摘要
采用蚁群算法求解复杂环境下移动机器人路径规划问题时,会出现运算时间过长、求解精度不高等问题,对此,定义一种新的动态搜索诱导算子以改进蚁群算法性能.重点设计了动态搜索模型,即:在进化初期设定较大阈值以增加种群的多样性;而伴随进化过程,利用衰减模型动态调整为较小阈值以加快收敛速度.TSP测试实验结果表明,该改进蚁群算法不仅能加快收敛速度,而且有效提高了优化解的质量.复杂环境中机器人路径规划问题的求解验证了所提出算法的实际应用效果.
To overcome difficulties of the traditional ant colony optimization, a novel ant colony system based on dynamic search（DSACS） strategy for path planning problem of mobile robot is proposed.Therefore, a dynamic search model is designed.in the prophase, a bigger parameter is used to increase the diversity of the population; in the anaphase, a smaller parameter is adjusted through the attenuation model to accelerate convergence.Experimental results of TSP benchmark instances show that the improved ant colony algorithm can not only accelerate the convergence, but also improve the quality of the optimal solution.Simulation results of path planning problems under the complex environment verify the cutual effect of the DSACS strategy.
出处
《控制与决策》
EI
CSCD
北大核心
2017年第3期552-556,共5页
Control and Decision
基金
国家自然科学基金项目(61075115
61403249
61673258)
关键词
蚁群系统
动态搜索诱导算子
移动机器人
路径规划
复杂环境
ant colony system
dynamic search bias operator
mobile robot
path planning
complex environment
