Handbook of Metaheuristics

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Michel Gendreau, Jean-Yves Potvin
Springer Science & Business Media, Sep 11, 2010 - Business & Economics - 648 pages
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The rst edition of the Handbook of Metaheuristics was published in 2003 under the editorship of Fred Glover and Gary A. Kochenberger. Given the numerous - velopments observed in the eld of metaheuristics in recent years, it appeared that the time was ripe for a second edition of the Handbook. For different reasons, Fred and Gary were unable to accept Springer’s invitation to prepare this second e- tion and they suggested that we should take over the editorship responsibility of the Handbook. We are deeply honored and grateful for their trust. As stated in the rst edition, metaheuristics are “solution methods that orch- trate an interaction between local improvement procedures and higher level stra- gies to create a process capable of escaping from local optima and performing a robust search of a solution space. ” Although this broad characterization still holds today, many new and exciting developments and extensions have been observed in the last few years. We think in particular to hybrids, which take advantage of the strengths of each of their individual metaheuristic components to better explore the solution space. Hybrids of metaheuristics with other optimization techniques, like branch-and-bound, mathematical programming or constraint programming are also increasingly popular. On the front of applications, metaheuristics are now used to nd high-quality solutions to an ever-growing number of complex, ill-de ned re- world problems, in particular combinatorial ones.
 

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Contents

1 Simulated Annealing
1
2 Tabu Search
41
3 Variable Neighborhood Search
60
Fundamentals Advances and Applications
87
5 Genetic Algorithms
109
6 A Modern Introduction to Memetic Algorithms
140
7 Genetic Programming
185
Overview and Recent Advances
226
13 Large Neighborhood Search
399
14 Artificial Immune Systems
420
15 A Classification of Hyperheuristic Approaches
449
16 Metaheuristic Hybrids
469
17 Parallel Metaheuristics
497
Learning While Optimizing
543
19 Stochastic Search in Metaheuristics
572
20 An Introduction to Fitness Landscape Analysis and Cost Models for Local Search
599

9 Advanced Multistart Methods
265
Advances Hybridizations and Applications
282
11 Guided Local Search
321
Framework and Applications
362
21 Comparison of Metaheuristics
624
Subject Index
641
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