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 Pai, G. A. Vijayalakshmi author.
 London, UK : ISTE, Ltd. ; Hoboken, NJ : John Wiley & Sons, Inc., 2018.
 Description
 Book — 1 online resource.
 Summary

 1. A Brief Primer on Metaheuristics.
 2. Heuristic Portfolio Selection.
 3. Risk Budgeted Portfolio Optimization.
 4. Heuristic Optimization of Equity Market Neutral Portfolios.
 5. Metaheuristic 13030 Portfolio Construction.
 6. Metaheuristic Portfolio Rebalancing with Transaction Costs.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
2. Evolutionary algorithms [2017]
 Pétrowski, Alain, author.
 London : ISTE, 2017.
 Description
 Book — 1 online resource.
 Summary

 Preface xi
 Chapter 1 Evolutionary Algorithms 1 1.1 From natural evolution to engineering 1 1.2 A generic evolutionary algorithm 3 1.3 Selection operators 5 1.4 Variation operators and representation 21 1.5 Binary representation 25 1.6 The simple genetic algorithm 30 1.7 Conclusion 31
 Chapter 2 Continuous Optimization 33 2.1 Introduction 33 2.2 Real representation and variation operators for evolutionary algorithms 35 2.3 Covariance Matrix Adaptation Evolution Strategy 46 2.4 A restart CMA Evolution Strategy 55 2.5 Differential Evolution (DE) 57 2.6 SuccessHistory based Adaptive Differential Evolution (SHADE) 65 2.7 Particle Swarm Optimization 70 2.8 Experiments and performance comparisons 77 2.9 Conclusion 88 2.10 Appendix: set of basic objective functions used for the experiments 89
 Chapter 3 Constrained Continuous Evolutionary Optimization 93 3.1 Introduction 93 3.2 Penalization 98 3.3 Superiority of feasible solutions 112 3.4 Evolving on the feasible region 117 3.5 Multiobjective methods 123 3.6 Parallel population approaches 130 3.7 Hybrid methods 132 3.8 Conclusion 132
 Chapter 4 Combinatorial Optimization 135 4.1 Introduction 135 4.2 The binary representation and variation operators 140 4.3 Orderbased Representation and variation operators 143 4.4 Conclusion 163
 Chapter 5 Multiobjective Optimization 165 5.1 Introduction 165 5.2 Problem formalization 166 5.3 The quality indicators 167 5.4 Multiobjective evolutionary algorithms 169 5.5 Methods using a "Pareto ranking" 169 5.6 Manyobjective problems 176 5.7 Conclusion 181
 Chapter 6 Genetic Programming for Machine Learning 183 6.1 Introduction 183 6.2 Syntax tree representation 186 6.3 Evolving the syntax trees 187 6.4 GP in action: an introductory example 194 6.5 Alternative Genetic Programming Representations 200 6.6 Example of application: intrusion detection in a computer system 210 6.7 Conclusion 215 Bibliography 217 Index 233.
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(source: Nielsen Book Data)
 Ma, Haiping author.
 London, UK : ISTE, Ltd. ; Hoboken, NJ : John Wiley & Sons, Inc. 2017.
 Description
 Book — 1 online resource.
