关键词: 蚁群算法, 种群多样性, 种群相似度, 自适应信息素更新, 收敛速度, 旅行商问题

Abstract:

An adaptive improved ant colony algorithm based on population similarity is proposed to solve the problem that ant colony algorithm converges slowly and is prone to local optimality. The population similarity is used to measure the diversity of individuals in the population, and the parameters and pheromone update strategy of the ant colony algorithm are automatically adjusted according to the variation of population similarity in the evolutionary process to improve the optimization performance of the algorithm. The algorithm is used to solve the TSP problem and compared with the classic ACS and MMAS algorithms. The experimental results show that the proposed algorithm has significantly improved the quality of the solution and the stability of the algorithm, and effectively balances the contradiction between population diversity and convergence speed.

Key words: ant colony algorithm, population diversity, population similarity, adaptive pheromone updating, convergence speed, traveling salesman problem