Cover of: Multi-Objective Optimization in Computer Networks Using Metaheuristics | Yezid Donoso

Multi-Objective Optimization in Computer Networks Using Metaheuristics

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AUERBACH
Computer Communications & Networking, Technology, Computers - Communications / Networking, Science/Mathematics, Interactive & Multimedia, Networking - Network Protocols, Telecommunications, Technology / Engineering / Electrical, Engineering - Electrical & Electronic, Computer networks, Mathematical optimiz
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FormatHardcover
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Open LibraryOL8261243M
ISBN 100849380847
ISBN 139780849380846

Multi-Objective Optimization in Computer Networks Using Metaheuristics - CRC Press Book 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.

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 Cited by: Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks.

Description Multi-Objective Optimization in Computer Networks Using Metaheuristics FB2

It analyzes layer 3 (IP), layer 2. 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). Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-aim disadvantage in routing laptop networks.

It analyzes layer three (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless options). Multi-Objective Optimization in Computer Networks Using Metaheuristics - Kindle edition by Donoso, Yezid, Fabregat, Ramon. Download it once and read it on your Multi-Objective Optimization in Computer Networks Using Metaheuristics book device, PC, phones or tablets.

Use features like bookmarks, note taking and highlighting while reading Multi-Objective Optimization in Computer Networks Using Metaheuristics.4/5(1). Get this from a library. Multi-objective optimization in computer networks using metaheuristics.

[Yezid Donoso; Ramon Fabregat] -- "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. Multi-Objective Optimization in Computer Networks Using Metaheuristics View larger image. By: Yezid Donoso and Synopsis 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. Get this from a library. Multi-objective optimization in computer networks using metaheuristics.

Details Multi-Objective Optimization in Computer Networks Using Metaheuristics FB2

[Yezid Donoso; Ramon Fabregat] -- 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.

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 by: Multi-Objective Optimization in Computer Networks Using Metaheuristics Yezid Donoso Ramon Fabregat A Auerbach Publications Taylor & Francis Group Boca Raton New York Auerbach Publications is an imprint of the Taylor & Francis Group, an informa business.

Entdecken Sie "Multi-Objective Optimization in Computer Networks Using Metaheuristics" von Yezid Donoso und finden Sie Ihren Buchhändler. 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. resources needed to function well. Using metaheuristics, 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). Book Info: Published in Author Yezid Donoso. Multi-objective optimization using metaheuristics: non-standard algorithms El-Ghazali Talbia,b, Matthieu Basseurc, Antonio J.

Nebrod and Enrique Albad aINRIA-University of Lille, Bat.M3Villeneuve d’Ascq, France bKing Saud University, Riyadh, Saudi Arabia cLaboratoire d’Etudes et de Recherche en Informatique d’Angers, 2 bd Lavoisier, Angers, France.

Multiobjective Optimization. Multiobjective optimization can be defined as determining a vector of design variables that are within the feasible region to minimize (maximize) a vector of objective functions and can be mathematically expressed as follows(1)MinimizeF(x)={f1(x),f2(x),fm(x)}Subject tog(x)≤0where x is the vector of design.

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Multi-Objective Optimization in Computer Networks Using Metaheuristics Yezid Donoso, Ramon Fabregat Limited preview - AgriScientia, Volumes Snippet view - All Book Search results » Bibliographic information.

Title: Diseño y análisis de experimentos: Author: Douglas C. Montgomery: Edition: 2: Publisher: Limusa Wiley /5(3). INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C), ISSN IJCCC was founded inat Agora University, by Ioan DZITAC (Editor-in-Chief), Florin Gheorghe FILIP (Editor-in-Chief), and Misu-Jan MANOLESCU (Managing Editor).Cited by: 1.

Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems.

AbstractThis paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms.

Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements Cited by:   Simple answer: when deterministic methods don’t work well.

First, the bad: Metaheuristic methods (particle swarm, genetic algorithms, etc.) are rarely more efficient than gradient based methods when an explicit equation based model exists.

These m. Evolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization. Author Bios. BERNABÉ DORRONSORO, PHD, earned his PhD in computer science from the University of Málaga (Spain) in His main research.

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This book is intended for researchers, teachers, engineers, managers, and practitioners seeking research on algorithms to enhance the order picking performance.

Increased concentration after reading the book Handbook of Research on Metaheuristics for Order Picking Optimization in Warehouses to Smart Cities. In our crazy Internet world. This book addresses computationally-efficient multi-objective optimization of antenna structures using variable-fidelity electromagnetic simulations, surrogate modeling techniques, and design space reduction methods.

Based on contemporary research, it formulates multi-objective design tasks. This chapter provides a short overview of multi-objective optimization using metaheuristics. The chapter includes a description of some of.

Optimization Using Evolutionary Algorithms and Metaheuristics: Applications in Engineering - CRC Press Book Metaheuristic optimization is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with.

Book Description. Metaheuristic optimization is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.

You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read.

Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them. Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume ) Log in to check access. Metaheuristics Multicriteria Optimization Multiobjective Operations Research Scheduling algorithms genetic algorithms metaheuristic multi-objective optimization optimization.

Editors and affiliations. Xavier Gandibleux. Kim S and Jeong I Interactive Multi-Objective Optimization Using Mobile Application Proceedings of the Asia Pacific Information Technology Conference, () Computer Networks: The International Journal of Computer and Telecommunications Nebro A and Alba E Using multi-objective metaheuristics to solve the software project.

The purpose of this book is to collect contributions that deal with the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems. Such a collection intends to provide an overview of the state-of-the-art developments in this field, with the aim of motivating more researchers in operations research.In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.Multi-objective inventory routing with uncertain demand using population-based metaheuristics State-of-the-art evolutionary multi-objective optimization algorithms and a new method based on swarm intelligence are used to compute approximation of the 3-D Pareto front.

and Schmeck H., Multi-objective particle swarm optimization on Cited by: