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

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