Abstract:

Aiming at the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm, a dynamic hierarchical dual-morphic ant colony algorithm is proposed based on artificial bee colony algorithm. In the algorithm, the ant colony is divided into the Xunyou ants and the Zhencha ants according to different fitness, and the dynamic pheromone update strategy with different weighting coefficients is executed：the Xunyou ants are responsible for searching the optimal path and carring out pheromone updating strategy with larger weight, so as to enhance its orientation and speed up the convergence of the algorithm. The Zhencha ants are responsible for exploring the non-optimal path and finding other better solutions to ensure the diversity of the algorithm. At the end of each iteration, two kinds of ants exchange excellent solutions to improve the quality of solutions. Taking the traveling salesman problem as an example, it is compared with the classical ant colony algorithm, the latest ant colony improvement algorithm and other latest optimization algorithms, and its performance is better.

Key words: ant colony algorithm, artificial bee colony algorithm, fitness;dualmorphic, dynamic pheromone updating strategy, exchange excellent solutions

摘要：

针对蚁群算法收敛速度慢、易陷入局部最优等问题，结合人工蜂群算法的分级思想，提出动态分级的双蚁态蚁群算法。根据适应度不同，将蚁群划分为寻优蚁和侦查蚁，并执行不同加权系数的动态信息素更新策略：寻优蚁负责较优路径的搜索，执行较大权重的信息素更新策略，以增强其导向性，提高算法收敛速度。侦查蚁则负责探索非较优路径，发现其他更优解，以保证算法多样性。然后，每次迭代结束则两类蚂蚁进行优良解交换，以提高解的质量。以旅行商问题为例，将其与经典蚁群算法、最新蚁群改进算法以及其他最新优化算法进行对比，其表现皆更优。

关键词: 蚁群算法, 人工蜂群算法, 适应度, 双蚁态, 动态信息素更新策略, 优良解交换