HIPACO: An RSSI Indoor Positioning Algorithm Based on Improved Ant Colony Optimization Algorithm

Aiming at the shortcomings of traditional ACO algorithms in indoor localization applications, a high-performance improved ant colony algorithm (HIPACO) based on dynamic hybrid pheromone strategy is proposed. The algorithm divides the ant colony into worker ants (local exploitation) and soldier ants (global exploration) through a division of labor mechanism, in which the worker ants use a pheromone-weighted learning strategy for refined search, and the soldier ants perform Gaussian perturbation-guided global exploration. At the same time, an adaptive pheromone attenuation model (elite particle enhancement, ordinary particle attenuation) and a dimensional balance strategy (sinusoidal modulation function) are designed to dynamically optimize the searching process; moreover, a hybrid guidance mechanism is introduced to apply adaptive Gaussian perturbation guidance on successive failed particles to dynamically optimize the searching process. A hybrid guidance mechanism is introduced to enhance the robustness of the algorithm by applying adaptive Gaussian perturbation to successive failed particles. The experimental results show that in the 3D localization scenario with four beacon nodes, the average localization error of HIPACO is 0.82 ± 0.35 m, which is 42.3% lower than that of the traditional ACO algorithm, the convergence speed is improved by 2.1 times, and the optimal performance is maintained under different numbers of anchor nodes and spatial scales. This study provides an efficient solution to the indoor localization problem in the presence of multipath effect and non-line-of-sight propagation.