• 人工智能 •

### 基于滑动窗口和蚁群优化算法的二次路径规划算法

1. 1. 福建师范大学 数学与计算机科学学院, 福州350007;
2. 福建师范大学 网络安全与密码技术福建省重点实验室, 福州350007
• 收稿日期:2014-08-11 修回日期:2014-09-17 出版日期:2015-01-01 发布日期:2015-01-26
• 通讯作者: 郭躬德
• 作者简介:赖智铭(1988-),男,福建龙岩人,硕士研究生,主要研究方向:人工智能、数据挖掘;郭躬德(1965-),男,福建龙岩人,教授,博士生导师,主要研究方向:人工智能、数据挖掘.
• 基金资助:

国家自然科学基金资助项目(61070062, 61175123);福建高校产学合作科技重大项目(2010H6007).

### Quadratic path planning algorithm based on sliding window and ant colony optimization algorithm

LAI Zhiming1,2, GUO Gongde1,2

1. 1. College of Mathematics and Computer Science, Fujian Normal University, Fuzhou Fujian 350007, China;
2. Fujian Key Laboratory of Network Security and Cryptography, Fujian Normal University, Fuzhou Fujian 350007, China
• Received:2014-08-11 Revised:2014-09-17 Online:2015-01-01 Published:2015-01-26

Abstract:

A Quadratic path planning algorithm based on sliding window and Ant Colony Optimization (QACO) algorithm was put forward on the issue of weak planning ability of Ant Colony Optimization (ACO) algorithm in complex environments. The feedback strategy of the ACO based on Feedback Strategy (ACOFS) algorithm was improved, and the feedback times were reduced through the decrease of pheromone along feedback path. In the first path planning, the improved ACO algorithm was applied to make a global path planning for the grid environment. In the second path planning, the sliding windows slid along the global path. Local path in sliding windows was planned with ACO algorithm. Then the global path could be optimized by local path until target location was contained in the sliding window. The simulation experiments show that, the average planning time of QACO algorithm respectively reduces by 26.21%, 52.03% and the average length of path reduces by 47.82%, 42.28% compared with the ACO and QACO algorithms. So the QACO algorithm has a relatively strong path planning ability in complex environments.