Description
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.- 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
Table of Contents
PART 1 Fundamentals 1. Introduction2. Optimization problems3. Traditional methods4. Metaheuristic algorithms5. Simulated annealing6. Tabu search7. Genetic algorithm8. Ant colony optimization9. Particle swarm optimization10. Differential evolutionPART 2 Advanced technologies11. Solution encoding and initialization operator12. Transition operator13. Evaluation and determination operators14. Parallel metaheuristic algorithm15. Hybrid metaheuristic and hyperheuristic algorithms16. Local search algorithm17. Pattern reduction18. Search economics19. Advanced applications20. Conclusion and future research directionsA. Interpretations and analyses of simulation resultsB. Implementation in Python
