Ant colony optimization and constraint programming:
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| Main Author: | |
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| Format: | Book |
| Language: | English French |
| Published: |
London [u.a.]
Iste [u.a.]
2010
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| Subjects: | |
| Online Access: | Publisher description Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XII, 232 S. graph. Darst. |
| ISBN: | 9781848211308 |
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| 240 | 1 | 0 | |a Optimisation par colonies de fourmis |
| 245 | 1 | 0 | |a Ant colony optimization and constraint programming |c Christine Solnon |
| 264 | 1 | |a London [u.a.] |b Iste [u.a.] |c 2010 | |
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| 650 | 4 | |a Swarm intelligence | |
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| adam_text | Titel: Ant colony optimization and constraint programming
Autor: Solnon, Christine
Jahr: 2010
Table of Contents
Foreword .................................. xi
Acknowledgements ............................ xiii
Chapter 1. Introduction ......................... 1
1.1. Overview of the book....................... 2
1.1.1. Constraint programming.................. 3
1.1.2. Ant colony optimization.................. 4
1.1.3. Constraint programming with ant colony optimization . 4
Chapter 2. Computational Complexity ................ 7
2.1. Complexity of an algorithm................... 8
2.2. Complexity of a problem..................... 10
2.2.1. The V class......................... 10
2.2.2. TheTVP class........................ 11
2.2.3. TVT-complete problems.................. 12
2.2.4. ËËÑ-hard problems..................... 14
2.2.5. Undecidable problems................... 14
2.2.6. Complexity of optimization problems.......... 15
2.3. Where the most difficult instances can be found........ 15
2.3.1. Phase transition....................... 16
2.3.2. Search landscape...................... 19
2.4. Solving AfP-hard problems in practice............. 21
2.4.1. Exploitation of particular cases.............. 23
2.4.2. Approximation algorithms................. 23
2.4.3. Heuristics and metaheuristics............... 24
2.4.4. Structuring and filtering the search space ........ 24
vi ACÓ and CP
Part I. Constraint Programming................ 27
Introduction to Part I........................... 29
Chapter 3. Constraint Satisfaction Problems............. 31
3.1. What is a constraint?....................... 31
3.1.1. Definition of a constraint.................. 32
3.1.2. Arity of a constraint and global constraints....... 33
3.2. What is a constraint satisfaction problem?........... 33
3.2.1. Complexity of CSPs.................... 34
3.3. Optimization problems related to CSPs............. 35
3.3.1. Maximizing constraint satisfaction............ 35
3.3.2. Constrained optimization ................. 36
3.4. The ?-queens problem...................... 37
3.4.1. Description of the problem ................ 37
3.4.2. First CSP model ...................... 38
3.4.3. Second CSP model..................... 39
3.4.4. Third CSP model...................... 40
3.4.5. Influence of the model on the solution process ..... 41
3.5. The stable marriage problem................... 43
3.5.1. Description of the problem ................ 43
3.5.2. CSPmodel ......................... 45
3.6. Randomly generated binary CSPs................ 46
3.7. The car sequencing problem................... 47
3.7.1. Description of the problem ................ 47
3.7.2. CSPmodel ......................... 49
3.8. Discussion............................. 50
Chapter 4. Exact Approaches ...................... 53
4.1. Construction of a search tree................... 53
4.2. Constraint propagation...................... 57
4.2.1. Forward checking...................... 57
4.2.2. Maintaining arc consistency................ 59
4.3. Ordering heuristics........................ 60
4.3.1. Heuristics for choosing variables............. 61
4.3.2. Heuristics for choosing values............... 62
4.3.3. Randomized restart..................... 63
4.4. From satisfaction to optimization problems.......... 63
4.5. Discussion............................. 65
Table of Contents vii
Chapter 5. Perturbative Heuristic Approaches............ 69
5.1. Genetic algorithms........................ 70
5.1.1. Basic principles....................... 70
5.1.2. Using GAs to solve CSPs................. 73
5.2. Local search............................ 73
5.2.1. Basic principles....................... 73
5.2.2. Metaheuristics based on LS................ 75
5.2.3. Using LS to solve CSPs.................. 77
5.3. Particle swarm optimization................... 78
5.3.1. Basic principles....................... 78
5.3.2. Using PSO to solve CSPs................. 80
5.4. Discussion............................. 80
Chapter 6. Constructive Heuristic Approaches............ 85
