混合改进人工蜂群算法的机器人路径规划研究

Research on Robot Path Planning Based on Hybrid Improved Artificial Bee Colony Algorithm

传统群智能算法在研究路径规划时,存在早熟、搜索效率低及难以获取最佳路径等不足。针对这些问题,提出了一种混合改进人工蜂群算法。新算法首先利用人工势场法高效简单的优势将其与标准人工蜂群算法相结合,然后针对算法中存在易于陷入局部最优等缺陷将Levy分布与柯西变异算子引入标准人工蜂群算法中,新算法用Levy分布产生的步长取代食物源更新公式中的随机步长,在随机搜索策略中运用柯西分布的特点进行全局搜索。实验结果表明,改进后的算法在求解机器人运动路径时能够有效提高搜索效率和精度,新算法具有可行性和有效性。

The traditional group intelligence algorithm has shortcomings in path planning research,such as premature aging,low search efficiency,and difficulty in obtaining the best path.In response to these shortcomings,a hybrid improved artificial bee colony algorithm is put forward in the paper.First,because the artificial potential field method has the advantage of high efficiency and simplicity,the new algorithm combines it with the standard artificial bee colony algorithm.Then,the Levy distribution and the Cauchy mutation operator are introduced into the standard artificial bee colony algorithm for the defect that the algorithm is easy to fall into the local optimum.In the new algorithm,the random step size in the food source update formula is replaced by the step size generated through the Levy distribution,and the characteristics of Cauchy distribution are utilized in a random search strategy for global search.The experimental results show that the algorithm search efficiency and accuracy are effectively improved when the improved algorithm is used to solve the robot motion path.The new algorithm is feasible and effective.