Modifying Ant Colony Optimization by introducing the concept of perception radius

The main motive of Ant Colony Optimization (ACO) is to find the shortest path from the ants' nest to the food source by sensing the amount of pheromone (a chemical secreted by ants which is used as the communication system) on the different paths available. In this paper, we propose a novel methodology which solves the problem of an ant facing an obstacle in its path from the nest to the food source, in which case the conventional ACO may fail. This work proposes a modified ant colony optimization by introducing the concept of perception radius, for enabling the ants to find the path to the food source even if there is any break in the pheromone trail. The Travelling Salesman Problem, as an example, is solved using the proposed modified ACO and the results obtained are compared with Dijkstra's algorithm. It is clearly demonstrated that our methodology not only works well when breaks or hurdles are encountered by ants but also provides efficient results.