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Hybrid Metaheuristics 2013 ed. [Kõva köide]

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  • Formaat: Hardback, 458 pages, kõrgus x laius: 235x155 mm, kaal: 887 g, XXVI, 458 p., 1 Hardback
  • Sari: Studies in Computational Intelligence 434
  • Ilmumisaeg: 01-Aug-2012
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642306705
  • ISBN-13: 9783642306709
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Hybrid Metaheuristics 2013 ed.Suurem pilt
  • Formaat: Hardback, 458 pages, kõrgus x laius: 235x155 mm, kaal: 887 g, XXVI, 458 p., 1 Hardback
  • Sari: Studies in Computational Intelligence 434
  • Ilmumisaeg: 01-Aug-2012
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642306705
  • ISBN-13: 9783642306709
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783642306709.html
Märksõnad:

The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables readers to design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuristics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling.



This book provides a complete background for designing and implementing hybrid metaheuristics to solve complex optimization problems - continuous/discrete, mono-objective/multi-objective, optimization under uncertainty - in a wide range of application domains.

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From the book reviews:

Hybrid Metaheuristics is an excellent manuscript for a reader who wants to understand state-of-the-art hybrid metaheuristics and their applications. this book could be a source of inspiration for those looking for new procedures for hybridizing metaheuristics and new application areas for metaheuristic techniques. a good reference for researchers, practitioners, and students of operations research or computer science who want to have a complete view of metaheuristics and the process of obtaining new procedures by hybridization. (Javier Faulin, Interfaces, Vol. 44 (5), September-October, 2014)

Part I Hybrid Metaheuristics for Mono and Multi-objective Optimization, and Optimization under Uncertainty
1 A Unified Taxonomy of Hybrid Metaheuristics with Mathematical Programming, Constraint Programming and Machine Learning
3(74)
El-Ghazali Talbi
1.1 Introduction
3(1)
1.2 Hybrid Metaheuristics
4(15)
1.2.1 Design Issues
4(12)
1.2.2 Implementation Issues
16(1)
1.2.3 A Grammar for Extended Hybridization Schemes
17(2)
1.3 Combining Metaheuristics with Mathematical Programming
19(16)
1.3.1 Mathematical Programming Approaches
20(5)
1.3.2 Classical Hybrid Approaches
25(10)
1.4 Combining Metaheuristics with Constraint Programming
35(6)
1.4.1 Constraint Programming
36(1)
1.4.2 Classical Hybrid Approaches
37(4)
1.5 Hybrid Metaheuristics with Machine Learning and Data Mining
41(9)
1.5.1 Data Mining Techniques
41(2)
1.5.2 Main Schemes of Hybridization
43(7)
1.6 Hybrid Metaheuristics for Multi-objective Optimization
50(16)
1.6.1 Combining Metaheuristics for MOPs
50(6)
1.6.2 Combining Metaheuristics with Exact Methods for MOP
56(6)
1.6.3 Combining Metaheuristics with Data Mining for MOP
62(4)
1.7 Conclusions and Perspectives
