Handbook of Metaheuristic Algorithms From Fundamental Theories to Advanced Applications
Handbook 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.
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| Main Authors | , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Chantilly
Elsevier Science & Technology
2023
Academic Press |
| Edition | 1 |
| Series | Uncertainty, Computational Techniques, and Decision Intelligence |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9780443191084 0443191085 |
| DOI | 10.1016/C2022-0-00638-3 |
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| Abstract | Handbook 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. |
|---|---|
| AbstractList | Handbook 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. Handbook 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. |
| Author | Chiang, Ming-Chao Tsai, Chun-Wei |
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| DOI | 10.1016/C2022-0-00638-3 |
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| PublicationDate | 2023 2023-06-01 |
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| PublicationSeriesTitle | Uncertainty, Computational Techniques, and Decision Intelligence |
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| Snippet | Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the... |
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| SubjectTerms | Metaheuristics |
| Subtitle | From Fundamental Theories to Advanced Applications |
| TableOfContents | 5.2.3 Additional functions -- 5.3 Simulation results of SA -- 5.3.1 Simulation results of SA for the one-max problem -- 5.3.2 Simulation results of SA for the deceptive problem -- 5.4 Discussion -- Supplementary source code -- 6 Tabu search -- 6.1 The basic idea of tabu search (TS) -- 6.2 Implementation of TS for the one-max and deceptive problems -- 6.2.1 Declaration of functions and parameters -- 6.2.2 The main loop -- 6.2.3 Additional functions -- 6.3 Simulation results of TS -- 6.3.1 Simulation results of TS for the one-max problem -- 6.3.2 Simulation results of TS for the deceptive problem -- 6.4 Discussion -- Supplementary source code -- 7 Genetic algorithm -- 7.1 The basic idea of genetic algorithm (GA) -- 7.2 Implementation of GA for the one-max and deceptive problems -- 7.2.1 Declaration of functions and parameters -- 7.2.2 The main loop -- 7.2.3 Additional functions -- 7.3 Simulation results of GA -- 7.3.1 Simulation results of GA for the one-max problem -- 7.3.2 Simulation results of GA for the deceptive problem -- 7.4 Discussion -- Supplementary source code -- 8 Ant colony optimization -- 8.1 The basic idea of ant colony optimization (ACO) -- 8.1.1 The ant system (AS) -- 8.1.2 The ant colony system (ACS) -- 8.2 Implementation of ACO for the traveling salesman problem -- 8.2.1 Declaration of functions and parameters -- 8.2.2 The main loop -- 8.2.3 Additional functions -- 8.3 Simulation results of ACO for the traveling salesman problem -- 8.4 Discussion -- Supplementary source code -- 9 Particle swarm optimization -- 9.1 The basic idea of particle swarm optimization (PSO) -- 9.2 Implementation of PSO for the function optimization problem -- 9.2.1 Declaration of functions and parameters -- 9.2.2 The main loop -- 9.2.3 Additional functions -- 9.3 Simulation results of PSO for the function optimization problem -- 9.4 Discussion Supplementary source code -- 10 Differential evolution -- 10.1 The basic idea of differential evolution (DE) -- 10.2 Implementation of DE for the function optimization problem -- 10.2.1 Declaration of functions and parameters -- 10.2.2 The main loop -- 10.2.3 Additional functions -- 10.2.4 Other mutation strategies -- 10.3 Simulation results of DE for the function optimization problem -- 10.4 Discussion -- Supplementary source code -- Part 2 Advanced technologies -- 11 Solution encoding and initialization operator -- 11.1 Encoding of solutions -- 11.2 Initialization operator -- 11.3 Discussion -- Supplementary source code -- 12 Transition operator -- 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 -- 12.3.1 Declaration of functions and parameters -- 12.3.2 The main loop -- 12.3.3 Additional functions -- 12.3.4 Adding new crossover operators -- 12.4 Simulation results of GA for the TSP with different crossover operators -- 12.5 Discussion -- Supplementary source code -- 13 Evaluation and determination operators -- 13.1 Evaluation operator -- 13.2 Determination operator -- 13.2.1 Determination operator for single-solution-based metaheuristic algorithms -- 13.2.2 Determination operator for population-based metaheuristic algorithms -- 13.3 Schema theorem -- 13.3.1 Selection and fitness function for the schema -- 13.3.2 Crossover for the schema -- 13.3.3 Mutation for the schema -- 13.3.4 A simple example of schema theory -- 13.4 Fitness landscape analysis -- 13.5 Discussion -- 14 Parallel metaheuristic algorithm -- 14.1 The basic idea of the parallel metaheuristic algorithm -- 14.1.1 Single-solution-based parallel metaheuristic algorithms -- 14.1.2 Population-based parallel metaheuristic algorithms 14.2 Implementation of parallel GA for the TSP -- 14.2.1 Declaration of functions and parameters -- 14.2.2 