基于协同合作的蚁群优化算法

蚁群优化是一种模拟蚂蚁觅食的群集智能搜索算法,基本蚁群算法收敛性较差,易陷入局部最优解。本文在基本蚁群算法的基础上,提出一种新的蚁群优化算法,通过在信息素局部更新中引入信息素扩散模型,在信息素全局更新中引入随机扰动机制,发挥蚂蚁之间的协同合作能力,提高了算法的收敛速度。以TSP为例的仿真实验表明,该算法具有较强的寻优能力、较好的鲁棒性和有效性。

Ant Colony Optimization Algorithm Based on Co-operation Strategy

Ant colony optimization is a kind of searching algorithm which simulates ants foraging. Ant Colony System has the limitation of poor convergence, and is easy to fall in local optimal solution. This paper proposes a new ant colony optimization algorithm, based on a more reasonable diffusion model to update the local pheromone and a disturbance strategy to update the global pheromone. The algorithm shows some advantages in improving the search speed and enhancing collaboration between the ants. As an example of TSP, the simulation results show that the algorithm has better global convergence, robustness and validity.