Network models and optimization : multiobjective genetic algorithm approach
Network models are critical tools in business, management, science and industry. This book presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines
xiv, 692 pages : illustrations ; 24 cm
9781848001800, 9781848001817, 1848001800, 1848001819
195735685
Cover
Copyright
Preface
Contents
1 Multiobjective Genetic Algorithms
1.1 Introduction
1.1.1 General Structure of a Genetic Algorithm
1.1.2 Exploitation and Exploration
1.1.3 Population-based Search
1.1.4 Major Advantages
1.2 Implementation of Genetic Algorithms
1.2.1 GA Vocabulary
1.2.2 Encoding Issue
1.2.3 Fitness Evaluation
1.2.4 Genetic Operators
1.2.5 Handling Constraints
1.3 Hybrid Genetic Algorithms
1.3.1 Genetic Local Search
1.3.2 Parameter Adaptation
1.4 Multiobjective Genetic Algorithms
1.4.1 Basic Concepts of Multiobjective Optimizations
1.4.2 Features and Implementation of Multiobjective GA
1.4.3 Fitness Assignment Mechanism
1.4.4 Performance Measures
References
2 Basic Network Models
2.1 Introduction
2.1.1 Shortest Path Model: Node Selection and Sequencing
2.1.2 Spanning TreeModel: Arc Selection
2.1.3 Maximum Flow Model: Arc Selection and Flow Assignment
2.1.4 Representing Networks
2.1.5 Algorithms and Complexity
2.1.6 NP-Complete
2.1.7 List of NP-complete Problems in Network Design
2.2 Shortest PathModel
2.2.1 Mathematical Formulation of the SPP Models
2.2.2 Priority-based GA for SPPModels
2.2.3 Computational Experiments and Discussions
2.3 Minimum Spanning Tree Models
2.3.1 Mathematical Formulation of the MST Models
2.3.2 PrimPred-based GA for MST Models
2.3.3 Computational Experiments and Discussions
2.4 Maximum Flow Model
2.4.1 Mathematical Formulation
2.4.2 Priority-based GA for MXF Model
2.4.3 Experiments
2.5 Minimum Cost FlowModel
2.5.1 Mathematical Formulation
2.5.2 Priority-based GA for MCF Model
2.5.3 Experiments
2.6 Bicriteria MXF/MCF Model
2.6.1 Mathematical Formulations
2.6.2 Priority-based GA for bMXF/MCF Model
2.6.3 i-awGA for bMXF/MCF Model
2.6.4 Experiments and Discussion
2.7 Summary
References
3 Logistics Network Models
3.1 Introduction
3.2 Basic Logistics Models
3.2.1 Mathematical Formulation of the Logistics Models
3.2.2 Prüfer Number-based GA for the Logistics Models
3.2.3 Numerical Experiments
3.3 Location Allocation Models
3.3.1 Mathematical Formulation of the Logistics Models
3.3.2 Location-based GA for the Location Allocation Models
3.3.3 Numerical Experiments
3.4 Multi-stage Logistics Models
3.4.1 Mathematical Formulation of the Multi-stage Logistics
3.4.2 Priority-based GA for the Multi-stage Logistics
3.4.3 Numerical Experiments
3.5 Flexible Logistics Model
3.5.1 Mathematical Formulation of the Flexible Logistics Model
3.5.2 Direct Path-based GA for the Flexible Logistics Model
3.5.3 Numerical Experiments
3.6 Integrated Logistics Model with Multi-time Period and Inventory
3.6.1 Mathematical Formulation of the Integrated Logistics Model
3.6.2 Extended Priority-based GA for the Integrated Logistics Model
3.6.3 Local Search Technique
3.6.4 Numerical Experiments
3.7 Summary
References
4 Communication Network Models
4.1 Introduction
4.2 Centralized Network Models
4.2.1 Capacitated Multipoint Network Models
4.2.2 Capacitated QoS Network Model
4.3 Backbone Network Model
4.3.1 Pierre and Legault's Approach