 Summary

 Chapter 1. The Science of Biogeography 1 1.1. Introduction 1 1.2. Island biogeography 3 1.3. Influence factors for biogeography 6
 Chapter 2. Biogeography and Biological Optimization 11 2.1. A mathematical model of biogeography 11 2.2. Biogeography as an optimization process 16 2.3. Biological optimization 19 2.3.1. Genetic algorithms 19 2.3.2. Evolution strategies 20 2.3.3. Particle swarm optimization 21 2.3.4. Artificial bee colony algorithm 22 2.4. Conclusion 23
 Chapter 3. A Basic BBO Algorithm 25 3.1. BBO definitions and algorithm 25 3.1.1. Migration 26 3.1.2. Mutation 27 3.1.3. BBO implementation 27 3.2. Differences between BBO and other optimization algorithms 35 3.2.1. BBO and genetic algorithms 35 3.2.2. BBO and other algorithms 36 3.3. Simulations 37 3.4. Conclusion 44
 Chapter 4. BBO Extensions 45 4.1. Migration curves 45 4.2. Blended migration 49 4.3. Other approaches to BBO 51 4.4. Applications 56 4.5. Conclusion 59
 Chapter 5. BBO as a Markov Process 61 5.1. Markov definitions and notations 61 5.2. Markov model of BBO 72 5.3. BBO convergence 79 5.4. Markov models of BBO extensions 90 5.5. Conclusions 99
 Chapter 6. Dynamic System Models of BBO 103 6.1. Basic notation 103 6.2. Dynamic system models of BBO 105 6.3. Applications to benchmark problems 119 6.4. Conclusions 122
 Chapter 7. Statistical Mechanics Approximations of BBO 123 7.1. Preliminary foundation 123 7.2. Statistical mechanics model of BBO 128 7.2.1. Migration 128 7.2.2. Mutation 134 7.3. Further discussion 141 7.3.1. Finite population effects 141 7.3.2. Separable fitness functions 142 7.4. Conclusions 143
 Chapter 8. BBO for Combinatorial Optimization 145 8.1. Traveling salesman problem 147 8.2. BBO for the TSP 148 8.2.1. Population initialization 148 8.2.2. Migration in the TSP 150 8.2.3. Mutation in the TSP 157 8.2.4. Implementation framework 159 8.3. Graph coloring 163 8.4. Knapsack problem 165 8.5. Conclusion 167
 Chapter 9. Constrained BBO 169 9.1. Constrained optimization 170 9.2. Constrainthandling methods 172 9.2.1. Static penalty methods 172 9.2.2. Superiority of feasible points 173 9.2.3. The eclectic evolutionary algorithm 174 9.2.4. Dynamic penalty methods 174 9.2.5. Adaptive penalty methods 176 9.2.6. The nichedpenalty approach 177 9.2.7. Stochastic ranking 178 9.2.8. level comparisons 178 9.3. BBO for constrained optimization 179 9.4. Conclusion 185
 Chapter 10. BBO in Noisy Environments 187 10.1. Noisy fitness functions 188 10.2. Influence of noise on BBO 190 10.3. BBO with resampling 193 10.4. The Kalman BBO 196 10.5. Experimental results 199 10.6. Conclusion 201
 Chapter 11. Multiobjective BBO 203 11.1. Multiobjective optimization problems 204 11.2. Multiobjective BBO 211 11.2.1. Vector evaluated BBO 211 11.2.2. Nondominated sorting BBO 213 11.2.3. Niched Pareto BBO 216 11.2.4. Strength Pareto BBO 218 11.3. Realworld applications 223 11.3.1. Warehouse scheduling model 223 11.3.2. Optimization of warehouse scheduling 229 11.4. Conclusion 231
 Chapter 12. Hybrid BBO Algorithms 233 12.1. Oppositionbased BBO 234 12.1.1. Opposition definitions and concepts 234 12.1.2. Oppositional BBO 236 12.1.3. Experimental results 238 12.2. BBO with local search 240 12.2.1. Local search methods 240 12.2.2. Simulation results 245 12.3. BBO with other EAs 247 12.3.1. Iterationlevel hybridization 247 12.3.2. Algorithmlevel hybridization 250 12.3.3. Experimental results 254 12.4. Conclusion 256 Appendices 259 Appendix A. Unconstrained Benchmark Functions 261 Appendix B. Constrained Benchmark Functions 265 Appendix C. Multiobjective Benchmark Functions 289 Bibliography 309 Index 325.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 London, UK : ISTE, Ltd. ; Hoboken, NJ : Wiley, 2017.
 Description
 Book — 1 online resource.
 Summary

 1. Single Solution Based Metaheuristics.
 2. Populationbased Methods.
 3. Performance Evaluation of Metaheuristics.
 4. Metaheuristics for FACTS Placementâ ¨ and Sizing.
 5. Genetic Algorithmbased Wind Farm Topology Optimization.
 6. Topological Study of Electrical Networks.
 7. Parameter Estimation of Î±Stable Distributions.
 8. SmartGrid and MicroGrid Perspectives.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Héliodore, Frédéric, author.
 London, UK : ISTE, Ltd. ; Hoboken, NJ : John Wiley & Sons, 2017.
 Description
 Book — 1 online resource (1 volume) : illustrations.
