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.
Volume I contains 20 chapters and presents the basic principles and current algorithms and methods of well-known swarm intelligence algorithms and efficient improvements from typical particle swarm optimisation (PSO), ant colony optimisation (ACO) and fireworks algorithm (FWA) as well as other swarm intelligence algorithms for swarm robotics. The companion Volume II covers innovations, new algorithms and methods, and volume III covers applications of swarm intelligence.
You might also be interested in Swarm Intelligence Volume 2: Innovation, new 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: Survey of Swarm Intelligence - Ying Tan
Chapter 2: Generalization Ability of Swarm Intelligence Algorithms - Shi Cheng, Quande Qin, Bin Liu, Jiqiang Feng, Xiujuan Lei, Yuhui Shi
Chapter 3: A Unifying Framework for Swarm Intelligence-Based Hybrid Algorithms - Bin Xin, Yipeng Wang, Bo Liu
Chapter 4: Ant Colony Systems for Optimization Problems in Dynamic Environments - Yirui Wang, Shangce Gao, Yuki Todo
Chapter 5: Ant Colony Optimization for Dynamic Combinatorial Optimization Problems - Michalis Mavrovouniotis, Shengxiang Yang
Chapter 6: Comparison of Multidimensional Swarm Embedding Techniques by Potential Fields - Youngha Hwang, Okan K. Ersoy
Chapter 7: Inertia Weight Control Strategies for PSO Algorithms - Ahmad Nickabadi, Reza Safabakhsh, Mohammad Mehdi Ebadzadeh
Chapter 8: Robot path planning using swarms of active particles - Helbert Eduardo Espitia C., Jorge Ivan Sofrony E.
Chapter 9: MAHM: A PSO-based Multiagent Architecture for Hybridisation of Metaheuristics - Givanaldo R. Souza, Elizabeth F. G. Goldbarg, Anne M. P. Canuto, Marco C. Goldbarg, Iloneide C. de O. Ramos
Chapter 10: The Critical State in Particle Swarm Optimisation - Adam Erskine, Thomas Joyce, J. Michael Herrmann
Chapter 11: Bounded Distributed Flocking Control of Nonholonomic Mobile Robots - Thang Nguyen, Hung M. La, Vahid Azim, Thanh-Trung Han
Chapter 12: Swarming in Forestry Environments: Collective Exploration and Network Deployment - Micael S. Couceiro, David Portugal
Chapter 13: Guiding Swarm Behavior by Soft Control - Jing Han, Caiyun Wang
Chapter 14: Agreeing to disagree: synergies between Particle Swarm Optimisation and Complex Networks - Mihai Udrescu, Andrei Lihu
Chapter 15: Ant Colony Algorithms for the Travelling Salesman Problem and the Quadratic Assignment Problem - Nikola Ivkovic
Chapter 16: A Review of Particle Swam Optimization for Multimodal Problems - Jian-Ping Li, Shanxin Yuan, Qing Sheng Li, Bo Li, Yim Fun Hu
Chapter 17: Decentralized Control in Robotic Swarms - Hamed Rezaee, Farzaneh Abdollahi
Chapter 18: PSO in ANN, SVM and Data Clustering - Terje Kristensen, Fabien Guillaume
Chapter 19: Modelling of interaction in Swarm Intelligence focused on Particle Swarm Optimization and Social Networks Optimization - F. Grimaccia, M. Mussetta, A. Niccolai, R. E. Zich
Chapter 20: Coordinating Swarms of Microscopic Agents to Assemble Complex Structures - Bruce J. Maclennan