Abstract: To address the issues of global search stability and susceptibility to local optima in traditional dung beetle optimization (DBO) algorithm for search and rescue path planning in urban warfare environments, this paper proposes an improved dung beetle optimization (IDBO) algorithm based on a hybrid strategy to enhance path planning efficiency and reliability. The IDBO algorithm introduces refractive reverse learning and elite selection strategies to increase population diversity and global search capability. In the rolling phase, it combines the osprey optimization algorithm (OOA) with the optimal solution to overcome the reliance on the worst individuals, enhancing global search capability in complex terrains. In the breeding phase, a dynamic selection mechanism and adaptive [t]-distribution strategy are employed to balance global exploration and local exploitation, meeting the dual requirements for accuracy and speed in search and rescue tasks. In the foraging phase, the Jacobi curve is incorporated to strengthen the algorithm’s ability to escape local optima, enabling it to effectively handle various uncertainties in urban warfare environments. Performance tests on the CEC2005 function set demonstrate that IDBO outperforms the DBO algorithm in global search capability and convergence accuracy. In path planning experiments within a simulated urban warfare search and rescue environment, in the static environment, the IDBO algorithm achieves shortest paths of 27.841 and 57.256 in simple and complex grid maps respectively, representing reductions of 2.57% and 15.35% compared to the DBO algorithm. In a dynamic environment, the shortest paths are 29.213 and 59.367, which are 3.85% and 14.37% shorter than those generated by the DBO algorithm, further validating its effectiveness and stability in urban warfare search and rescue path planning.

Key words: path planning, urban search and rescue, dung beetle optimization algorithm, refractive reverse learning, Jacobi curve, Wilcoxon rank-sum test

摘要： 针对巷战环境下搜救路径规划中传统蜣螂优化算法（DBO）在全局搜索稳定性和陷入局部最优等问题，提出一种基于混合策略的改进蜣螂优化（IDBO）算法，以提升搜救过程中的路径规划效率与可靠性。引入折射反向学习与精英选择策略，增强种群多样性和全局搜索能力；在滚球阶段结合鱼鹰优化算法（OOA）和最优解的耦合，解决了传统算法依赖最差个体支持的缺陷，增强算法在复杂地形中的全局搜索能力；在繁殖阶段引入动态选择机制与自适应[t]分布策略，平衡全局探索和局部开发，以适应搜救任务中对搜索精度和速度的双重需求；在觅食阶段结合雅克比曲线，提升算法跳出局部最优的能力，使算法能够有效应对巷战环境中的多种不确定因素。通过在CEC2005函数集上的性能测试，IDBO算法在全局搜索能力和收敛精度方面均优于DBO算法。在巷战搜救仿真环境下的路径规划实验中，静态环境下简单与复杂栅格地图下IDBO算法规划最短路径分别为27.841和57.256，较DBO算法分别缩短2.57%和15.35%；动态环境下最短路径为29.213和59.367，较DBO算法缩短3.85%与14.37%，进一步验证了IDBO算法在巷战搜救路径规划中的有效性和稳定性。

关键词: 路径规划, 巷战搜救, 蜣螂优化算法, 折射反向学习, 雅克比曲线, Wilcoxon秩和检验