Evolution of Ant Colony Optimization Algorithm -- A Brief Literature Review
This is an incremental review summarizing existing research for researchers in swarm intelligence and optimization fields.
The paper provides a brief literature review on the evolution of Ant Colony Optimization (ACO) algorithms, covering recent developments in applications like multi-objective optimization and algorithmic improvements such as hybridization.
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.