A Self-Adaptive Heterogeneous Ant Colony Algorithm for Path Planning

To address the issues of traditional ant colony optimization (ACO) algorithms in path planning, including susceptibility to local optima, low convergence efficiency, and insufficient environmental adaptability, this paper proposes a Self-Adaptive Heterogeneous Ant Colony Optimization (SA-HACO). Firstly, a non-uniform pheromone initialization method is introduced to preset pheromone concentrations on feasible paths between start and end points, enhancing initial search efficiency. Secondly, a pheromone diffusion model is designed to enable pheromone spreading to adjacent grids, strengthening global exploration capability and preventing local optima. Meanwhile, a heterogeneous ant colony system is implemented, where Gaussian mutation functions assign differentiated parameters to individual ants, thereby improving population diversity. Combined with an information entropy-based adaptive mechanism that dynamically adjusts exploration-exploitation weight parameters, the algorithm achieves a balance between convergence speed and global optimization capability. Simulation experiments conducted on 30×30 and 50×50 grid maps demonstrate that SA-HACO significantly outperforms comparative algorithms (ACO, PS-ACO, and JOP-ACO) in path length, smoothness, and convergence speed. Particularly in complex environments, it consistently obtains optimal paths, verifying its robustness and adaptability. This research provides a novel methodology for path planning problems that effectively balances efficiency and global optimization capability.