ABSTRACT
Swarm intelligence is a contemporary computational and behavioral metaphor for solving search and optimization problems that take collective biological patterns provided by social insects like ants, bees, wasps, etc. and other animal-based societies like fish, birds, etc. as stimulus to model algorithmic solutions. This chapter explains four superlative and intriguing swarm intelligence techniques, namely, cuckoo Search, Glowworm, WASP and Fish Swarm along with their variants, mathematical proof, and applicability in real-world applications. Each algorithm uncovers the nature of collective intelligence that transpires through the cooperation of large number of homogeneous agents in the environment to solve combinatorial problems. The representation of concepts within each strategy is done firstly by identifying the analogy from swarm biology, which is then understood to confer models using pseudo-codes and proofs. A brief description about the tuning of models for practical applications to which it has been applied by researchers in various issues is also provided.
