The concept of swarm intelligence at first originated from the observation of nature. Through the observation and study of the behaviour of swarms of living creatures as ants colony, bird flocks, bees colony and fish school, inspired by the swarm/social phenomena exhibited by these biological swarms, the swarm of simple individuals through mutual cooperation shows up the emergence phenomena at the level of swarm, that is, "the swarm of simple individuals shows the characteristics of complex intelligent behaviour through cooperation.”
The swarm intelligence algorithms are characterised of simplicity, uncertainty, interactivity, distributed parallelism, robustness, scalability, and self-organisation. At present, the study of swarm intelligence algorithms mainly includes theory, algorithm and application. Its development trends include developing hybrid algorithms, new improved algorithms and theoretical analysis as well as solving large-scale problems (big data application). In general, swarm intelligence algorithms may shed a light on breaking the curse of no free lunches (NFLs), which shows that a deep study might give us enough anticipation motivating more and more researchers to engage in the research of swarm intelligence algorithms and their applications.
Thousands of papers are published each year presenting new algorithms, new improvements and numerous real world applications. This makes it hard for researchers and students to share their ideas with other colleagues; follow up the works from other researchers with common interests; and to follow new developments and innovative approaches. This complete and timely collection fills this gap by presenting the latest research systematically and thoroughly to provide readers with a full view of the field of swarm. Students will learn the principles and theories of typical swarm intelligence algorithms; scholars will get inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions.
Swarm Intelligence 3 Volume set is a timely and comprehensive 3-vol collection covering the principles, new developments, innovations and applications of swarm intelligence algorithms.
Divided in three volumes: Volume 1 covers the basic principles and current algorithms and methods of well-known swarm intelligence algorithms and efficient improvements; Volume 2 presents front-edge research with novel and newly proposed algorithms and methods; Volume 3 presents real-world applications of swarm intelligence algorithms and related evolutionary algorithms.
Dr. Ying Tan is a Professor of Peking University and director of the Computational Intelligence Laboratory at Peking University, China. He is also a Professor at the Faculty of Design, Kyushu University, Japan. He is the inventor of Fireworks Algorithm (FWA).
He serves as Editor-in-Chief of the International Journal of Computational Intelligence and Pattern Recognition, Associate Editor of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Cybernetics, the IEEE Transactions on Neural Networks and Learning Systems, the International Journal of Swarm Intelligence Research, and so on. He also served as guest editor of several referred Journals, including the IEEE/ACM Transactions on Computational Biology and Bioinformatics, Information Science, Neurocomputing, and Natural Computing, etc.
He has been the founder general chair of the ICSI International Conference series since 2010. He won many academic awards, including the 2nd-Class Natural Science Award of China in 2009, Outstanding Chapter Award by Springer, the Best Paper Award of CAAI Transactions on Intelligent Systems, and Innovative Achievement Award of CAAI in 2016. He has published more than 300 papers in refereed journals and conferences, and authored/co-authored 15 books and 30+ book chapters, and 4 invention patents.
With contributions from an international selection of leading researchers, Swarm Intelligence is essential reading for engineers, researchers, professionals and practitioners with interests in swarm intelligence working in the fields of computer science, information technology, artificial intelligence, neural networks, computational intelligence, bioengineering, physics, mathematics, and social sciences.
This information is provisional and will be updated prior to publication