 Summary

 1. Single Solution Based Metaheuristics.
 2. Populationbased Methods.
 3. Performance Evaluation of Metaheuristics.
 4. Metaheuristics for FACTS Placementâ ¨ and Sizing.
 5. Genetic Algorithmbased Wind Farm Topology Optimization.
 6. Topological Study of Electrical Networks.
 7. Parameter Estimation of Î±Stable Distributions.
 8. SmartGrid and MicroGrid Perspectives.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
6. Metaheuristics for logistics [2016]
 Deroussi, Laurent, author.
 London : ISTE Ltd. ; Hoboken, NJ : John Wiley & Sons, Inc., 2016.
 Description
 Book — 1 online resource : illustrations
 Summary

 Introduction xi
 Part 1. Basic Notions 1
 Chapter 1. Introductory Problems 3 1.1. The "swing states" problem 3 1.2. Adel and his camels 5 1.3. Sauron's forges 7 1.3.1. Problem 1: The inspection of the forges 8 1.3.2. Problem 2: The production of the deadly weapon 9
 Chapter 2. A Review of Logistic Problems 13 2.1. Some history 13 2.1.1. The FermatTorricelli point 13 2.1.2. The Monge problem 14 2.1.3. The Seven Bridges of Koenigsberg and the Icosian Game 15 2.2. Some polynomial problems 16 2.2.1. The assignment problem 16 2.2.2. The transportation problem 17 2.2.3. The MinimumCost Spanning Tree problem 19 2.3. Packing problems 20 2.3.1. The knapsack problem 20 2.3.2. The bin packing problem 21 2.4. Routing problems 22 2.4.1. The traveling salesman problem 23 2.4.2. The vehicle routing problem (VRP) 24 2.5. Production scheduling problems 24 2.5.1. The flowshop scheduling problem (FSSP)26 2.5.2. The jobshop scheduling problem (JSSP) 29 2.6. Lotsizing problems 31 2.7. Facility location problems 33 2.7.1. The Uncapacitated Plant Location Problem (UPLP) 33 2.7.2. The Dynamic Location Problem (DLP) 35 2.8. Conclusion 36
 Chapter 3. An Introduction to Metaheuristics 37 3.1. Optimization problems 37 3.2. Metaheuristics: basic notions 39 3.2.1. Intensification and diversification 40 3.2.2. Neighborhood systems 40 3.3. Individualbased metaheuristics 41 3.3.1. Local search 41 3.3.2. Simulated annealing 44 3.3.3. The kangaroo Algorithm 46 3.3.4. Iterated local search 48 3.3.5. Tabu Search 49 3.4. Populationbased metaheuristics 50 3.4.1. Evolutionary algorithms 51 3.4.2. The ant colony algorithm 52 3.4.3. Particle Swarm Optimization 53 3.5. Conclusion 55
 Chapter 4. A First Implementation of Metaheuristics 57 4.1. Representing a list of objects 57 4.2. The implementation of a local search 59 4.2.1. The construction of an initial solution 59 4.2.2. Description of basic moves 60 4.2.3. The implementation of stochastic descent (LS) 62 4.3. The implementation of individualbased metaheuristics 64 4.3.1. Simulated annealing (SA) 64 4.3.2. Iterated local search (ILS) 66 4.14. Conclusion 66
 Part 2. Advanced Notions 69
 Chapter 5. The Traveling Salesman Problem 71 5.1. Representing a solution: the twolevel tree structure 71 5.2. Constructing initial solutions 74 5.2.1. A greedy heuristic: nearest neighbor 74 5.2.2. A simplification heuristic: the Christofides algorithm 76 5.3. Neighborhood systems 78 5.3.1. The Lin & Kernighan neighborhood 79 5.3.2. Ejection chain techniques 83 5.4. Some results 86 5.5. Conclusion 88
 Chapter 6. The FlowShop Problem 89 6.1. Representation and assessment of a solution 89 6.2. Construction of the initial solution 90 6.2.1. Simplification heuristics: CDS 91 6.2.2. A greedy heuristic: NEH 94 6.3. Neighborhood systems 97 6.3.1. Improvement of the insertion movements 98 6.3.2. Variabledepth neighborhood search 101 6.4. Results 107 6.5. Conclusion 107
 Chapter 7. Some Elements for Other Logistic Problems 109 7.1. Direct representation versus indirect representation 109 7.2. Conditioning problems 111 7.2.1. The knapsack problem 111 7.2.2. The binpacking problem 112 7.3. Lotsizing problems 114 7.4. Localization problems 115 7.5. Conclusion 117
