Hao Kun, Zhao Jiale, Wang Beibei, Liu Yonglei, Wang Chuanqi
School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China.
School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, China.
Comput Intell Neurosci. 2021 May 7;2021:5536574. doi: 10.1155/2021/5536574. eCollection 2021.
An adaptive genetic algorithm based on collision detection (AGACD) is proposed to solve the problems of the basic genetic algorithm in the field of path planning, such as low convergence path quality, many iterations required for convergence, and easily falling into the local optimal solution. First, this paper introduces the Delphi weight method to evaluate the weight of path length, path smoothness, and path safety in the fitness function, and a collision detection method is proposed to detect whether the planned path collides with obstacles. Then, the population initialization process is improved to reduce the program running time. After comprehensively considering the population diversity and the number of algorithm iterations, the traditional crossover operator and mutation operator are improved, and the adaptive crossover operator and adaptive mutation operator are proposed to avoid the local optimal solution. Finally, an optimization operator is proposed to improve the quality of convergent individuals through the second optimization of convergent individuals. The simulation results show that the adaptive genetic algorithm based on collision detection is not only suitable for simulation maps with various sizes and obstacle distributions but also has excellent performance, such as greatly reducing the running time of the algorithm program, and the adaptive genetic algorithm based on collision detection can effectively solve the problems of the basic genetic algorithm.
提出了一种基于碰撞检测的自适应遗传算法（AGACD），以解决基本遗传算法在路径规划领域存在的问题，如收敛路径质量低、收敛所需迭代次数多以及容易陷入局部最优解等。首先，本文引入德尔菲权重法来评估适应度函数中路径长度、路径平滑度和路径安全性的权重，并提出一种碰撞检测方法来检测规划路径是否与障碍物发生碰撞。然后，改进种群初始化过程以减少程序运行时间。在综合考虑种群多样性和算法迭代次数后，对传统的交叉算子和变异算子进行改进，提出自适应交叉算子和自适应变异算子以避免局部最优解。最后，提出一种优化算子，通过对收敛个体进行二次优化来提高收敛个体的质量。仿真结果表明，基于碰撞检测的自适应遗传算法不仅适用于各种大小和障碍物分布的仿真地图，而且具有优异的性能，如大大减少算法程序的运行时间，基于碰撞检测的自适应遗传算法能够有效解决基本遗传算法的问题。
