Ebook: Hybrid Metaheuristics
- Tags: Computational Intelligence, Artificial Intelligence (incl. Robotics)
- Series: Studies in Computational Intelligence 434
- Year: 2013
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages 1-21
Front Matter....Pages 1-1
A Unified Taxonomy of Hybrid Metaheuristics with Mathematical Programming, Constraint Programming and Machine Learning....Pages 3-76
Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing....Pages 77-95
Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search....Pages 97-117
Front Matter....Pages 119-119
Hybridizing Cellular GAs with Active Components of Bio-inspired Algorithms....Pages 121-133
Hybridizations of GRASP with Path-Relinking....Pages 135-155
Hybrid Metaheuristics for the Graph Partitioning Problem....Pages 157-185
Hybrid Metaheuristics for Medical Data Classification....Pages 187-217
HydroCM: A Hybrid Parallel Search Model for Heterogeneous Platforms....Pages 219-235
A Multi-thread GRASPxELS for the Heterogeneous Capacitated Vehicle Routing Problem....Pages 237-269
Front Matter....Pages 271-271
The Heuristic (Dark) Side of MIP Solvers....Pages 273-284
Combining Column Generation and Metaheuristics....Pages 285-334
Application of Large Neighborhood Search to Strategic Supply Chain Management in the Chemical Industry....Pages 335-352
A VNS-Based Heuristic for Feature Selection in Data Mining....Pages 353-368
Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives....Pages 369-385
Front Matter....Pages 387-387
A Multi-paradigm Tool for Large Neighborhood Search....Pages 389-414
Front Matter....Pages 415-415
Predicting Metaheuristic Performance on Graph Coloring Problems Using Data Mining....Pages 417-432
Boosting Metaheuristic Search Using Reinforcement Learning....Pages 433-452
Back Matter....Pages 0--1
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.
Content:
Front Matter....Pages 1-21
Front Matter....Pages 1-1
A Unified Taxonomy of Hybrid Metaheuristics with Mathematical Programming, Constraint Programming and Machine Learning....Pages 3-76
Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing....Pages 77-95
Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search....Pages 97-117
Front Matter....Pages 119-119
Hybridizing Cellular GAs with Active Components of Bio-inspired Algorithms....Pages 121-133
Hybridizations of GRASP with Path-Relinking....Pages 135-155
Hybrid Metaheuristics for the Graph Partitioning Problem....Pages 157-185
Hybrid Metaheuristics for Medical Data Classification....Pages 187-217
HydroCM: A Hybrid Parallel Search Model for Heterogeneous Platforms....Pages 219-235
A Multi-thread GRASPxELS for the Heterogeneous Capacitated Vehicle Routing Problem....Pages 237-269
Front Matter....Pages 271-271
The Heuristic (Dark) Side of MIP Solvers....Pages 273-284
Combining Column Generation and Metaheuristics....Pages 285-334
Application of Large Neighborhood Search to Strategic Supply Chain Management in the Chemical Industry....Pages 335-352
A VNS-Based Heuristic for Feature Selection in Data Mining....Pages 353-368
Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives....Pages 369-385
Front Matter....Pages 387-387
A Multi-paradigm Tool for Large Neighborhood Search....Pages 389-414
Front Matter....Pages 415-415
Predicting Metaheuristic Performance on Graph Coloring Problems Using Data Mining....Pages 417-432
Boosting Metaheuristic Search Using Reinforcement Learning....Pages 433-452
Back Matter....Pages 0--1
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