Research on Cable Laying Path Optimization Based on ACA

With the continuous expansion of urban power grids, the planning and optimization of cable laying paths have become increasingly important in power system design. Traditional path planning methods often suffer from low efficiency, local optima, and poor adaptability to complex environments. To address these issues, this paper conducts a systematic study on cable laying path optimization based on the Ant Colony Algorithm (hereinafter called ACA), aiming to enhance the intelligence and engineering applicability of path planning. Based on the classical ACA and its improved variants, this study proposes strategies suitable for complex environments, including adaptive adjustment of heuristic factors, optimization of pheromone update mechanisms, and the integration of local search strategies. The results demonstrate that the proposed improved ACA achieves better optimization performance in terms of path length, current-carrying balance, and laying efficiency. The algorithm exhibits strong engineering adaptability and optimization capability in cable path planning, providing effective design references for practical power engineering. The innovation of this study lies in the targeted structural improvements to the ACA based on the specific requirements of cable engineering applications. Moreover, path planning visualization is implemented on the MATLAB platform, offering theoretical and methodological references for future path optimization in other engineering scenarios.