ABSTRACT
The chapter presents one of the well-known nature-inspired optimization method named as ant colony optimization (ACO). The method is inspired from the foraging behavior of some ant species to seek the shorted path between their nest and food source, without using visual cues. This skill of ants is due to the release and deposition of organic compound on their root, known as pheromone trails. Generally, the most of this compound is produced when ants are traveling with food. The concentration of pheromone increases as more ants travel through this path which further helps the following ant colony to take shortest part. Most importantly, the pheromone evaporates after sometime therefore adjacent ants sense the intensity level of compound and follow the highly concentrated pheromone path. This behavior of ant colony effectively modeled in some set of mathematical equations to optimize complex real-life engineering optimization problems. This chapter illustrates the basics of ACO method by following a step-by-step example. Matlab and C++ source codes of ACO algorithm are also provided for hands-on experience.
