Ant Colony Optimization—Recent Variants, Application and Perspectives

The purpose of this chapter is to present a comprehensive grasp of ant colony optimization (ACO) algorithms and how they are used to advance intelligent systems. It provides information on how ant colony optimization methods work and how many different engineering issues can be solved using them. It creates a link to close the gap between publications that describe the ACO algorithm approach and more conventional books that are devoted to ACO algorithm research. ACO is a probabilistic approach for generating solution to the computational issues that imitate ant foraging behaviour. The key feature of ACO is that artificial ants are permitted to change a pheromone trail on a graph during each iteration. Other ants can utilize these pheromone trails to determine their next course of action, and over time, they develop into a kind of map that directs the ants to the best options. Since its conception, ACO is applied to resolve a variety of optimization issues, such as load balancing, gene selection and the knapsack problem, since its conception. ACO has received a great deal of attention in the fields of computer science and operations research and has demonstrated promising results in the optimization of large-scale problems. People in specialized sectors such as biomedical engineering, as well as engineers, mathematicians and computer scientists, show a great deal of interest in such sophisticated algorithms. Understanding ACO algorithms will shed light on how algorithms operate in general and explain how these algorithms generate solutions to various problems.