6.1. Greedy randomized approaches................. 86
6.1.1. Basic principles....................... 86
6.1.2. Using greedy randomized algorithms to solve CSPs . . 88
6.2. Estimation of distribution algorithms.............. 88
6.2.1. Basic principles....................... 88
6.2.2. Using EDAs to solve CSPs ................ 90
6.3. Ant colony optimization..................... 90
6.4. Discussion............................. 91
Chapter 7. Constraint Programming Languages........... 93
7.1. Constraint logic programming.................. 94
7.2. Constraint programming libraries................ 96
7.3. Constraint-based local search.................. 96
7.4. Discussion............................. 99
Part II. Ant Colony Optimization ............... 101
Introduction to Part II .......................... 103
Chapter 8. From Swarm Intelligence to Ant Colony Optimization 105
8.1. Complex systems and swarm intelligence........... 106
8.2. Searching for shortest paths by ant colonies.......... 108
8.3. Ant system and the traveling salesman problem........ Ill
8.3.1. Pheromone structure.................... 112
8.3.2. Construction of a Hamiltonian cycle by an ant..... 114
viii ACO and CP
8.3.3. Pheromone updating step................. 115
8.3.4. Artificial versus real ants.................. 115
8.4. Generic ACO framework..................... 116
8.4.1. Pheromone structure and construction graph ...... 116
8.4.2. Construction of combinations by ants .......... 118
8.4.3. Improving combinations with local search........ 120
8.4.4. Pheromone updating step................. 121
8.4.5. Parameters of an ACO algorithm............. 122
Chapter 9. Intensification versus Diversification........... 125
9.1. ACO mechanisms for intensifying the search......... 125
9.2. ACO mechanisms for diversifying the search......... 127
9.3. Balancing intensification and diversification.......... 128
9.4. Measures of diversification/intensification........... 135
9.4.1. The ë-branching factor................... 136
9.4.2. Resampling ratio...................... 136
9.4.3. Similarity ratio....................... 137
Chapter 10. Beyond Static Combinatorial Problems ........ 141
10.1. Multi-objective problems..................... 141
10.1.1. Definition of multi-objective problems.......... 141
10.1.2. Solving multi-objective problems with ACO...... 143
10.2. Dynamic optimization problems................. 145
10.2.1. Definition of dynamic optimization problems...... 145
10.2.2. Solving dynamic optimization problems with ACO ... 146
10.3. Optimization problems over continuous domains....... 147
10.3.1. Definition of continuous optimization problems..... 147
10.3.2. Solving continuous optimization problems with ACO . 148
Chapter 11. Implementation Issues................... 151
11.1. Data structures........................... 151
11.1.1. Data structures associated with pheromone factors ... 151
11.1.2. Data structures associated with heuristic factors..... 153
11.1.3. Data structures associated with ants ........... 154
11.2. Selection of a component with respect to probabilities .... 154
11.3. Implementation of a local search procedure.......... 157
11.4. Computation of diversification/intensification measures . . . 157
11.4.1. Resampling ratio...................... 158
11.4.2. Similarity ratio....................... 158
Table of Contents ix
Part III. CP with ACÓ........................ 161
Introduction to Part III.......................... 163
Chapter 12. Sequencing Cars with ACO................. 165
12.1. Notation.............................. 165
12.2. A first pheromone structure for identifying good car
sequences ............................. 166
12.2.1. Pheromone structure.................... 167
12.2.2. Construction of a sequence by an ant........... 168
12.2.3. Pheromone laying step................... 170
12.3. A second pheromone structure for identifying critical cars . . 171
12.3.1. Pheromone structure.................... 171
12.3.2. Construction of a sequence by an ant........... 172
12.3.3. Pheromone updating step................. 172
12.4. Combining the two pheromone structures........... 173
12.4.1. First pheromone structure................. 173
12.4.2. Second pheromone structure................ 173
12.4.3. Construction of a sequence by an ant........... 173
12.5. Comparison of the different ACO algorithms......... 174
12.5.1. Considered algorithms................... 174
12.5.2. Test suite........................... 175
12.5.3. Parameter settings ..................... 175
12.5.4. Experimental results.................... 177
12.6. Comparison of ACO with state-of-the-art approaches..... 178
12.6.1. Considered approaches................... 178
12.6.2. IDWalk ........................... 179
12.6.3. VFLS ............................ 179
12.6.4. Experimental set-up.................... 180
12.6.5. Experimental results.................... 180