The main loop -- 14.2.3 Additional functions -- 14.3 Simulation results of parallel GA for the TSP -- 14.4 Discussion -- Supplementary source code -- 15 Hybrid metaheuristic and hyperheuristic algorithms -- 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 -- 15.3.1 Declaration of functions and parameters -- 15.3.2 The main loop of HGA -- 15.3.3 Additional functions of HGA -- 15.3.4 The declaration of functions and parameters of SA -- 15.3.5 The main loop of SA -- 15.3.6 Additional functions of SA -- 15.4 Simulation results of the hybrid heuristic algorithm for the TSP -- 15.5 Discussion -- Supplementary source code -- 16 Local search algorithm -- 16.1 The basic idea of local search -- 16.1.1 Iterating with different solutions -- 16.1.2 Changing the landscape of the problem -- 16.1.3 Accepting non-improving neighbors -- 16.1.4 k-opt -- 16.2 Metaheuristic algorithm with local search -- 16.3 Implementation of GA with 2-opt for the TSP -- 16.3.1 Declaration of functions and parameters -- 16.3.2 The main loop -- 16.3.3 Additional functions -- 16.4 Simulation results of GA with 2-opt for the TSP -- 16.5 Discussion -- Supplementary source code -- 17 Pattern reduction -- 17.1 The basic idea of pattern reduction -- 17.2 Implementation of PREGA for clustering problems -- 17.2.1 Declaration of functions and parameters -- 17.2.2 The main loop -- 17.2.3 Additional functions -- 17.3 Simulation results of PREGA for clustering problems -- 17.4 Related work -- 17.5 Discussion -- Supplementary source code -- 18 Search economics -- 18.1 The basic idea of search economics -- 18.1.1 The resource arrangement operator Front Cover -- Handbook of Metaheuristic Algorithms -- Copyright -- Contents -- 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 1 Fundamentals -- 1 Introduction -- 1.1 Why metaheuristic algorithms -- 1.2 Organization of this book -- 2 Optimization problems -- 2.1 Problem definition -- 2.2 Combinatorial optimization problems -- 2.2.1 The one-max and 0-1 knapsack problems -- 2.2.2 The B2D and deceptive problems -- 2.2.3 The traveling salesman problem (TSP) -- 2.3 Continuous optimization problems -- 2.3.1 The single-objective optimization problem -- 2.3.2 The multi-objective optimization problem -- 2.4 Summary -- 3 Traditional methods -- 3.1 Exhaustive search (ES) -- 3.1.1 The basic idea of ES -- 3.1.2 Implementation of ES for the one-max problem -- 3.1.3 Discussion of ES -- 3.2 Hill climbing (HC) -- 3.2.1 The basic idea of HC -- 3.2.2 Implementation of HC for the one-max problem -- 3.2.2.1 Main function -- 3.2.2.2 Search function of HC -- 3.2.2.2.1 Declaration of parameters and functions -- 3.2.2.2.2 The main loop -- 3.2.2.2.3 Additional functions -- 3.2.2.3 Library function -- 3.2.3 Discussion of HC -- 3.3 Comparisons between ES and HC -- 3.3.1 Simulation results of ES and HC for the one-max problem -- 3.3.2 Simulation results of ES and HC for the deceptive problem -- 3.4 Summary of ES and HC -- Supplementary source code -- 4 Metaheuristic algorithms -- 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 -- 5 Simulated annealing -- 5.1 The basic idea of simulated annealing (SA) -- 5.2 Implementation of SA for the one-max and deceptive problems -- 5.2.1 Declaration of functions and parameters -- 5.2.2 The main loop 18.1.2 The vision search operator -- 18.1.3 The marketing research operator -- 18.2 Implementation of SE for the one-max problem -- 18.2.1 Declaration of functions and parameters -- 18.2.2 The main loop -- 18.2.3 Additional functions -- 18.3 Simulation results of SE for the one-max problem -- 18.4 Discussion -- Supplementary source code -- 19 Advanced applications -- 19.1 Data clustering -- 19.1.1 Problem description and definition -- 19.1.2 Solution encoding -- 19.1.3 Metaheuristic algorithm for data clustering -- 19.2 Cluster-head selection -- 19.2.1 Problem description and definition -- 19.2.2 Solution encoding -- 19.2.3 Metaheuristic algorithm for cluster-head selection -- 19.3 Traffic light control -- 19.3.1 Problem description and definition -- 19.3.2 Solution encoding -- 19.3.3 Metaheuristic algorithm for traffic light control -- 19.4 Hyperparameter optimization -- 19.4.1 Problem description and definition -- 19.4.2 Solution encoding -- 19.4.3 Metaheuristic algorithm for hyperparameter optimization -- 19.5 Convolutional neural network filter pruning -- 19.5.1 Problem description and definition -- 19.5.2 Solution encoding -- 19.5.3 Metaheuristic algorithm for convolutional neural network pruning -- 19.6 Discussion -- 20 Conclusion and future research directions -- 20.1 Conclusion -- 20.2 Future research directions -- A Interpretations and analyses of simulation results -- A.1 Interpretations of metaheuristics -- A.1.1 Quality of the end result -- A.1.2 Convergence curves -- A.1.3 Number of evaluations and computation time -- A.2 Analyses of metaheuristics -- A.2.1 Impact of parameters and operators -- A.2.2 Complexity and statistical analyses -- A.3 Discussion -- Supplementary source code -- B Implementation in Python -- Supplementary source code -- References -- Index -- Back Cover |
| Title | Handbook of Metaheuristic Algorithms |
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