Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications (Uncertainty, Computational Techniques, and Decision Intelligence) - Softcover

Über die Autorinnen und Autoren

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.

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Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for 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.

provides a brief introduction to metaheuristic algorithms from the ground up; from the basic ideas to advanced solutions. Readers will be able to understand metaheuristic algorithms and how to use them to solve problems across a wide range of scientific and engineering fields. 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, expanding the gap between theory and implementation. This book can also help students and researchers to construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains.