Optimization techniques are stimulated by Swarm Intelligence wherever the target is to get a decent competency of a problem. The knowledge of the behavior of animals or insects has a variety of models in Swarm Intelligence. Swarm Intelligence has become a potential technique for evolving many robust optimization problems. Researchers have developed various algorithms by modeling the behaviors of the different swarm of animals or insects. This paper explores three existing meta-heuristic methods named as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO) and Bat Algorithm (BA). Ant Colony Optimization was stimulated by the nature of ants. Bee Colony Optimization was inspired by the plundering behavior of honey bees. Bat Algorithm was emerged on the echolocation characteristics of micro bats. This study analyzes the problem-solving behavior of groups of relatively simple agents wherein local interactions among agents, are either directly or indirectly through the environment. The scope of this paper is to explore the characteristics of swarm intelligence as well as its advantages, limitations and application areas, and subsequently, to explore the behavior of ants, bees and micro bats along with its most popular variants. Furthermore, the behavioral comparison of these three techniques has been analyzed and tried to point out which technique is better for optimization among them in Swarm Intelligence. From this, the paper can help to understand the most appropriate technique for optimization according to their behavior.
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