メタヒューリスティック・アルゴリズム・ハンドブック Handbook of Metaheuristic Algorithms : From Fundamental Theories to Advanced Applications (Uncertainty, Computational Techniques, and Decision Intelligence)

Tsai, Chun-Wei/ Chiang, Ming-Chao

Academic Press Inc（2023/06発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 622 p.
• 言語 ENG
• 商品コード 9780443191084
• DDC分類 519.6

Full 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.

Contents

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