Evolution of Ant Colony Optimization Algorithm -- A Brief Literature Review

TL;DR

This paper reviews recent advancements in Ant Colony Optimization, highlighting new applications like multi-objective and continuous optimization, as well as algorithmic improvements such as hybridization and parallel computing.

Contribution

It provides a comprehensive overview of recent developments in ACO algorithms, focusing on both application areas and algorithmic enhancements.

Findings

Expansion to multi-objective optimization

Development of hybrid and parallel ACO algorithms

Application to time-varying NP-hard problems

Abstract

Ant Colony Optimization (ACO) is a metaheuristic proposed by Marco Dorigo in 1991 based on behavior of biological ants. Pheromone laying and selection of shortest route with the help of pheromone inspired development of first ACO algorithm. Since, presentation of first such algorithm, many researchers have worked and published their research in this field. Though initial results were not so promising but recent developments have made this metaheuristic a significant algorithm in Swarm Intelligence. This research presents a brief overview of recent developments carried out in ACO algorithms in terms of both applications and algorithmic developments. For application developments, multi-objective optimization, continuous optimization and time-varying NP-hard problems have been presented. While to review articles based on algorithmic development, hybridization and parallel architectures have been investigated.