Screen LibraryMetaheuristics /
Metaheuristics exhibit desirable properties like simplicity, easy parallelizability, and ready applicability to different types of optimization problems. After a comprehensive introduction to the field, the contributed chapters in this book include explanations of the main metaheuristics techniques,...
Saved in:
| Group Author: | |
|---|---|
| Published: |
Springer International Publishing,
|
| Publisher Address: | Cham : |
| Publication Dates: | [2016] |
| Literature type: | Book |
| Language: | English |
| Subjects: | |
| Summary: |
Metaheuristics exhibit desirable properties like simplicity, easy parallelizability, and ready applicability to different types of optimization problems. After a comprehensive introduction to the field, the contributed chapters in this book include explanations of the main metaheuristics techniques, including simulated annealing, tabu search, evolutionary algorithms, artificial ants, and particle swarms, followed by chapters that demonstrate their applications to problems such as multiobjective optimization, logistics, vehicle routing, and air traffic management. The authors are leading rese |
| Item Description: | 5 A Two-Phase Iterative Search Procedure: The GRASP Method. |
| Carrier Form: | xvi, 489 pages : illustrations (some color) ; 25 cm |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9783319454016 3319454013 |
| Index Number: | QA75 |
| CLC: | TP301.6 |
| Call Number: | TP301.6/M587-1 |
| Contents: |
Contributors; 1 Introduction; 1.1 ``Hard'' Optimization; 1.2 Source of the Effectiveness of Metaheuristics; 1.2.1 Trapping of a ``Classical'' Iterative Algorithm in a Local Minimum; 1.2.2 Capability of Metaheuristics to Extract Themselves from a Local Minimum; 1.3 Principles of the Most Widely Used Metaheuristics; 1.3.1 Simulated Annealing; 1.3.2 The Tabu Search Method; 1.3.3 Genetic Algorithms and Evolutionary Algorithms; 1.3.4 Ant Colony Algorithms; 1.3.5 Other Metaheuristics; 1.4 Extensions of Metaheuristics; 1.4.1 Adaptation for Problems with Continuous Variables. 1.4.2 Multiobjective Optimization1.4.3 Hybrid Methods; 1.4.4 Multimodal Optimization; 1.4.5 Parallelization; 1.5 Place of Metaheuristics in a Classification of Optimization Methods; 1.6 Applications of Metaheuristics; 1.7 An Open Question: The Choice of a Metaheuristic; 1.8 Outline of the Book; References; 2 Simulated Annealing; 2.1 Introduction; 2.2 Presentation of the Method; 2.2.1 Analogy Between an Optimization Problem and Some Physical Phenomena; 2.2.2 Real Annealing and Simulated Annealing; 2.2.3 Simulated Annealing Algorithm; 2.3 Theoretical Approaches. 2.3.1 Theoretical Convergence of Simulated Annealing2.3.2 Configuration Space; 2.3.3 Rules of Acceptance; 2.3.4 Program of Annealing; 2.4 Parallelization of the Simulated Annealing Algorithm; 2.5 Some Applications; 2.5.1 Benchmark Problems of Combinatorial Optimization; 2.5.2 Layout of Electronic Circuits; 2.5.3 Search for an Equivalent Schema in Electronics; 2.5.4 Practical Applications in Various Fields; 2.6 Advantages and Disadvantages of the Method; 2.7 Simple Practical Suggestions for Beginners; 2.8 Modeling of Simulated Annealing Through the Markov Chain Formalism. 2.8.1 Asymptotic Behavior of Homogeneous Markov Chains2.8.2 Choice of Annealing Parameters; 2.8.3 Modeling of the Simulated Annealing Algorithm by Inhomogeneous Markov Chains; 2.9 Annotated Bibliography; References; 3 Tabu Search; 3.1 Introduction; 3.2 The Quadratic Assignment Problem; 3.2.1 Example; 3.3 Basic Tabu Search; 3.3.1 Neighborhood; 3.3.2 Moves and Neighborhoods; 3.3.3 Neighborhood Evaluation; 3.3.4 Neighborhood Limitation: Candidate List; 3.3.5 Neighborhood Extension: Ejection Chains; 3.4 Short-Term Memory; 3.4.1 Hash Table; 3.4.2 Tabu List; 3.4.3 Duration of Tabu Conditions. 3.4.4 Aspiration Conditions3.5 Long-Term Memory; 3.5.1 Frequency-Based Memory; 3.5.2 Forced Moves; 3.6 Convergence of Tabu Search; 3.7 Conclusion; 3.8 Annotated Bibliography; References; 4 Variable Neighborhood Search; 4.1 Introduction; 4.2 Description of the Algorithm; 4.2.1 Local Search; 4.2.2 Diversification of the Search; 4.2.3 The Variable Neighborhood Search; 4.3 Illustration and Extensions; 4.3.1 Finding Extremal Graphs with VNS; 4.3.2 Improving the k-Means Algorithm; 4.3.3 Using VNS for Continuous Optimization Problems; 4.4 Conclusion; 4.5 Annotated Bibliography; References. |