计算机工程与应用2019，Vol.55Issue(4)：163-172,10.DOI:10.3778/j.issn.1002-8331.1711-0301
基于信息素负反馈的超启发式蚁群优化算法
Hyper Heuristic Ant Colony Optimization Algorithm Based on Pheromone Negative Feedback
摘要
Abstract
The existing ant colony algorithm is applied to the path planning of automated guided vehicle, which is slow in the convergence and easily fails into the local optimum. To solve these problems, this paper proposes a Hyper Heuristic Ant Colony Optimization algorithm based on pheromone negative feedback(ACONhh)for path planning of mobile robots. The algorithm makes full use of historical search information and continues to gain error experience, thus further leads ant colony to explore optimal path. Hierarchical selection of feasible nodes is adopted to accelerate the initial convergence rate of the algorithm. Meanwhile, the volatility factor changes constantly with an analogous parabola, and pheromone update mechanism is adjusted to improve the randomness of global search. The convergence of ACONhh algorithm is strictly proved. Simulation and experimental results show that the convergence speed and global search performance of the proposed algorithm are outperform those of popular ACO, ACOhh and ACOihh algorithms.关键词
自动导引小车/路径规划/蚁群优化算法/信息素负反馈/分层化选择Key words
automated guided vehicle/ path planning/ ant colony optimization/ pheromone negative feedback mechanism/hierarchical selection分类
信息技术与安全科学引用本文复制引用
薛文艳,赵江,郝崇清,刘慧贤..基于信息素负反馈的超启发式蚁群优化算法[J].计算机工程与应用,2019,55(4):163-172,10.基金项目
国家自然科学基金(No.51507048) （No.51507048）
河北省自然科学基金(No.F2014208013). （No.F2014208013）
