Understanding the Effects of Ant Algorithms on Path Planning with Gain-Ant Colony Optimization

Abstract

With the advent of more automated and unmanned systems, there is an increasing need for path planners. Intelligent path planners play an important role in the navigation of automated systems. In this work, the performance of an enhanced gain-ant colony optimization has been tested with the most popularly used ant algorithms – Ant system, Ant colony system and Min-Max ant system in the application of path planning. The pheromone update mechanism of traditional ant metaheuristic is enhanced with a local optimization mechanism and simulated with popular ant algorithms for an efficient choice of update rule. Evaluation is done using performance measures like path length and computation time taken. The results are statistically verified and analyzed. Path planned by proposed algorithm was found to be 3.25% shorter than existing algorithms.