66(11)
References
68(9)
2 Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing
77(20)
Ulrike Ritzinger
Jakob Puchinger
2.1 Introduction
77(1)
2.2 Dynamic and Stochastic Vehicle Routing Problems
78(4)
2.2.1 Vehicle Routing Problem Variants and Available Information
79(2)
2.2.2 Algorithms for Solving Dynamic Vehicle Routing Problems
81(1)
2.2.3 Algorithms for Solving Stochastic Vehicle Routing Problems
82(1)
2.3 Hybrid Metaheuristics for Dynamic Problems
82(3)
2.3.1 Parallelization Approaches
83(1)
2.3.2 Other Hybridization Approaches
84(1)
2.4 Hybrid Metaheuristics with Stochastic Information
85(3)
2.4.1 Hybridization of Search Techniques
85(2)
2.4.2 Objective Function Hybridization
87(1)
2.5 Hybrid Metaheuristics for Dynamic and Stochastic Problems
88(2)
2.5.1 Single Solution Approaches
88(1)
2.5.2 Algorithms Based on Solution Pools
89(1)
2.6 Conclusion
90(7)
References
91(6)
3 Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search
97(24)
Jeremie Dubois-Lacoste
Manuel Lopez-Ibanez
Thomas Stutzle
3.1 Introduction
97(2)
3.2 Preliminaries
99(1)
3.3 Dominance-Based Multi-objective Optimization
100(3)
3.4 Scalarization-Based Multi-objective Optimization
103(4)
3.5 Hybridization of Search Paradigms
107(2)
3.5.1 Sequential Hybridization
108(1)
3.5.2 Iterative Hybridization
109(1)
3.6 Experimental Results of TP+PLS
109(4)
3.7 Conclusion
113(8)
References
113(8)
Part II Combining Metaheuristics with (Complementary) Metaheuristics
4 Hybridizing Cellular GAs with Active Components of Bio-inspired Algorithms
121(14)
E. Alba
A. Villagra
4.1 Introduction
121(1)
4.2 Characterizing Cellular Genetic Algorithms
122(2)
4.3 Classic PSO
124(1)
4.4 Our Hybrid cGA Algorithms
125(1)
4.5 Experiments and Analysis of Results
125(7)
4.6 Conclusions and Further Work
132(3)
References
132(3)
5 Hybridizations of GRASP with Path-Relinking
135(22)
Paola Festa
Mauricio G.C. Resende
5.1 Introduction
135(2)
5.2 GRASP
137(5)
5.2.1 GRASP Construction
139(2)
5.2.2 Other Local Search Strategies
141(1)
5.2.3 Stopping Criteria
141(1)
5.3 Path-Relinking
142(4)
5.3.1 Flavors of Path-Relinking
143(2)
5.3.2 Path-Relinking and Elite Sets
145(1)
5.4 GRASP with Path-Relinking and Evolutionary Path-Relinking
146(1)
5.5 Hybrid GRASP Lagrangean Heuristic
147(1)
5.6 Parallel GRASP with Path-Relinking
148(1)
5.7 Concluding Remarks
149(8)
References
151(6)
6 Hybrid Metaheuristics for the Graph Partitioning Problem
157(30)
Una Benlic
Jin-Kao Hao
6.1 Introduction
157(1)
6.2 Problem Definition and Bechmark Instances
158(2)
6.2.1 Problem Description and Notations
158(1)
6.2.2 Benchmark Instances
159(1)
6.3 Classical Approaches for the Graph Partitioning Problem
160(3)
6.4 Evolutionary Hybrids for Graph Partitioning
163(6)
6.4.1 Kernighan-Lin Bisection Algorithm, Improvement and Adaptation
163(2)
6.4.2 Selected Evolutionary Approaches for Graph Partitioning
165(4)
6.5 Multilevel Graph Partitioning
169(14)
6.5.1 Formal Definition of the Multilevel Paradigm
169(1)
6.5.2 Multilevel Schemes
169(4)
6.5.3 Effective Refinement Strategies Based on Metaheuristics
173(6)
6.5.4 The Key to Effectiveness of Partition Refinement Procedures
179(4)
6.6 Conclusion
