Swarm Intelligence (SI) is one of the most important and challenging paradigms under the umbrella of computational intelligence. It focuses on the research of collective behaviours of a swarm in nature and/or social phenomenon to solve complicated and difficult problems which cannot be handled by traditional approaches. 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 be inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions.
Volume 1 contains 20 chapters presenting the basic principles and current algorithms and methods of well-known swarm intelligence algorithms and efficient improvements from typical particle swarm optimization (PSO), ant colony optimization (ACO) and fireworks algorithm (FWA) as well as other swarm intelligence algorithms for swarm robotics.
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.
Table of Contents- Chapter 1: Survey of swarm intelligence
- Chapter 2: Generalization ability of swarm intelligence algorithms
- Chapter 3: A unifying framework for swarm intelligence-based hybrid algorithms
- Chapter 4: Ant colony systems for optimization problems in dynamic environments
- Chapter 5: Ant colony optimization for dynamic combinatorial optimization problems
- Chapter 6: Comparison of multidimensional swarm embedding techniques by potential fields
- Chapter 7: Inertia weight control strategies for PSO algorithms
- Chapter 8: Robot path planning using swarms of active particles
- Chapter 9: MAHM: a PSO-based multiagent architecture for hybridisation of metaheuristics
- Chapter 10: The critical state in particle swarm optimisation
- Chapter 11: Bounded distributed flocking control of nonholonomic mobile robots
- Chapter 12: Swarming in forestry environments: collective exploration and network deployment
- Chapter 13: Guiding swarm behavior by soft control
- Chapter 14: Agreeing to disagree: synergies between particle swarm optimisation and complex networks
- Chapter 15: Ant colony algorithms for the travelling salesman problem and the quadratic assignment problem
- Chapter 16: A review of particle swarm optimization for multimodal problems
- Chapter 17: Decentralized control in robotic swarms
- Chapter 18: PSO in ANN, SVM and data clustering
- Chapter 19: Modelling of interaction in swarm intelligence focused on particle swarm optimization and social networks optimization
- Chapter 20: Coordinating swarms of microscopic agents to assemble complex structures
AuthorsYing Tan Professor. Peking University, Computational Intelligence Laboratory, China. Professor. Kyushu University, Faculty of Design, Japan.
Ying Tan is a full professor, PhD advisor, 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 serves as Editor-in-Chief of the International Journal of Computational Intelligence and Pattern Recognition (IJCIPR), and is Associate Editor of IEEE Transactions on Evolutionary Computation (TEC), IEEE Transactions on Cybernetics (CYB), IEEE Transactions on Neural Networks and Learning Systems (NNLS), International Journal of Swarm Intelligence Research (IJSIR), and International Journal of Artificial Intelligence (IJAI). He has been the founder general chair of the ICSI International Conference series since 2010, is the inventor of the Fireworks Algorithm (FWA), and has published extensively in this field.