvers and source codes programmed in C language, which solve some of the multi-objective optimization problems presented in the book.
|
| Subject
|
:
|
Computer networks.
|
| Subject
|
:
|
Mathematical optimization.
|
| Subject
|
:
|
Computer networks.
|
| Subject
|
:
|
COMPUTERS-- Data Transmission Systems-- General.
|
| Subject
|
:
|
COMPUTERS-- Networking-- Vendor Specific.
|
| Subject
|
:
|
Mathematical optimization.
|
| Dewey Classification
|
:
|
004.6
|
| LC Classification
|
:
|
TK5105.5.D665 2007
|
| Added Entry
|
:
|
Fabregat, Ramon,1963-
|
| |