183(4)
References
183(4)
7 Hybrid Metaheuristics for Medical Data Classification
187(32)
Sarab Al-Muhaideb
Mohamed El Bachir Menai
7.1 Introduction
187(1)
7.2 The Classification Problem
188(2)
7.3 Features and Challenges of Medical Data Classification
190(4)
7.4 Hybrid Metaheuristics for Model Learning and Optimization in Medical Data Classification
194(10)
7.4.1 Learning Classifier Systems
194(6)
7.4.2 Other Hybrid Metaheuristics
200(3)
7.4.3 Hybrid Metaheuristics for Enhancing Classification Accuracy in Medical Data Classification
203(1)
7.5 Hybrid Metaheuristics for Model Selection in Medical Data Classification
204(7)
7.5.1 Hybrid Metaheuristics for Feature Subset Selection in Medical Data Classification
207(1)
7.5.2 Hybrid Metaheuristics for Artificial Neural Network Model Selection in Medical Data Classification
208(3)
7.5.3 Hybrid Metaheuristics for Full Model Selection in Medical Data Classification
211(1)
7.6 Conclusion
211(8)
References
213(6)
8 HydroCM: A Hybrid Parallel Search Model for Heterogeneous Platforms
219(18)
Julian Dominguez
Enrique Alba
8.1 Introduction
219(1)
8.2 Decentralized, Heterogeneous and Hybrid Parallel Metaheuristics
220(5)
8.2.1 Parallel EA Models
220(1)
8.2.2 Parallel LSM Models
221(1)
8.2.3 Being Heterogeneous
222(2)
8.2.4 Classifying Hybrid Metaheuristics
224(1)
8.3 Description of Our Proposal
225(2)
8.3.1 An Overview of HydroCM
225(1)
8.3.2 Ethane
226(1)
8.4 Performance Measures and Speedup
227(1)
8.5 Problems, Parameters, and Platform
228(2)
8.5.1 Benchmark Problems
228(1)
8.5.2 Parameters of the Algorithms and Platform
229(1)
8.6 Tests and Analysis
230(4)
8.6.1 Numerical Effort
231(1)
8.6.2 Total Run Time
231(1)
8.6.3 Speedup
232(1)
8.6.4 Evolution of the Fitness
233(1)
8.7 Conclusions
234(3)
References
234(3)
9 A Multi-thread GRASPxELS for the Heterogeneous Capacitated Vehicle Routing Problem
237(36)
Christophe Duhamel
Christophe Gouinaud
Philippe Lacomme
Caroline Prodhon
9.1 Introduction
237(1)
9.2 Parallel Metaheuristics
238(4)
9.2.1 Technologies
238(1)
9.2.2 State of the Art for Operations Research Problems
239(3)
9.3 Heterogeneous Capacitated Vehicle Routing Problem
242(1)
9.4 Hybrid GRASP x Parallel ELS
243(3)
9.4.1 GRASPxELS Principle
244(1)
9.4.2 Parallelization
244(1)
9.4.3 Key-Features of the GRASP x Parallel ELS
245(1)
9.5 GRASP x Parallel ELS for the HVRP
246(2)
9.6 Detailed Components of the GRASP x Parallel ELS
248(4)
9.6.1 Initial Solutions of the GRASP
248(1)
9.6.2 Dispatching: Threads Management
249(1)
9.6.3 Parallel ELS Scheme
249(2)
9.6.4 Synchronization and Selection Procedure
251(1)
9.6.5 Shared Memory Management
251(1)
9.7 Computational Evaluation
252(10)
9.7.1 Settings and Benchmarks
253(2)
9.7.2 Evaluation of the Communication Time
255(1)
9.7.3 Results on Classical HVRP Instances
255(3)
9.7.4 Results on DLP_HVRP Instances
258(2)
9.7.5 Convergence Rate of the Parallel ELS
260(2)
9.8 Concluding Remarks and Future Research
262(11)
References
263(10)
Part III Combining Metaheuristics with Exact Methods from Mathematical Programming Approaches
10 The Heuristic (Dark) Side of MIP Solvers
273(12)
Andrea Lodi
10.1 Introduction
