关键词: 交互学习模型, 强化学习策略, 自适应步长, 动态调整机制, 布谷鸟搜索算法, 蚁群优化算法

Abstract:

In order to solve the traveling salesman problem, the cuckoo search algorithm has the problems of lack of initial information and slow convergence. An Interactive Learning Cuckoo Search Algorithm（ILCSA） is proposed. In order to improve the search efficiency of the cuckoo algorithm, a two-layer interactive learning model is constructed by combining the ant colony algorithm. The ant colony is used as the bottom population, and the cuckoo is used as the high-level population. The two populations learn from each other, cooperate to find the best, and improve the search speed. In addition, cuckoo search algorithm introduces a reinforcement learning strategy, adaptively updates the step size, dynamically adjusts the discovery probability, and deeply optimizes the optimal solution to further improve the quality of the solution. Finally, multiple sets of standard TSPLIB examples of different scales are compared with other optimization algorithms. The results show that the ILCSA algorithm performs better in terms of accuracy and stability.

Key words: interactive learning model, reinforcement learning strategy, adaptive step size, dynamic adjustment mechanism, Cuckoo Search Algorithm（CSA）, ant colony optimization algorithm