基于搜索集中度和动态信息素更新的蚁群算法

Ant colony algorithm based on search concentration and dynamic pheromone updating

蚁群算法是一种启发式搜索算法,被广泛应用于求解复杂的组合优化问题。基本蚁群算法存在收敛速度慢和早熟停滞等问题,针对这些问题,提出了一种基于搜索集中度和动态信息素更新的蚁群算法。通过在选择策略中引入"搜索集中度"因子,让算法可以自适应的调节蚂蚁选择城市的范围,在此基础上采用动态改变信息素增量和信息素回滚的机制,缩短了搜索时间,也使算法更容易跳出局部极值。仿真实验结果表明,改进后的算法算法具有较快的收敛速度,提高了解的全局性,有效避免了算法陷入局部最优。

Ant colony algorithm is a kind of heuristic search algorithms.It has been widely used to solve complex combinatorial optimization problems.Basic ant colony algorithm has some disadvantages,such as slow convergence and premature stagnation.In order to overcome these problems,we propose an improved ant colony algorithm,which is based on search concentration and dynamic pheromone updating.Specifically, by introducing the "Search Concentration"factor in the selection strategy,the algorithm can adaptively adjust the range of cities selected by the ants.In addition,increments of pheromone are dynamically changed and a kind of pheromone rollback mechanism is used.As a result,the search time are shortened and the algorithm is more easy to jump out of the local extremum.Simulation experimental results show that the improved algorithm has a faster convergence speed,improves the global understanding,and effectively avoids the algorithm falling into local optimum.