Wang Shuai, Zhu Yifan, Du Yuhong, Yang Ming
School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China.
College of Management and Economics, Tianjin University, Tianjin 300072, China.
Biomimetics (Basel). 2025 Aug 1;10(8):503. doi: 10.3390/biomimetics10080503.
Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications.
传统算法如迪杰斯特拉算法（Dijkstra）和人工势场法（APF）在路径规划时依赖完整的环境信息，这在建模过程中导致了诸多限制。这不仅增加了算法的复杂度，还降低了规划的效率和可靠性。群体智能算法具有强大的数据处理和搜索能力，使其能够有效地解决不同环境下的路径规划问题并生成近似最优路径。然而，群体智能算法存在诸如早熟收敛以及在搜索过程中容易陷入局部最优等问题。因此，提出了一种改进的人工蜂群 - 甲虫触角搜索（IABCBAS）算法。首先，将帐篷混沌和非均匀变异引入到蜂群算法中以增强种群多样性和空间搜索能力。其次，将随机反向学习机制和贪婪策略分别融入到甲虫触角搜索算法中，以提高其寻向能力和逃离局部最优的能力。最后，自适应调整两种算法的权重以平衡全局搜索和局部优化。使用九个基准函数和四种比较算法的实验结果表明，改进后的算法在高维和低维环境以及单峰和多峰环境中均表现出卓越的路径点搜索性能和高稳定性。消融实验结果表明，算法中引入的优化策略有效地提高了路径规划过程中的收敛精度和速度。路径规划实验结果表明，与比较算法相比，改进算法在二维多障碍物环境中的平均路径规划距离减少了23.83%，在三维表面环境中的平均规划时间缩短了27.97%。路径规划效率的提升使得该算法在工程应用中具有一定价值。