273(1)
10.2 The Heuristic Nature of MIP Solvers
274(3)
10.2.1 Some Trivial Facts
275(1)
10.2.2 Less Trivial Facts
276(1)
10.3 Key Features of MIP Solvers
277(5)
10.3.1 Preprocessing
277(2)
10.3.2 Cutting Plane Generation
279(1)
10.3.3 Sophisticated Branching Strategies
280(1)
10.3.4 Primal Heuristics
281(1)
10.4 MIP Solvers, Metaheuristics and Hybrid Algorithms
282(3)
References
283(2)
11 Combining Column Generation and Metaheuristics
285(50)
Filipe Alvelos
Amaro de Sousa
Dorabella Santos
11.1 Introduction
286(4)
11.1.1 Motivations
286(2)
11.1.2 Literature Review
288(1)
11.1.3 Contributions
289(1)
11.1.4
Chapter Structure
290(1)
11.2 A Decomposition Mixed Integer Programming Model
290(9)
11.2.1 Model Statement
291(2)
11.2.2 Examples
293(6)
11.3 Column Generation
299(7)
11.3.1 Overview
299(1)
11.3.2 Subproblem
300(2)
11.3.3 Algorithm
302(2)
11.3.4 Example
304(1)
11.3.5 Perturbed Column Generation
305(1)
11.4 Solving the MIP Decomposition Model
306(3)
11.4.1 Static Approaches
307(1)
11.4.2 MIP Based CG Heuristic
307(1)
11.4.3 Branch-and-Price
308(1)
11.4.4 Branch-and-Price Heuristics
308(1)
11.4.5 Lagrangean Heuristics
309(1)
11.5 A Combinatorial Decomposition Model
309(5)
11.5.1 Solution Representation and Search Space
309(2)
11.5.2 Evaluating Solutions and Moves
311(3)
11.6 SearchCol
314(10)
11.6.1 Overview
314(3)
11.6.2 Evaluating Solutions
317(2)
11.6.3 Initial Solutions
319(2)
11.6.4 Defining Perturbations
321(3)
11.6.5 Termination
324(1)
11.7 Metaheuristic Search in SearchCol
324(6)
11.7.1 Additional Algorithmic Components
325(2)
11.7.2 SearchCol with Multi-start Local Search
327(3)
11.7.3 SearchCol with Variable Neighborhood Search
330(1)
11.7.4 SearchCol with MIP
330(1)
11.8 Conclusions
330(5)
References
331(4)
12 Application of Large Neighborhood Search to Strategic Supply Chain Management in the Chemical Industry
335(18)
Pedro J. Copado-Mendez
Christian Blum
Gonzalo Guillen-Gosalbez
Laureano Jimenez
12.1 Introduction
335(2)
12.2 Spatially Explicit Supply Chain Models
337(1)
12.3 The Proposed LNS Algorithm
338(2)
12.3.1 Algorithm
339(1)
12.4 Experimental Evaluation
340(5)
12.4.1 Algorithm Tuning
341(3)
12.4.2 Final Comparison
344(1)
12.5 Conclusions
345(8)
References
345(8)
13 A VNS-Based Heuristic for Feature Selection in Data Mining
353(16)
A. Mucherino
L. Liberti
13.1 Introduction
353(2)
13.2 Consistent Biclustering
355(4)
13.3 A Bilevel Reformulation
359(2)
13.4 A VNS-Based Heuristic
361(2)
13.5 Computational Experiments
363(3)
13.5.1 Wine Fermentations
363(2)
13.5.2 Colon Cancer - Set I
365(1)
13.5.3 Colon Cancer - Set II
365(1)
13.5.4 Ovarian Cancer
366(1)
13.6 Conclusions
366(3)
References
367(2)
14 Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives
369
Graham Kendall
Barry McCollum
Frederico R.B. Cruz
Paul McMullan
Lyndon While
14.1 Introduction
370(1)
14.2 Related Work
371(1)
14.3 Problem Definition
372(1)
14.4 Experimental Setup
373(3)
14.4.1 Phase 1: CPLEX
373(1)
14.4.2 Phase 2: Simulated Annealing
373(1)
14.4.3 Evaluation Function
374(1)
14.4.4 Perturbation Operators
375(1)
14.4.5 Experimental Methodology
376(1)
14.5 Results
376(7)
14.6 Conclusion
383
References
383