Chun-Wei Tsai received his Ph.D. degree from the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan in 2009 where he is currently an assistant professor. He has more than 20 years of experience in metaheuristic algorithms and their applications and has served as the secretary general of Taiwan Association of Cloud Computing from 2018 to 2021; as an associate editor for Journal of Internet Technology, IEEE Access, IET Networks, and IEEE Internet of Things Journal since 2014, 2017, 2018, and 2020, respectively. He has also been a member of the Editorial Board of the Elsevier Journal of Network and Computer Applications (JNCA) and Elsevier ICT Express since 2017 and 2021, respectively. His research interests include computational intelligence, data mining, cloud computing, and internet of things. Ming-Chao Chiang received his B.S. degree in Management Science from National Chiao Tung University, Hsinchu, Taiwan, R.O.C. in 1978, and the M.S., M.Phil., and Ph.D. degrees in Computer Science from Columbia University, New York, USA in 1991, 1998, and 1998, respectively. He has over 12 years of experience in the software industry encompassing a wide variety of roles and responsibilities in both large and start-up companies in Taiwan and the USA before joining the faculty of the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C. in 2003, where he is currently a professor. His research interests include image processing, evolutionary computation, and system software.

"The present book provides resources, references and alternative ways of simple and fast solution methods and algorithms. It is organized in such a way that the readers can not only realize most of the metaheuristic algorithms, but also use them to solve real-world problems. The book can be used by students and researchers as a reference for self-study to enter this research domain or by teachers as a reference or textbook for a course.... The ultimate goal of the book is to share with the audience the authors’ experience and know-how on metaheuristic algorithms from the ground up, that is, from the basic ideas to advanced technologies, even for readers who have no background knowledge in artificial intelligence or machine learning." --Haydar Akca, zbMATHOpen

• 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

Chun-Wei Tsai received his Ph.D. degree from the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan in 2009 where he is currently an assistant professor. He has more than 20 years of experience in metaheuristic algorithms and their applications and has served as the secretary general of Taiwan Association of Cloud Computing from 2018 to 2021; as an associate editor for Journal of Internet Technology, IEEE Access, IET Networks, and IEEE Internet of Things Journal since 2014, 2017, 2018, and 2020, respectively. He has also been a member of the Editorial Board of the Elsevier Journal of Network and Computer Applications (JNCA) and Elsevier ICT Express since 2017 and 2021, respectively. His research interests include computational intelligence, data mining, cloud computing, and internet of things. Ming-Chao Chiang received his B.S. degree in Management Science from National Chiao Tung University, Hsinchu, Taiwan, R.O.C. in 1978, and the M.S., M.Phil., and Ph.D. degrees in Computer Science from Columbia University, New York, USA in 1991, 1998, and 1998, respectively. He has over 12 years of experience in the software industry encompassing a wide variety of roles and responsibilities in both large and start-up companies in Taiwan and the USA before joining the faculty of the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C. in 2003, where he is currently a professor. His research interests include image processing, evolutionary computation, and system software.

"The present book provides resources, references and alternative ways of simple and fast solution methods and algorithms. It is organized in such a way that the readers can not only realize most of the metaheuristic algorithms, but also use them to solve real-world problems. The book can be used by students and researchers as a reference for self-study to enter this research domain or by teachers as a reference or textbook for a course.... The ultimate goal of the book is to share with the audience the authors’ experience and know-how on metaheuristic algorithms from the ground up, that is, from the basic ideas to advanced technologies, even for readers who have no background knowledge in artificial intelligence or machine learning." --Haydar Akca, zbMATHOpen

• 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

Chun-Wei Tsai received his Ph.D. degree from the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan in 2009 where he is currently an assistant professor. He has more than 20 years of experience in metaheuristic algorithms and their applications and has served as the secretary general of Taiwan Association of Cloud Computing from 2018 to 2021; as an associate editor for Journal of Internet Technology, IEEE Access, IET Networks, and IEEE Internet of Things Journal since 2014, 2017, 2018, and 2020, respectively. He has also been a member of the Editorial Board of the Elsevier Journal of Network and Computer Applications (JNCA) and Elsevier ICT Express since 2017 and 2021, respectively. His research interests include computational intelligence, data mining, cloud computing, and internet of things. Ming-Chao Chiang received his B.S. degree in Management Science from National Chiao Tung University, Hsinchu, Taiwan, R.O.C. in 1978, and the M.S., M.Phil., and Ph.D. degrees in Computer Science from Columbia University, New York, USA in 1991, 1998, and 1998, respectively. He has over 12 years of experience in the software industry encompassing a wide variety of roles and responsibilities in both large and start-up companies in Taiwan and the USA before joining the faculty of the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C. in 2003, where he is currently a professor. His research interests include image processing, evolutionary computation, and system software.

"The present book provides resources, references and alternative ways of simple and fast solution methods and algorithms. It is organized in such a way that the readers can not only realize most of the metaheuristic algorithms, but also use them to solve real-world problems. The book can be used by students and researchers as a reference for self-study to enter this research domain or by teachers as a reference or textbook for a course.... The ultimate goal of the book is to share with the audience the authors’ experience and know-how on metaheuristic algorithms from the ground up, that is, from the basic ideas to advanced technologies, even for readers who have no background knowledge in artificial intelligence or machine learning." --Haydar Akca, zbMATHOpen

• 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