Metaheuristics: Progress in Complex Systems Optimization

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Karl F. Doerner, Michel Gendreau, Peter Greistorfer, Walter Gutjahr, Richard F. Hartl, Marc Reimann
Springer Science & Business Media, Aug 13, 2007 - Mathematics - 410 pages
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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.

 

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Contents

EXPERIMENTS USING SCATTER SEARCH FOR THE MULTIDEMAND MULTIDIMENSIONAL KNAPSACK PROBLEM
3
A SCATTER SEARCH HEURISTIC FOR THE FIXEDCHARGE CAPACITATED NETWORK DESIGN PROBLEM
25
Tabu Search
41
TABU SEARCHBASED METAHEURISTIC ALGORITHM FOR LARGESCALE SET COVERING PROBLEMS
43
LOGTRUCK SCHEDULING WITH A TABU SEARCH STRATEGY
64
Natureinspired methods
89
SOLVING THE CAPACITATED MULTIFACILITY WEBER PROBLEM BY SIMULATED ANNEALING THRESHOLD ACCEPTING AND GENE...
91
REVIEWER ASSIGNMENT FOR SCIENTIFIC ARTICLES USING MEMETIC ALGORITHMS
113
ADAPTIVE CONTROL OF GENETIC PARAMETERS FOR DYNAMIC COMBINATORIAL PROBLEMS
205
A MEMETIC ALGORITHM FOR DYNAMIC LOCATION PROBLEMS
224
A STUDY OF CANONICAL GAs FOR NSOPs Panmictic versus Decentralized Genetic Algorithms for NonStationary Problems
245
PARTICLE SWARM OPTIMIZATION AND SEQUENTIAL SAMPLING IN NOISY ENVIRONMENTS
261
Distributed and Parallel Algorithms
274
EMBEDDING A CHAINED LINKERNIGHAN ALGORITHM INTO A DISTRIBUTED ALGORITHM
275
EXPLORING GRID IMPLEMENTATIONS OF PARALLEL COOPERATIVE METAHEURISTICS A Case Study for the Mirrored Traveling Tourna...
297
Algorithm Tuning Algorithm Design and Software Tools
323

GRASP and Iterative Methods
135
GRASP WITH PATHRELINKING FOR THE TSP
136
USING A RANDOMISED ITERATIVE IMPROVEMENT ALGORITHM WITH COMPOSITE NEIGHBOURHOOD STRUCTURES FOR THE UNIV...
153
Dynamic and Stochastic Problems
170
VARIABLE NEIGHBORHOOD SEARCH FOR THE PROBABILISTIC SATISFIABILITY PROBLEM
171
THE ACOFRACE ALGORITHM FOR COMBINATORIAL OPTIMIZATION UNDER UNCERTAINTY
189
USING EXPERIMENTAL DESIGN TO ANALYZE STOCHASTIC LOCAL SEARCH ALGORITHMS FOR MULTIOBJECTIVE PROBLEMS
324
DISTANCE MEASURES AND FITNESSDISTANCE ANALYSIS FOR THE CAPACITATED VEHICLE ROUTING PROBLEM
345
TUNING TABU SEARCH STRATEGIES VIA VISUAL DIAGNOSIS
365
SOLVING VEHICLE ROUTING USING IOPT
389
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