Metaheuristics (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
410
Utgivningsdatum
2007-08-01
Upplaga
illustrated ed
Förlag
Springer-Verlag New York Inc.
Originalspråk
English
Medarbetare
Doerner, Karl F. (ed.), Gendreau, Michel (ed.), Greistorfer, Peter (ed.), Gutjahr, Walter (ed.), Hartl, Richard F. (ed.), Reimann, Marc (ed.)
Illustratör/Fotograf
92 schw-w Tabellen 66 schw-w Abb
Illustrationer
92 Tables, black and white; 66 Illustrations, black and white; XIV, 410 p. 66 illus.
Dimensioner
233 x 166 x 28 mm
Vikt
831 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9780387719191
Metaheuristics (inbunden)

Metaheuristics

Progress in Complex Systems Optimization

Inbunden Engelska, 2007-08-01
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This book's aim is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field.
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Innehållsförteckning

Scatter Search.- Experiments Using Scatter Search for the Multidemand Multidimensional Knapsack Problem.- A Scatter Search Heuristic for the Fixed-Charge Capacitated Network Design Problem.- Tabu Search.- Tabu Search-Based Metaheuristic Algorithm for Large-scale Set Covering Problems.- Log-Truck Scheduling with a Tabu Search Strategy.- Nature-inspired methods.- Solving the Capacitated Multi-Facility Weber Problem by Simulated Annealing, Threshold Accepting and Genetic Algorithms.- Reviewer Assignment for Scientific Articles using Memetic Algorithms.- GRASP and Iterative Methods.- Grasp with Path-Relinking for the Tsp.- Using a Randomised Iterative Improvement Algorithm with Composite Neighbourhood Structures for the University Course Timetabling Problem.- Dynamic and Stochastic Problems.- Variable Neighborhood Search for the Probabilistic Satisfiability Problem.- The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty.- Adaptive Control of Genetic Parameters for Dynamic Combinatorial Problems.- A Memetic Algorithm for Dynamic Location Problems.- A Study of Canonical GAs for NSOPs.- Particle Swarm Optimization and Sequential Sampling in Noisy Environments.- Distributed and Parallel Algorithms.- Embedding a Chained Lin-Kernighan Algorithm into a Distributed Algorithm.- Exploring Grid Implementations of Parallel Cooperative Metaheuristics.- Algorithm Tuning, Algorithm Design and Software Tools.- Using Experimental Design to Analyze Stochastic Local Search Algorithms for Multiobjective Problems.- Distance Measures and Fitness-Distance Analysis for the Capacitated Vehicle Routing Problem.- Tuning Tabu Search Strategies Via Visual Diagnosis.- Solving Vehicle Routing Using IOPT.