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Handbook of Metaheuristic Algorithms
From Fundamental Theories to Advanced Applications
- 1st Edition - May 30, 2023
- Authors: Chun-Wei Tsai, Ming-Chao Chiang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 9 1 0 8 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 9 1 0 9 - 1
Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including… Read more
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Request a sales quoteHandbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains.
Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.
- Presents a unified framework for metaheuristics and describes well-known algorithms and their variants
- Introduces fundamentals and advanced topics for solving engineering optimization problems, e.g., scheduling problems, sensors deployment problems, and clustering problems
- Includes source code based on the unified framework for metaheuristics used as examples to show how TS, SA, GA, ACO, PSO, DE, parallel metaheuristic algorithm, hybrid metaheuristic, local search, and other advanced technologies are realized in programming languages such as C++ and Python
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of figures
- List of tables
- List of algorithms
- List of listings
- About the authors
- Chun-Wei Tsai (1978–)
- Ming-Chao Chiang (1956–)
- Preface
- Part One: Fundamentals
- Chapter One: Introduction
- Abstract
- 1.1. Why metaheuristic algorithms
- 1.2. Organization of this book
- References
- Chapter Two: Optimization problems
- Abstract
- 2.1. Problem definition
- 2.2. Combinatorial optimization problems
- 2.3. Continuous optimization problems
- 2.4. Summary
- References
- Chapter Three: Traditional methods
- Abstract
- 3.1. Exhaustive search (ES)
- 3.2. Hill climbing (HC)
- 3.3. Comparisons between ES and HC
- 3.4. Summary of ES and HC
- Supplementary source code
- Chapter Four: Metaheuristic algorithms
- Abstract
- 4.1. What is a metaheuristic algorithm?
- 4.2. A unified framework for metaheuristic algorithms
- 4.3. Comparisons of metaheuristics with exhaustive and greedy search
- References
- Chapter Five: Simulated annealing
- Abstract
- 5.1. The basic idea of simulated annealing (SA)
- 5.2. Implementation of SA for the one-max and deceptive problems
- 5.3. Simulation results of SA
- 5.4. Discussion
- Supplementary source code
- References
- Chapter Six: Tabu search
- Abstract
- 6.1. The basic idea of tabu search (TS)
- 6.2. Implementation of TS for the one-max and deceptive problems
- 6.3. Simulation results of TS
- 6.4. Discussion
- Supplementary source code
- References
- Chapter Seven: Genetic algorithm
- Abstract
- 7.1. The basic idea of genetic algorithm (GA)
- 7.2. Implementation of GA for the one-max and deceptive problems
- 7.3. Simulation results of GA
- 7.4. Discussion
- Supplementary source code
- References
- Chapter Eight: Ant colony optimization
- Abstract
- 8.1. The basic idea of ant colony optimization (ACO)
- 8.2. Implementation of ACO for the traveling salesman problem
- 8.3. Simulation results of ACO for the traveling salesman problem
- 8.4. Discussion
- Supplementary source code
- References
- Chapter Nine: Particle swarm optimization
- Abstract
- 9.1. The basic idea of particle swarm optimization (PSO)
- 9.2. Implementation of PSO for the function optimization problem
- 9.3. Simulation results of PSO for the function optimization problem
- 9.4. Discussion
- Supplementary source code
- References
- Chapter Ten: Differential evolution
- Abstract
- 10.1. The basic idea of differential evolution (DE)
- 10.2. Implementation of DE for the function optimization problem
- 10.3. Simulation results of DE for the function optimization problem
- 10.4. Discussion
- Supplementary source code
- References
- Part Two: Advanced technologies
- Chapter Eleven: Solution encoding and initialization operator
- Abstract
- 11.1. Encoding of solutions
- 11.2. Initialization operator
- 11.3. Discussion
- Supplementary source code
- References
- Chapter Twelve: Transition operator
- Abstract
- 12.1. Why use different transition operators
- 12.2. Different transition operators of GA for solving the TSP
- 12.3. Implementation of GA for the TSP with different crossover operators
- 12.4. Simulation results of GA for the TSP with different crossover operators
- 12.5. Discussion
- Supplementary source code
- References
- Chapter Thirteen: Evaluation and determination operators
- Abstract
- 13.1. Evaluation operator
- 13.2. Determination operator
- 13.3. Schema theorem
- 13.4. Fitness landscape analysis
- 13.5. Discussion
- References
- Chapter Fourteen: Parallel metaheuristic algorithm
- Abstract
- 14.1. The basic idea of the parallel metaheuristic algorithm
- 14.2. Implementation of parallel GA for the TSP
- 14.3. Simulation results of parallel GA for the TSP
- 14.4. Discussion
- Supplementary source code
- References
- Chapter Fifteen: Hybrid metaheuristic and hyperheuristic algorithms
- Abstract
- 15.1. The basic idea of the hybrid metaheuristic algorithm
- 15.2. The basic idea of the hyperheuristic algorithm
- 15.3. Implementation of the hybrid heuristic algorithm for the TSP
- 15.4. Simulation results of the hybrid heuristic algorithm for the TSP
- 15.5. Discussion
- Supplementary source code
- References
- Chapter Sixteen: Local search algorithm
- Abstract
- 16.1. The basic idea of local search
- 16.2. Metaheuristic algorithm with local search
- 16.3. Implementation of GA with 2-opt for the TSP
- 16.4. Simulation results of GA with 2-opt for the TSP
- 16.5. Discussion
- Supplementary source code
- References
- Chapter Seventeen: Pattern reduction
- Abstract
- 17.1. The basic idea of pattern reduction
- 17.2. Implementation of PREGA for clustering problems
- 17.3. Simulation results of PREGA for clustering problems
- 17.4. Related work
- 17.5. Discussion
- Supplementary source code
- References
- Chapter Eighteen: Search economics
- Abstract
- 18.1. The basic idea of search economics
- 18.2. Implementation of SE for the one-max problem
- 18.3. Simulation results of SE for the one-max problem
- 18.4. Discussion
- Supplementary source code
- References
- Chapter Nineteen: Advanced applications
- Abstract
- 19.1. Data clustering
- 19.2. Cluster-head selection
- 19.3. Traffic light control
- 19.4. Hyperparameter optimization
- 19.5. Convolutional neural network filter pruning
- 19.6. Discussion
- References
- Chapter Twenty: Conclusion and future research directions
- Abstract
- 20.1. Conclusion
- 20.2. Future research directions
- References
- Appendix A: Interpretations and analyses of simulation results
- A.1. Interpretations of metaheuristics
- A.2. Analyses of metaheuristics
- A.3. Discussion
- Supplementary source code
- References
- Appendix B: Implementation in Python
- Supplementary source code
- References
- References
- Index
- No. of pages: 622
- Language: English
- Edition: 1
- Published: May 30, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780443191084
- eBook ISBN: 9780443191091
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Chun-Wei Tsai
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