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Swarm intelligence refers to a class of algorithms inspired by natural phenomena and social behaviours, playing a pivotal role in contemporary artificial intelligence methods. While extensive research has been conducted on the design and application of swarm intelligence algorithms, studies focusing on their theoretical foundations remain limited. Notably, there is a significant gap in research regarding key aspects such as mathematical models, convergence analysis, and time complexity.

This book addresses these gaps by offering a comprehensive exploration of the theoretical underpinnings of swarm intelligence algorithms. It provides in-depth discussions on the mathematical models, convergence behaviours, and time complexity of algorithms such as Particle Swarm Optimization, Ant Colony Optimization, Pigeon-inspired Optimization, Brainstorming Algorithm, and Fireworks Algorithm.