改进蚁群算法在配电网规划中的应用

Improved Ant Colony Algorithm for Power Distribution Network Planning

对蚁群算法模型进行研究,对信息素的选取和信息素更新加以改进,提出了一种IACOA算法。算法利用多态自适应调整信息素的蚁群搜索机制以提高其寻优速度,弥补了传统蚁群算法在配电网规划中计算速度慢、易于陷入局部最优解的不足,并改善了解的收敛性。同时引入数据表技术,节约了二次规划的规划时间。该方法利用国际标准参数进行验证,并对72个负荷点的小型自发电配电网进行优化,取得满意的优化结果。实验结果表明,改进蚁群算法为配电网的规划提供了一种新的有效的方法。

The traditional Ant Colony Algorithm is improved from the aspect of searching process and pheromone modification, and an IACOA algorithm is proposed. With the IACOA, the defects of low searching efficiency and easy to fall into local optimal solution in traditional Ant Colony Algorithm are remedied. And the astringency of solution is improved. The database technology is introduced to save the planning time of quadratic programming. This method is verified by international standard parameters. Moreover, a satisfactory optimum solution for a power distribution network planning has been obtained. The results show that the improved Ant Colony Algorithm has provided a novel effective method for the power distribution network planning.