 Part 3. Evolutions and Current Trends 119
 Chapter 8. Supply Chain Management 121 8.1. Introduction to supply chain management 121 8.2. Horizontal synchronization of the supply chain 122 8.2.1. The beer game 123 8.2.2. The bullwhip effect 125 8.3. Vertical synchronization of a supply chain 126 8.4. An integral approach of the supply chain 127 8.5. Conclusion 129
 Chapter 9. Hybridization and Coupling Using Metaheuristics 131 9.1. Metaheuristics for the optimization of the supply chain 131 9.2. Hybridization of optimization methods 133 9.2.1. Classification of hybrid methods 133 9.2.2. Illustration by example 134 9.2.3. "Metaheuristic/local search" hybridization 135 9.2.4. Metaheuristic hybridization/Exact Methods 135 9.3. Coupling of optimization methods and performance evaluations 138 9.3.1. Double complexity 138 9.3.2. Coupling of optimization method/simulation model 139 9.4. Conclusion 141
 Chapter 10. Flexible Manufacturing Systems 143 10.1. Introduction to the FMS challenges 143 10.2. The jobshop problem with transport 145 10.2.1. Definition of the problem 145 10.3. Proposal for a metaheuristic/simulation coupling 148 10.3.1. Representation of a solution 148 10.3.2. Simulation method 149 10.3.3. Optimization method 152 10.3.4. Results 153 10.4. Workshop layout problem 154 10.4.1. Aggregated model and exact resolution 154 10.4.2. Detailed model and approximate solutions 157 10.5. Conclusion 159
 Chapter 11. Synchronization Problems Based on Vehicle Routings 161 11.1. Inventory routing problem 162 11.1.1. Presentation of the problem 162 11.1.2. Resolution by metaheuristics 166 11.2. The locationrouting problem 167 11.2.1. Definition of the problem 167 11.2.2. Solution with metaheuristics 171 11.3. Conclusion 172
 Chapter 12. Solution to Problems 173 12.1. The swing state problem 173 12.2. Adel and his camels 176 12.2.1. First question 176 12.2.2. Second question 177 12.2.3. Third question 180 12.3. The forges of Sauron 180 12.3.1. The inspection of the forges 180 12.3.2. Production of the lethal weapon 183 Conclusion 185 Bibliography 187 Index 197.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
7. Metaheuristics for logistics [2016]
 Deroussi, Laurent, author.
 London : ISTE Ltd. ; Hoboken, NJ : John Wiley & Sons, Inc., 2016.
 Description
 Book — 1 online resource : illustrations.
 Summary

 Introduction xi
 Part 1. Basic Notions 1
 Chapter 1. Introductory Problems 3 1.1. The "swing states" problem 3 1.2. Adel and his camels 5 1.3. Sauron's forges 7 1.3.1. Problem 1: The inspection of the forges 8 1.3.2. Problem 2: The production of the deadly weapon 9
 Chapter 2. A Review of Logistic Problems 13 2.1. Some history 13 2.1.1. The FermatTorricelli point 13 2.1.2. The Monge problem 14 2.1.3. The Seven Bridges of Koenigsberg and the Icosian Game 15 2.2. Some polynomial problems 16 2.2.1. The assignment problem 16 2.2.2. The transportation problem 17 2.2.3. The MinimumCost Spanning Tree problem 19 2.3. Packing problems 20 2.3.1. The knapsack problem 20 2.3.2. The bin packing problem 21 2.4. Routing problems 22 2.4.1. The traveling salesman problem 23 2.4.2. The vehicle routing problem (VRP) 24 2.5. Production scheduling problems 24 2.5.1. The flowshop scheduling problem (FSSP)26 2.5.2. The jobshop scheduling problem (JSSP) 29 2.6. Lotsizing problems 31 2.7. Facility location problems 33 2.7.1. The Uncapacitated Plant Location Problem (UPLP) 33 2.7.2. The Dynamic Location Problem (DLP) 35 2.8. Conclusion 36
 Chapter 3. An Introduction to Metaheuristics 37 3.1. Optimization problems 37 3.2. Metaheuristics: basic notions 39 3.2.1. Intensification and diversification 40 3.2.2. Neighborhood systems 40 3.3. Individualbased metaheuristics 41 3.3.1. Local search 41 3.3.2. Simulated annealing 44 3.3.3. The kangaroo Algorithm 46 3.3.4. Iterated local search 48 3.3.5. Tabu Search 49 3.4. Populationbased metaheuristics 50 3.4.1. Evolutionary algorithms 51 3.4.2. The ant colony algorithm 52 3.4.3. Particle Swarm Optimization 53 3.5. Conclusion 55
