The dynamic traveling salesman problem is an extension of the standard traveling salesman problem and has attracted a great deal of interest due to its wide range of real-world applications.Ant colony optimization al-gorithms can transform historical environmental information and have the ability to adapt to dynamic changes to solve dynamic traveling salesman problems.The trade-off between algorithmic exploration and exploitation capability is a key issue when using ant colony optimization algorithms to solve optimization problems.The traditional idea is to focus on the exploration capability in the early stage of search,so that the ant colony can fully acquire the information of the search space,and gradually enhance the exploitation capability as the search process proceeds,so that the ant colony gradually converges.However,this way of thinking is not conducive to obtaining high-quality solutions quickly in dy-namic scenarios.To address this problem,a new exploration-exploitation trade-off strategy is proposed,which first u-ses a simulated annealing algorithm to enhance the exploitation ability to quickly obtain higher-quality solutions after the environment changes,and then uses an adaptive roulette selection method to help the algorithm jump out of local extremes when the solution quality is difficult to improve.Experiments on dynamic traveling salesman problems with changing weights demonstrate that such strategies outperform other ant colony optimization algorithms and variants.
关键词
动态旅行商问题/蚁群优化/探索-利用权衡策略/模拟退火算法/轮盘赌选择方法
Key words
Dynamic traveling salesman problem/Ant colony optimization/Trade-off strategies between explora-tion and exploitation/Simulated annealing algorithm/Roulette selection method
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