Swarm Intelligence (SI) is a major and complex area within computational intelligence, centered on studying the collective behaviors of swarms in nature or society to tackle problems that traditional methods cannot solve. With thousands of new algorithms, improvements, and real-world applications published each year, it can be difficult for researchers and students to keep up with the latest developments and share ideas. This comprehensive and timely collection addresses that challenge by systematically presenting the most recent research, giving readers a complete overview of the field. Students will learn the foundational principles and theories of common swarm intelligence algorithms, scholars will find new research directions, and practitioners will discover practical methods and guidance for their applications.
Volume 1 features 20 chapters covering the fundamental principles and current algorithms in swarm intelligence, including efficient improvements to well-known methods such as particle swarm optimization (PSO), ant colony optimization (ACO), and the fireworks algorithm (FWA), as well as other algorithms relevant to swarm robotics.
With contributions from leading international experts, Swarm Intelligence is essential reading for engineers, researchers, professionals, and practitioners interested in this field.
