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Hybrid Metaheuristics: Powerful Tools for Optimization 1st ed. 2016 [Kõva köide]

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  • Formaat: Hardback, 157 pages, kõrgus x laius: 235x155 mm, kaal: 3908 g, 9 Illustrations, color; 11 Illustrations, black and white; XVI, 157 p. 20 illus., 9 illus. in color., 1 Hardback
  • Sari: Artificial Intelligence: Foundations, Theory, and Algorithms
  • Ilmumisaeg: 31-May-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319308823
  • ISBN-13: 9783319308821
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Hybrid Metaheuristics: Powerful Tools for Optimization 1st ed. 2016Suurem pilt
  • Formaat: Hardback, 157 pages, kõrgus x laius: 235x155 mm, kaal: 3908 g, 9 Illustrations, color; 11 Illustrations, black and white; XVI, 157 p. 20 illus., 9 illus. in color., 1 Hardback
  • Sari: Artificial Intelligence: Foundations, Theory, and Algorithms
  • Ilmumisaeg: 31-May-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319308823
  • ISBN-13: 9783319308821
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319308821.html
Märksõnad:
This book explains the most prominent, successfulhybridization techniques and some newer promising strategies. A firstintroductory chapter reviews the basic principles of local search, prominentmetaheuristics, and tree search, dynamic programming, mixed integer linearprogramming, and constraint programming for combinatorial optimizationpurposes. The chapters that follow present five generally applicablehybridization strategies, with exemplary case studies on selected problems:incomplete solution representations and decoders; problem instance reduction;large neighborhood search; parallel non-independent construction of solutionswithin metaheuristics; and hybridization based on complete solution archives.The authors are among the leading researchers in thehybridization of metaheuristics with other techniques for optimization, andtheir work reflects the broad shift to problem-oriented rather thanalgorithm-oriented approaches, enabling faster and more effectiveimplement

ation in real-life applications. This hybridization is not restrictedto different variants of metaheuristics but includes, for example, thecombination of mathematical programming, dynamic programming, constraintprogramming or statistical modeling with metaheuristics, reflecting cross-fertilizationin fields such as optimization, algorithmics, mathematical modeling, operationsresearch, statistics, and simulation. The book is a valuable introduction andreference for researchers and graduate students in these domains.

Introduction.- Incomplete SolutionRepresentations and Decoders.- Hybridization Based on Problem InstanceReduction.- Hybridization Based on Large Neighborhood Search.- Making Use of aParallel, Non-independent, Construction of Solutions Within Metaheuristics.-Hybridization Based on Complete Solution Archives.- Further Hybrids andConclusions.

Arvustused

From the perspective of a practitioner with real-world experience in combinatorial optimization, the text is comprehensive, and at the same time it offers fresh angles and shares valuable expertise. I highly recommend this book, both to practitioners and theoreticians at the post graduate levels, be they rooted either at the formal/rigid or heuristic/soft ends of Combinatorial Optimization research or practice. (Ofer M. Shir, Genetic Programming and Evolvable Machines, Vol. 19 (1-2), June, 2018)

This book by Blum and Raidl constructs a bridge between these two approaches and aims to share expertise gained from each end. The book is well-structured. I highly recommend this book,both to practitioners and theoreticians at the post graduate levels, be they rooted either at the formal/rigid or heuristic/soft ends of Combinatorial Optimization research or practice. (Ofer M. Shir, Genetic Programming and Evolvable Machines, Vol. 19 (1-2), June, 2018)

