摘要
在机器人路径规划问题中,蚁群算法作为经典的优化算法,相比于传统的数学方法在求解最优化问题时具有较强的求解能力,同时蚁群系统具有鲁棒性和简单性,因此常用于对各种问题的最优化研究。但是蚁群算法也存在一些不足之处,初期信息素表达能力缺失,盲目搜索进而导致收敛速度慢,信息素表达能力的局限性使得算法易陷入局部最优甚至使算法循环停滞不前。针对这一情况,在简单蚁群算法的基础上,引入遗传算法和非线性强化局部搜索能力寻找全局最优,同时对信息素更新方式进行优化,加快计算初期算法的收敛速度。最后在MATLAB仿真平台上对算法进行验证,实验结果表明,算法的运行速度和搜索能力均得到了较大的提高。
Ant colony algorithm as a classical optimization algorithm in robot path planning problem,compared with traditional mathematical methods,it has strong solving ability when solving optimization problems.At the same time,ant colony system is robust and simple,so it is often used to optimize various problems.However,there are some shortcomings in the ant colony algorithm.The initial pheromone expression ability is absent,blind search leads to slow convergence,and the limitation of pheromone expression ability makes the algorithm easy to fall into local optimum or even make the algorithm cycle stagnant.Aiming at this situation,the traditional ant col⁃ony algorithm is improved,and the genetic algorithm and nonlinear enhanced local search ability are introduced to find the global optimal.At the same time,the pheromone update method is optimized,and the convergence speed of the initial algorithm is accelerated.Finally,the algorithm is simulated by MATLAB.The experimental results show that the algorithm's running speed and search ability have been greatly improved.
作者
侯远韶
HOU Yuanshao(Mechanical and Electrical Engineering,Henan Industry and Trade Vocational College,Zhengzhou 451191,China)
出处
《安阳工学院学报》
2020年第6期39-42,共4页
Journal of Anyang Institute of Technology
基金
河南省高等学校重点科研项目计划(20A120007)。
关键词
机器人
蚁群算法
路径优化
遗传算法
robot
ant colony algorithm
path optimization
genetic algorithm
