基于自适应动态鲸鱼优化算法的路径规划研究

Research on Path Planning Based on Adaptive Dynamic Whale Optimization Algorithm

针对鲸鱼优化算法(WOA)求解路径规划问题时存在收敛慢、精度低以及局部最优等缺陷,提出一种自适应动态鲸鱼优化算法(ADWOA)来实现对路径的合理规划。首先,引入改进后的Circle混沌映射提高初始种群质量;然后,使用非线性收敛因子和自适应惯性权重平衡算法的全局寻优和局部寻优,加快算法收敛速度;最后,使用动态的螺旋更新系数,提高算法跳出局部最优的能力。通过仿真实验验证了自适应动态鲸鱼优化算法在机器人路径规划时的有效性。

Aiming at the shortcomings of slow convergence,low accuracy and local optimization when solving the path planning problem with whale optimization algorithm(WOA),an adaptive dynamic whale optimization algorithm(ADWOA)is proposed to realize the reasonable path planning.Firstly,the improved Circle chaotic map is introduced to improve the quality of the initial population.Then,the nonlinear convergence factor and the global and local optimization of the adaptive inertia weight balance algorithm are used to accelerate the convergence speed of the algorithm.Finally,the dynamic spiral update coefficient is used to improve the ability of the algorithm to jump out of the local optimum.The effectiveness of the adaptive dynamic whale optimization algorithm in robot path planning is verified by simulation experiments.