12.7. Discussion............................. 182
Chapter 13. Subset Selection with ACO................ 185
13.1. Subset selection problems.................... 186
13.1.1. Maximum clique...................... 186
13.1.2. Multidimensional knapsack................ 187
13.1.3. Maximum Boolean satisfiability ............. 187
13.1.4. Maximum constraint satisfaction............. 187
13.1.5. Minimum vertex cover................... 188
13.1.6. Maximum common subgraph............... 188
÷ ACO and CP
13.1.7. Edge-weighted fc-cardinality tree............. 189
13.2. Description of Ant-SSP...................... 189
13.2.1. Construction of a combination by an ant......... 190
13.2.2. Pheromone laying step................... 192
13.3. Instantiations of Ant-SSP with respect to two pheromone
strategies.............................. 192
13.3.1. The vertex pheromone strategy.............. 193
13.3.2. The clique pheromone strategy.............. 193
13.3.3. Comparison of the two strategies............. 194
13.4. Instantiation of Ant-SSP to solve CSPs............. 196
13.4.1. Heuristic factor....................... 196
13.4.2. Local search......................... 197
13.5. Experimental results....................... 197
13.5.1. Randomly generated binary instances.......... 197
13.5.2. Results on instances of the 2006 solver competition . . 199
13.6. Discussion............................. 202
Chapter 14. Integration of ACO in a CP Language ......... 205
14.1. Framework for integrating ACO within a CP library..... 206
14.1.1. Pheromone strategy..................... 207
14.1.2. Construction of assignments................ 208
14.1.3. Pheromone updating step................. 210
14.2. Illustration of ACO-CP on the car sequencing problem . . . . 210
14.2.1. CSP model ......................... 210
14.2.2. Variable ordering heuristic................. 211
14.2.3. Pheromone strategies.................... 211
14.2.4. Heuristic factor....................... 212
14.2.5. Experimental results.................... 213
14.3. Discussion............................. 214
Chapter 15. Conclusion.......................... 215
15.1. Towards constraint-based ACO search............. 215
15.2. Towards a reactive ACO search................. 216
Bibliography ................................ 219
Index..................................... 231
|
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| spelling | Solnon, Christine Verfasser aut Optimisation par colonies de fourmis Ant colony optimization and constraint programming Christine Solnon London [u.a.] Iste [u.a.] 2010 XII, 232 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Constraint programming (Computer science) Mathematical optimization Swarm intelligence Ant algorithms Optimierung (DE-588)4043664-0 gnd rswk-swf Ameisenalgorithmus (DE-588)4704280-1 gnd rswk-swf Constraint Künstliche Intelligenz (DE-588)4314755-0 gnd rswk-swf Ameisenalgorithmus (DE-588)4704280-1 s Constraint Künstliche Intelligenz (DE-588)4314755-0 s Optimierung (DE-588)4043664-0 s DE-604 http://www.loc.gov/catdir/enhancements/fy1005/2009050443-d.html Publisher description HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020300843&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Solnon, Christine Ant colony optimization and constraint programming Constraint programming (Computer science) Mathematical optimization Swarm intelligence Ant algorithms Optimierung (DE-588)4043664-0 gnd Ameisenalgorithmus (DE-588)4704280-1 gnd Constraint Künstliche Intelligenz (DE-588)4314755-0 gnd |
| subject_GND | (DE-588)4043664-0 (DE-588)4704280-1 (DE-588)4314755-0 |
| title | Ant colony optimization and constraint programming |
| title_alt | Optimisation par colonies de fourmis |
| title_auth | Ant colony optimization and constraint programming |
| title_exact_search | Ant colony optimization and constraint programming |
| title_full | Ant colony optimization and constraint programming Christine Solnon |
| title_fullStr | Ant colony optimization and constraint programming Christine Solnon |
| title_full_unstemmed | Ant colony optimization and constraint programming Christine Solnon |
| title_short | Ant colony optimization and constraint programming |
| title_sort | ant colony optimization and constraint programming |
| topic | Constraint programming (Computer science) Mathematical optimization Swarm intelligence Ant algorithms Optimierung (DE-588)4043664-0 gnd Ameisenalgorithmus (DE-588)4704280-1 gnd Constraint Künstliche Intelligenz (DE-588)4314755-0 gnd |
| topic_facet | Constraint programming (Computer science) Mathematical optimization Swarm intelligence Ant algorithms Optimierung Ameisenalgorithmus Constraint Künstliche Intelligenz |
| url | http://www.loc.gov/catdir/enhancements/fy1005/2009050443-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020300843&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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