Handbook of Metaheuristic Algorithms From Fundamental Theories to Advanced Applications

• Main Authors: Tsai, Chun-Wei, Chiang, Ming-Chao
• Format: eBook
• Language: English
• Published: Chantilly Elsevier Science & Technology 2023; Academic Press
• Edition: 1
• Series: Uncertainty, Computational Techniques, and Decision Intelligence
• Subjects: Metaheuristics
• ISBN: 9780443191084; 0443191085
• DOI: 10.1016/C2022-0-00638-3

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.

Table of Contents:

• 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