Swarm Intelligence -

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Résumé :
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 1 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.
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.

Sommaire:

• 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