关键词: 蚁群算法, 收敛性, 加速度, 兴趣度, 均匀度

Abstract: Ant Colony Algorithm （ACA） is meta-heuristic which has some typical shortcomings, such as long-computation-time, stagnation behavior. For avoiding these, the references such as [α,β,ρ,Q] and [m] etc. are studied for discovering their inertial relation, and their dynamic models are constructed with interest level, acceleration and uniformity. Based on these models, an Acceleration Ant Colony Algorithm （AACA） is proposed, which is improved from ACA by modifying the pheromone updating rule and the transition rule. Simulation shows that the AACA can solve the contradictory between convergence speed and stagnation behavior efficiently and has a better solution.

Key words: ant colony algorithm, convergence, acceleration, interest level, uniformity