Optimal Secondary Cable Laying Path Planning In Complex Environments Using An Improved Ant Colony Algorithm: Model Development And Simulation

In this paper, an improved ant colony algorithm (ACA) is suggested for the management of secondary cables and primary cable laying in complex environments. The method creates a digital representation of the region where the cable is laid, formulates a multiobjective function with path length, interference risk, and path tortuosity variables, and puts on engineering restrictions that connect the parts, bending radius, and the distance for safety. The upgraded ACA features a dynamic pheromone update with adaptable parameters that allow for a proper mixture of exploration and convergence, thus avoiding the algorithm being trapped in local optima and enhancing global search productivity. The simulation outcomes validate that the suggested technique maintains 100% path connectivity and simultaneously results in a constraint redundancy of below 10%. It also gives shorter and smoother paths compared to the classical methods. The major contributions are a constraint-integrated optimization frame, a dynamic pheromone reward-penalty system, and adaptive parameter setting, which together form a robust and effective solution for smart cable routing in challenging engineering environments.