Ant colony optimization using two-dimensional pheromone for single-objective transport problems

Abstract

One of the acclaimed algorithms that is used to solve combinatorial graph problems is ant colony optimization (ACO). In this article, we focus on a novel extended model of the pheromone that is responsible for storing collective knowledge. The presented two-dimensional pheromone is able to accommodate more information that is extracted from feasible solutions that can be used to improve the search of a solution space. The idea is positively evaluated on TSP and VRP problems, achieving better results as compared to the original algorithm. Since it is a universal concept, it can be applied to any single-objective problem that is solvable by ACO.