 Chapter 4. A First Implementation of Metaheuristics 57 4.1. Representing a list of objects 57 4.2. The implementation of a local search 59 4.2.1. The construction of an initial solution 59 4.2.2. Description of basic moves 60 4.2.3. The implementation of stochastic descent (LS) 62 4.3. The implementation of individualbased metaheuristics 64 4.3.1. Simulated annealing (SA) 64 4.3.2. Iterated local search (ILS) 66 4.14. Conclusion 66
 Part 2. Advanced Notions 69
 Chapter 5. The Traveling Salesman Problem 71 5.1. Representing a solution: the twolevel tree structure 71 5.2. Constructing initial solutions 74 5.2.1. A greedy heuristic: nearest neighbor 74 5.2.2. A simplification heuristic: the Christofides algorithm 76 5.3. Neighborhood systems 78 5.3.1. The Lin & Kernighan neighborhood 79 5.3.2. Ejection chain techniques 83 5.4. Some results 86 5.5. Conclusion 88
 Chapter 6. The FlowShop Problem 89 6.1. Representation and assessment of a solution 89 6.2. Construction of the initial solution 90 6.2.1. Simplification heuristics: CDS 91 6.2.2. A greedy heuristic: NEH 94 6.3. Neighborhood systems 97 6.3.1. Improvement of the insertion movements 98 6.3.2. Variabledepth neighborhood search 101 6.4. Results 107 6.5. Conclusion 107
 Chapter 7. Some Elements for Other Logistic Problems 109 7.1. Direct representation versus indirect representation 109 7.2. Conditioning problems 111 7.2.1. The knapsack problem 111 7.2.2. The binpacking problem 112 7.3. Lotsizing problems 114 7.4. Localization problems 115 7.5. Conclusion 117
 Part 3. Evolutions and Current Trends 119
 Chapter 8. Supply Chain Management 121 8.1. Introduction to supply chain management 121 8.2. Horizontal synchronization of the supply chain 122 8.2.1. The beer game 123 8.2.2. The bullwhip effect 125 8.3. Vertical synchronization of a supply chain 126 8.4. An integral approach of the supply chain 127 8.5. Conclusion 129
 Chapter 9. Hybridization and Coupling Using Metaheuristics 131 9.1. Metaheuristics for the optimization of the supply chain 131 9.2. Hybridization of optimization methods 133 9.2.1. Classification of hybrid methods 133 9.2.2. Illustration by example 134 9.2.3. "Metaheuristic/local search" hybridization 135 9.2.4. Metaheuristic hybridization/Exact Methods 135 9.3. Coupling of optimization methods and performance evaluations 138 9.3.1. Double complexity 138 9.3.2. Coupling of optimization method/simulation model 139 9.4. Conclusion 141
 Chapter 10. Flexible Manufacturing Systems 143 10.1. Introduction to the FMS challenges 143 10.2. The jobshop problem with transport 145 10.2.1. Definition of the problem 145 10.3. Proposal for a metaheuristic/simulation coupling 148 10.3.1. Representation of a solution 148 10.3.2. Simulation method 149 10.3.3. Optimization method 152 10.3.4. Results 153 10.4. Workshop layout problem 154 10.4.1. Aggregated model and exact resolution 154 10.4.2. Detailed model and approximate solutions 157 10.5. Conclusion 159
 Chapter 11. Synchronization Problems Based on Vehicle Routings 161 11.1. Inventory routing problem 162 11.1.1. Presentation of the problem 162 11.1.2. Resolution by metaheuristics 166 11.2. The locationrouting problem 167 11.2.1. Definition of the problem 167 11.2.2. Solution with metaheuristics 171 11.3. Conclusion 172
 Chapter 12. Solution to Problems 173 12.1. The swing state problem 173 12.2. Adel and his camels 176 12.2.1. First question 176 12.2.2. Second question 177 12.2.3. Third question 180 12.3. The forges of Sauron 180 12.3.1. The inspection of the forges 180 12.3.2. Production of the lethal weapon 183 Conclusion 185 Bibliography 187 Index 197.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Labadie, Nacima, author.