1 Introduction
1(26)
1.1 Basic Heuristic Methods
2(2)
1.1.1 Constructive Heuristics
2(1)
1.1.2 Local Search
3(1)
1.2 Metaheuristics
4(11)
1.2.1 Greedy Randomized Adaptive Search Procedures
5(1)
1.2.2 Iterated Greedy Algorithms
6(1)
1.2.3 Iterated Local Search
7(1)
1.2.4 Simulated Annealing
8(1)
1.2.5 Tabu Search
8(2)
1.2.6 Variable Neighborhood Search
10(2)
1.2.7 Ant Colony Optimization
12(1)
1.2.8 Evolutionary Algorithms
13(1)
1.2.9 Further Metaheuristics
14(1)
1.3 Tree Search Methods
15(2)
1.4 Dynamic Programming
17(2)
1.5 Mixed Integer Linear Programming
19(2)
1.6 Constraint Programming
21(2)
1.7 Why Hybridization?
23(1)
1.8 Further Outline of the Book
24(3)
2 Incomplete Solution Representations and Decoders
27(18)
2.1 General Idea
28(1)
2.2 Application to the Generalized Minimum Spanning Tree Problem
28(14)
2.2.1 Initial Solutions
30(1)
2.2.2 Node Set Based VNS (NSB-VNS)
30(3)
2.2.3 Global Edge Set Based VNS (GESB-VNS)
33(4)
2.2.4 Combined VNS (COMB-VNS)
37(1)
2.2.5 Results
37(5)
2.3 Other Applications of Decoder-Based Hybrids
42(3)
3 Hybridization Based on Problem Instance Reduction
45(18)
3.1 General Idea
45(3)
3.1.1 The Construct, Merge, Solve & Adapt Algorithm
46(2)
3.2 Application to Minimum Common String Partition
48(12)
3.2.1 Probabilistic Solution Generation
49(1)
3.2.2 Solving Reduced Sub-instances
50(2)
3.2.3 Experimental Evaluation
52(8)
3.3 Other Applications of the Idea of Instance Reduction
60(3)
3.3.1 The Generate-and-Solve Framework
61(1)
3.3.2 Solution Merging
61(2)
4 Hybridization Based on Large Neighborhood Search
63(20)
4.1 General Idea
64(1)
4.1.1 MIP-Based Large Neighborhood Search
64(1)
4.2 Application to Minimum Weight Dominating Set Problem
65(10)
4.2.1 Generation of the Initial Solution
67(1)
4.2.2 Partial Destruction of Solutions
67(1)
4.2.3 Application of the MIP Solver
68(1)
4.2.4 Experimental Evaluation
69(6)
4.3 Application to the Generalized Minimum Spanning Tree Problem
75(4)
4.3.1 Global Subtree Optimization Neighborhood
75(2)
4.3.2 Results of COMB*-VNS: The GSON-Enhanced VNS
77(2)
4.4 Other Applications of the Idea of Large Neighborhood Search
79(4)
5 Making Use of a Parallel, Non-independent, Construction of Solutions Within Metaheuristics
83(18)
5.1 General Idea
84(3)
5.1.1 Beam-ACO: Combining Ant Colony Optimization with Beam Search
85(2)
5.2 Application to the Multidimensional Knapsack Problem
87(11)
5.2.1 Greedy Heuristic
88(1)
5.2.2 Beam Search for the MKP
88(1)
5.2.3 A Pure ACO Approach for the MKP
89(3)
5.2.4 Beam-ACO for the MKP
92(1)
5.2.5 Experimental Evaluation
92(6)
5.3 Other Applications of the Idea of Parallel, Non-independent Solution Construction
98(3)
6 Hybridization Based on Complete Solution Archives
101(26)
6.1 General Idea
102(6)
6.1.1 Related Work
103(1)
6.1.2 Trie-Based Solution Archive for Binary Strings
104(4)
6.2 Application to Royal Road Functions and NK Landscapes
108(4)
6.2.1 Results for Royal Road Functions
109(1)
6.2.2 Results for NK Landscapes
110(2)
6.3 Application to the Generalized Minimum Spanning Tree Problem
112(11)
6.3.1 Solution Archive for the Node Set Based Representation
112(2)
6.3.2 Solution Archive for the Global Edge Set Based Representation
114(1)
6.3.3 The Archive-Enhanced Evolutionary Algorithm for GMST
115(1)
6.3.4 Pruning the NSB Trie by Bounding
116(2)
6.3.5 Pruning the GESB Trie by Bounding
118(1)
6.3.6 Results
119(4)
6.4 Other Applications of the Concept of Complete Solution Archives
123(4)
7 Further Hybrids and Conclusions
127(10)
7.1 Combinations of Metaheuristics with Other Heuristic Methods
127(2)
7.2 Hybridization by Efficiently Solving Problem Relaxations
129(1)
7.3 Strategic Guidance of Branch-and-Bound by Metaheuristics
129(2)
7.4 Mathematical Programming Decomposition Based Hybrids
131(3)
7.4.1 Lagrangian Decomposition
131(1)
7.4.2 Dantzig--Wolfe Decomposition and Column Generation
132(1)
7.4.3 Benders Decomposition
133(1)
7.5 Conclusions
134(3)
References 137