关键词: 蚁群优化（ACO）, 局部最优解, 重叠蚁群优化, 动态学习

Abstract: Considering that the ant colony optimization (ACO) algorithm in solving global optimal solution search problems has the defects such as slow convergence and prone to local optimal solution phenomenon, this paper provides an overlap ant colony optimization (OACO) algorithm to solve global optimal solution search problem. By setting multiple overlapping ant colony, initializing different parameters of every ant colony, and learning dynamically between ant colony pheromones, OACO algorithm reinforces the exploitation of ant colony optimization algorithm and avoids premature phenomenon. The experimental results show that OACO algorithm achieves good results in avoiding falling into local optimal solution, and is an efficient and effective improved ant colony optimization algorithm.

Key words: ant colony optimization (ACO), local optimal solution, overlap ant colony optimization, dynamic learning