 London : ISTE Ltd. ; Hoboken, NJ : John Wiley & Sons, Inc., 2016.
 Description
 Book — 1 online resource : illustrations.
 Summary

 Notations and Abbreviations ix Introduction xiii
 Chapter 1. General Presentation of Vehicle Routing Problems 1 1.1. Logistics management and combinatorial optimization 1 1.1.1. History of logistics 2 1.1.2. Logistics as a science 5 1.1.3. Combinatorial optimization 5 1.2. Vehicle routing problems 6 1.2.1. Problems in transportation optimization 6 1.2.2. Vehicle routing problems in other contexts 7 1.2.3. Characteristics of vehicle routing problems 7 1.2.4. The capacitated vehicle routing problem 11 1.3. Conclusion 13
 Chapter 2. Simple Heuristics and Local Search Procedures 15 2.1. Simple heuristics 16 2.1.1. Constructive heuristics 16 2.1.2. Twophase methods 19 2.1.3. Bestof approach and randomization 22 2.2. Local search 23 2.2.1. Principle 23 2.2.2. Classical moves 24 2.2.3. Feasibility tests 25 2.2.4. General approach from Vidal et al 28 2.2.5. Multiple neighborhoods 30 2.2.6. Very constrained problems 33 2.2.7. Acceleration techniques 33 2.2.8. Complex moves 36 2.3. Conclusion 37
 Chapter 3. Metaheuristics Generating a Sequence of Solutions 39 3.1. Simulated annealing (SA) 39 3.1.1. Principle 39 3.1.2. Simulated annealing in vehicle routing problems 40 3.2. Greedy randomized adaptive search procedure: GRASP 41 3.2.1. Principle 41 3.2.2. GRASP in vehicle routing problems 43 3.3. Tabu search 44 3.3.1. Principle 44 3.3.2. Tabu search in vehicle routing problems 45 3.4. Variable neighborhood search 47 3.4.1. Principle 47 3.4.2. Variable neighborhood search in vehicle routing problems 49 3.5. Iterated local search 50 3.5.1. Principle 50 3.5.2. Iterated local search in vehicle routing problems 52 3.6. Guided local search 54 3.6.1. Principle 54 3.6.2. Guided local search in vehicle routing problems 55 3.7. Large neighborhood search 56 3.7.1. Principle 56 3.7.2. Large neighborhood search in vehicle routing problems 58 3.8. Transitional forms 59 3.8.1. Evolutionary local search principle 59 3.8.2. Application to vehicle routing problems 60 3.9. Selected examples 61 3.9.1. GRASP for the locationrouting problem 61 3.9.2. Granular tabu search for the CVRP 65 3.9.3. Adaptive large neighborhood search for the pickup and delivery problem with time windows 69 3.10. Conclusion 74
 Chapter 4. Metaheuristics Based on a Set of Solutions 77 4.1. Genetic algorithm and its variants 77 4.1.1. Genetic algorithm 77 4.1.2. Memetic algorithm 79 4.1.3. Memetic algorithm with population management 79 4.1.4. Genetic algorithm and its variants in vehicle routing problems 80 4.2. Scatter search 82 4.2.1. Scatter search principle 82 4.2.2. Scatter search in vehicle routing problems 83 4.3. Path relinking 83 4.3.1. Principle 84 4.3.2. Path relinking in vehicle routing problems 85 4.4. Ant colony optimization 86 4.4.1. Principle 86 4.4.2. ACO in vehicle routing problems 89 4.5. Particle swarm optimization 89 4.5.1. Principle 89 4.5.2. PSO in vehicle routing problems 90 4.6. Other approaches and their use in vehicle routing problems 91 4.7. Selected examples 92 4.7.1. Scatter search for the periodic capacitated arc routing problem 92 4.7.2. PR for the mutidepot periodic VRP 97 4.7.3. Unified genetic algorithm for a wide class of vehicle routing problems 101 4.8. Conclusion 106
 Chapter 5. Metaheuristics Hybridizing Various Components 109 5.1. Hybridizing metaheuristics 109 5.1.1. Principle 110 5.1.2. Application to vehicle routing problems 111 5.1.3. Selected examples 112 5.2. Matheuristics 122 5.2.1. Principle 123 5.2.2. Application to vehicle routing problems 124 5.2.3. Selected examples 128 5.3. Conclusion 144 Conclusion 145 Bibliography 149 Index 167.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
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