Comparison between heterogeneous ant colony optimization algorithm and Genetic Algorithm for global path planning of mobile robot

Abstract

We proposed a novel ACO algorithm to solve the global path planning problems in the previous paper, called Heterogeneous ACO (HACO) algorithm. In this paper, we compare the performance of HACO algorithm with the modified Genetic Algorithm (GA) for global path planning. The HACO algorithm differs from the Conventional ACO (CACO) algorithm for the path planning in three respects. First, we proposed modified Transition Probability Function (TPF) and Pheromone Update Rule (PUR). Second, we newly introduced the Path Crossover (PC) in the PUR. Finally, we also proposed the first introduction of the heterogeneous ants in the ACO algorithm. We apply the proposed HACO algorithm and modified GA to the general global path planning problems and compare the performance of these through the computer simulation.