A Study on Solving Optimization Problems using Improved Ant Colony Optimization Algorithms

Abstract

The field of study of "Ant Algorithm" is derived from observations of real ant behavior and inspires the design of new algorithms for solving optimization and distributed control problems using these models. The main idea is that it can be used to coordinate a population of artificial agents that can jointly solve computational problems using self-organizing principles that allow for highly coordinated real ant behavior, and that the behavior of ant colonies inspires different kinds of ant algorithms. Will. Such as collection, division of labor, breeding classification and cooperative transportation. In all of these examples, ants coordinate their activities through information, a form of indirect communication that is mediated through environmental modifications. In this article, an overview of the ACO algorithm has been studied, some optimization problems and applications have been reviewed using the ant colony optimization algorithm, and these problems have yielded promising results and have been proposed.