基于拥挤度的改进蚁群算法

针对蚁群算法在求解旅行商问题时收敛时间长，且易陷入局部最优状态的缺陷，提出一种基于拥挤度的动态信息素蚁群优化策略。该算法引入静态拥挤度和动态拥挤度算子，主动提前预防停滞现象。将拥挤度与状态转移规则相结合，使蚁群状态实时跟随路径搜索情况而改变，提高蚁群自适应能力。针对蚁群路径搜索情况，加入邻域搜索优化规则，缩小搜索区域，结合2-opt局部优化策略，加快蚁群收敛速度。仿真结果表明，本算法既有较高的搜索效率又有较强的全局搜索能力。对比其他优化算法，无论是求解质量、稳定性还是收敛速度都能达到令人满意的效果。

An Improved Ant Colony Optimization Based on Congestion Degree

In order to solve the defects that the ant colony algorithm (ACO) has a long convergence time and is easy to fall into the local optimal state in solving traveling salesman problem (TSP),a dynamic pheromone ant colony optimization strategy based on crowding degree is proposed.Static congestion degree and dynamic congestion degree operator are introduced in the algorithm,which can prevent stagnation ahead of time.The state of ant colony can be changed according to the situation of path search in real time by combining the congestion degree and state transition rule,and the adaptive ability of ant colony can be improved.In view of the ant colony path search situation,adding the neighborhood search optimization rule,shrinking the search area,combining with the 2-opt local optimization strategy,so the ant colony convergence speed is accelerated.Simulation results show that the algorithm has both high search efficiency and strong global search ability.Compared with other optimization algorithms,the solution quality,stability and convergence rate can achieve satisfactory results.