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 characterized 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.
Volume II includes 17 chapters and covers front-edge research with novel and newly proposed algorithms and methods of swarm intelligence. The companion volume I covers principles, current algorithms and methods of swarm intelligence; and volume III covers applications of swarm intelligence.
You might also be interested in Swarm Intelligence Volume 1: Principles, current algorithms and methods, Swarm Intelligence Volume 3: Applications or Swarm Intelligence 3 Volume set.
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
Chapter 1: Standard Fireworks Algorithm 2017 - Yifeng Li, Ying Tan
Chapter 2: Guided Fireworks Algorithm Applied to Multilevel Image Thresholding - Eva Tuba, Milan Tuba
Chapter 3: Credit Card Number Encryption using Firework based Key Generation - Sreelaja N. K, Sreeja N. K
Chapter 4: S. T. (Shafiabady Teshnehlab) Optimization Algorithm - Niusha Shafiabady
Chapter 5: Predator-Prey Optimization with Heterogeneous Swarms - Arlindo Silva, Ana Paula Neves
Chapter 6: A Novel Modified Ant Lion Optimizer Algorithm: Extension to Proposed 4D-TC - Subhabrata Banerjee, Sudipta Chattopadhyay
Chapter 7: Push-Pull Glowworm Swarm Optimization Algorithm for Multimodal Functions - Shashi Barak, Varun J. Kompella, Krishnanand N. Kaipa, Debasish Ghose
Chapter 8: Firefly Algorithm and Its Applications - Xiujuan Lei, Yuchen Zhang, Jie Zhao, Shi Cheng, Ying Tan
Chapter 9: The Optimization Dialectical Method for the Multiple Sequences Alignment Problem - Rodrigo Gomes de Souza, Wellington Pinheiro dos Santos, Manoel Eusebio de Lima
Chapter 10: A New Binary Moth-Flame Optimization Algorithm (BMFOA)- Development and Application to Solve Unit Commitment Problem - Srikanth Reddy K, Lokesh Panwar, B. K. Panigrahi, Rajesh Kumar
Chapter 11: Binary Whale Optimization Algorithm for Unit Commitment Problem in Power System Operation - Srikanth Reddy K, Lokesh Panwar, B. K. Panigrahi, Rajesh Kumar
Chapter 12: Real Coded Grey Wolf Optimisation Algorithm for Progressive Thermal Power System Unit Commitment - S. Ganesan, M. Abirami, J. Rameshkumar, S. Subramanian
Chapter 13: Application of Grey Wolf Optimization in Fuzzy Controller Tuning for Servo Systems - Radu-Codrut David, Radu-Emil Precup, Stefan Preitl, Alexandra-Iulia Szedlak-Stinean, Lucian-Ovidiu Fedorovici
Chapter 14: Smart Swarm Inspired Algorithms for Microwave Imaging Problems - Massimo Donelli
Chapter 15: Interactive Chaotic Evolution - Yan Pei
Chapter 16: Symbiotic Organisms Search Algorithm for Static and Dynamic Transmission Expansion Planning - Sumit Verma, V. Mukherjee
Chapter 17: Inclined Planes system Optimisation (IPO) and its applications in data mining and system identification - Hamed Abdy, Seyed Hamid Zahiri