Novel Ant Colony Optimization algorithm with Path Crossover and heterogeneous ants for path planning

In this paper, a novel ACO algorithm is proposed to solve the global path planning problems, called Heterogeneous ACO (HACO) algorithm. We study to improve the performance and to optimize the algorithm for the global path panning of the mobile robot. The HACO algorithm differs from the Conventional ACO (CACO) algorithm for the path planning in three respects. We modify the Transition Probability Function (TPF) and the Pheromone Update Rule (PUR). In the PUR, we newly introduced the Path Crossover (PC). We also propose the first introduction of the heterogeneous ants in the ACO algorithm. In the simulation, we apply the proposed HACO algorithm to general path planning problems. At the last, we compare the performance with the CACO algorithm.