High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization

Abstract

To solve the problem of falling into local optimum and poor convergence speed of traditional ant colony algorithm, this paper proposes a High-frequency path mining-based Reward and Punishment mechanism for multi-colony Ant Colony Optimization (HRPACO). Firstly, the pheromone concentration on the path of effective strong association is rewarded adaptively according to the lift of association rules to accelerate the convergence speed. Secondly, the pheromone concentration on the path of minimum spanning tree is punished adaptively according to the support of association rules to improve the diversity of the colony. The interaction of reward and punishment mechanism can effectively balance the diversity and convergence. Finally, a self-evolutionary mechanism based on Gaussian filter is proposed to adaptively adjust the pheromone concentration by dynamic smoothing of the pheromone matrix, so as to help the colony jump out of the local optimum. The TSP is used to verify the performance of the algorithm. The simulation results show that the proposed algorithm can effectively accelerate the convergence speed and improve the accuracy of solution, especially for large-scale problems. Meanwhile, path planning is used to verify the feasibility of the proposed algorithm. The simulation results show that the algorithm can find an effective and better path even in the environment of complex obstacles.