ABSTRACT
Swarm intelligence (SI) is the developing field of biologically inspired artificial intelligence. It is established on the behavior models of social insects such as bees, cockroaches, wasps, termites, etc. A swarm is a configuration of tens of thousands of individuals that have chosen of their own will to converge on a common goal. Miscellaneous swarm intelligence techniques have been covering multiple application domains such as image processing, power system, process controlling, data analysis, etc. The techniques discussed in this chapter have been made applicable for various domains by modifying the objective functions to fit their particular application to solve various real-world problems. The chapter will enlist various SI techniques like the Termite Hill algorithm, Cockroach Swarm Optimization, the Monkey Search algorithm, the Bumblebee algorithm, the Social Spider Optimization algorithm, Cat Swarm Optimization, Intelligent Water Drop, Dolphin Echolocation, biogeography-based optimization, the Paddy Field algorithm, the Weightless Swarm algorithm, and Eagle Strategy. The main objective of this chapter is to highlight the SI techniques along with their brief explanation, highlighting their working methodology, algorithms, and mathematical proofs.
