Ant Based System: Overview, Modifications and Applications from 1992 to 2022

Abstract

The complexity of real-world problems motivated the researchers to innovate efficient problem-solving techniques. Generally, natural-inspired, bio-inspired, metaheuristics-based evolutionary computation, and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization and non-deterministic polynomial hard (NP-Hard) problems due to their ability to adjust to a variety of conditions. This paper shows an overview of swarm-based algorithms based on Ant behavior. The first algorithm that inspired Ant behavior in the search for food sources was developed in 1992 and tested in solving the TSP problem. Ant Colony Optimization (ACO) is a metaheuristic inspired by some Ant species' pheromone trail laying following behavior. Artificial Ant in ACO is a stochastic solution construction process that uses (artificial) pheromone information that is modified depending on the Ant’s search experience which is possibly accessible in heuristic information to generate solutions for problems. Notable research has been gained since the proposal of the Ant system. These contribute to the creation of high-performance algorithmic variants of a generic algorithmic framework for ACO, its successful application to a wide range of computing difficult problems, and the theoretical understanding of its properties.