Effective route generation framework using quantum mechanism-based multi-directional and parallel ant colony optimization

Path planning in a complex network is a traditional and important research area in the optimization and machine learning fields. Several metaheuristics have been proposed; however, they are limited by the number of iterations, limited resources, and computation times. Therefore, in this study, the above issue is solved by integrating the existing metaheuristic algorithm with a prediction method. As a metaheuristic algorithm, ant colony optimization (ACO) is modified and applied. Instead of modeling ants pursuing higher pheromone densities, the proposed framework predicts pheromone traits using the intermediate pheromone density. As the pheromone volumes in the intermediate stage fluctuate, they are modeled using a quantum mechanism. Thereafter, the following pheromone traits are estimated using the modeled stochastic differential equation and Ito integral. The predicted pheromones are used for the parameters in the subsequent ACO iterations. To demonstrate the effectiveness of the proposed framework, the shortest path generation in several large-scale networks is provided. The proposed framework is considered a highly efficient metaheuristic framework with the integration of quantum mechanism-based prediction and existing metaheuristics.

Automated summary

This study combines existing metaheuristic algorithms with a prediction method to solve the path planning problem. By modifying the ant colony optimization algorithm and using predicted pheromone traits, the efficiency of the algorithm is improved.