Metaheuristics

by Doerner, Karl F.; Gendreau, Michel; Greistorfer, Peter; Gutjahr, Walter J.; Hartl, Richard F.

• ISBN13: 9780387719191
• ISBN10: 0387719199
• Format: Hardcover
• Copyright: 2007-08-10
• Publisher: Springer Verlag

Summary

Metaheuristics has grown and continues to grow steadily. Seen both from the technical point of view and from the application-oriented side, these optimization tools have established their value in a remarkable story of success. Researchers have demonstrated the ability of these methods to solve hard combinatorial problems of practical sizes within reasonable computational time. Highlighted in METAHEURISTICS: Progress in Complex Systems Optimization are the recent developments made in the area of Simulated Annealing, Path Relinking, Scatter Search, Tabu Search, Variable Neighborhood Search, Hyper-heuristics, Constraint Programming, Iterated Local Search, GRASP, bio-inspired algorithms like Genetic Algorithms, Memetic Algorithms, Ant Colony Optimization or Swarm Intelligence, and several other paradigms. In addition, a series of tutorials on developing areas in Metaheuristics are presented in the volume. Giving these tutorials are some of the top researchers in Metaheuristics: Edmund Burke, Reuven Rubinstein, Eric Taillard, Gilles Pesant, Pierre Hansen, and Stefan Voß. Applications addressed are anticipated to include production planning, machine and project scheduling, the traveling salesman and vehicle routing, packing, knapsack and location problems with layout design, portfolio selection, network-design, health care, energy and environmental planning, data mining, pattern classification and biotechnology, among others. The aim of this book 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.

Table of Contents

Experiments using scatter search for the multidemand multidimensional knapsack problem	p. 3
A scatter search heuristic for the fixed-charge multicommodity flow network design problem	p. 25
Tabu search-based metaheuristic algorithm for large-scale set covering problems	p. 43
Log-truck scheduling with a tabu search strategy	p. 65
Solving the capacitated multi-facility weber problem by simulated annealing, threshold accepting and genetic algorithms	p. 91
A memetic algorithms for the reviewer assignment problem	p. 113
GRASP with path-relinking for the TSP	p. 137
Using a randomised iterative improvement algorithm with, composite neighbourhood structures for the university course timetabling problem	p. 153
Variable neighborhood search for the probabilistic satisfiability problem	p. 173
The ACO/F-RACE algorithm for combinatorial optimization under uncertainty	p. 189
Adaptive control of genetic parameters for dynamic combinatorial problems	p. 205
A memetic algorithm for dynamic location problems	p. 225
Panmictic versus decentralized genetic algorithms for non-stationary problems	p. 245
Particle swarm optimization and sequential sampling in noisy environments	p. 261
Embedding a chained Lin-Kernighan algorithm into a distributed algorithm	p. 277
Exploring grid implementations of parallel cooperative metaheuristics	p. 297
Using experimental design to analyze stochastic local search algorithms for multiobjective problems	p. 325
Distance measures and fitness-distance analysis for the capacitated vehicle routing problem	p. 345
Tuning TABU search strategies via visual diagnosis	p. 365
Solving vehicle routing using iOpt	p. 389
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