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Handbook of Metaheuristic Algorithms
From Fundamental Theories to Advanced Applications
Presents a unified framework for metaheuristics to describe well-known metaheuristic algorithms and their variants
Chun-Wei Tsai (Author), Ming-Chao Chiang (Author)
9780443191084, Elsevier Science
Paperback / softback, published 5 June 2023
622 pages
22.9 x 15.2 x 3.8 cm, 1.02 kg
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.
PART 1 Fundamentals 1. Introduction 2. Optimization problems 3. Traditional methods 4. Metaheuristic algorithms 5. Simulated annealing 6. Tabu search 7. Genetic algorithm 8. Ant colony optimization 9. Particle swarm optimization 10. Differential evolution PART 2 Advanced technologies 11. Solution encoding and initialization operator 12. Transition operator 13. Evaluation and determination operators 14. Parallel metaheuristic algorithm 15. Hybrid metaheuristic and hyperheuristic algorithms 16. Local search algorithm 17. Pattern reduction 18. Search economics 19. Advanced applications 20. Conclusion and future research directions A. Interpretations and analyses of simulation results B. Implementation in Python
Subject Areas: Expert systems / knowledge-based systems [UYQE], Artificial intelligence [UYQ], Enterprise software [UFL], Technology: general issues [TB]
