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.
Ge et al. (Mon,) studied this question.