Mobile robot path planning is an important part of the mobile robot field. The swarm intelligence optimisation algorithm has certain advantages in solving such multi-objective optimisation problems, so this Letter proposes to apply the pigeon-inspired optimisation (PIO) algorithm to the path planning problem. However, the traditional PIO algorithm is suffering from the problems of easy to get into local optimisation, low stability and premature convergence. To overcome its shortcomings, this Letter proposes a logistic beetle algorithm search–PIO (LBAS-PIO) algorithm. In the optimisation process of the LBAS-PIO algorithm, the PIO algorithm is initialised by the logistic mapping, making the search space more extensive. Learning from the idea of the beetle algorithm search, each pigeon has its own judgment of the environment and the number of iterations and search time are reduced. Further, the path evaluation function of the algorithm and the execution order of the operators are optimised to make the algorithm smoother and easier to find the global optimal solution. Simulation and experimental results illustrated the superiority of the LBAS-PIO algorithm and the LBAS-PIO algorithm better meets the needs of mobile robots for the optimal path planning.

中文翻译：

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求解基于逻辑甲虫算法搜索的路径规划问题——鸽子启发优化算法

移动机器人路径规划是移动机器人领域的重要组成部分。群体智能优化算法在解决此类多目标优化问题上具有一定的优势，因此本文提出将鸽子启发优化（PIO）算法应用于路径规划问题。然而，传统的PIO算法存在容易陷入局部优化、稳定性低和早熟收敛的问题。为了克服它的缺点，本文提出了一种逻辑甲虫算法搜索-PIO（LBAS-PIO）算法。在LBAS-PIO算法的优化过程中，PIO算法通过logistic映射进行初始化，使得搜索空间更加广阔。借鉴甲壳虫算法搜索的思想，每只鸽子都有自己对环境的判断，减少了迭代次数和搜索时间。进一步优化算法的路径评价函数和算子的执行顺序，使算法更流畅，更容易找到全局最优解。仿真和实验结果说明了LBAS-PIO算法的优越性，LBA​​S-PIO算法更好地满足了移动机器人对最优路径规划的需求。