Ant Colony Optimization Algorithm and Artificial Immune System Applied to a Robot Route

摘要

This Article aims to introduce two meta-heuristics techniques: Ant Colony Optimization (ACO) and Artificial Immune System (AIS) to find the best route for a robot. The ACO is an algorithm based on the ant food search process, and the AIS is inspired by the defending mechanism of the human organism. In order to illustrate and compare the potential of these techniques, this paper applies both techniques in a problem of determining the shortest possible route for a robot without hitting any obstacles in three different maps. According to the tests, the ACO shows better results regarding the number of iterations to reach the global optimum, while the AIS shows better results when it comes to the processing time. From the result, it can be seen that the ACO found a solution to all maps demonstrating it is an excellent choice for this problem type.