基于改进粒子群优化算法的多无人机协同搜索与目标发现研究

Research on Multi-UAV Cooperative Search and Target Detection Based on Improved Particle Swarm Optimization Algorithm

摘要: 本文针对复杂环境下的多无人机协同搜索与目标发现问题，提出一种改进的粒子群优化算法。该算法通过引入全局探索项、动态区域重分配机制、非线性惯性权重调整以及多目标适应度函数，有效解决了传统PSO算法在无人机协同搜索中存在的早熟收敛、探索效率低、避障能力弱等问题。实验在100 m × 100 m的搜索区域内进行，结果表明，相较于随机搜索和标准PSO算法，改进PSO算法在目标发现成功率上提高了35.2%，平均发现时间缩短了42.7%，区域覆盖率提升了28.5%。同时，该算法展现出良好的避障性能与通信保持能力，验证了其在复杂环境下的有效性与鲁棒性。本研究为复杂环境下多无人机协同搜索与目标发现提供了一种高效实用的解决方案。

Abstract: To address the problem of cooperative search and target discovery by multiple unmanned aerial vehicles (UAVs) in complex environments, an improved particle swarm optimization (PSO) algorithm is proposed in this paper. The proposed algorithm introduces a global exploration term, a dynamic region reallocation mechanism, a nonlinear inertia weight adjustment strategy, and a multi-objective fitness function, which effectively overcome the limitations of traditional PSO algorithms, such as premature convergence to local optima, low exploration efficiency, and weak obstacle avoidance capability in cooperative UAV search tasks. Simulation experiments are conducted in a 100 m × 100 m search area. The results demonstrate that, compared with random search and standard PSO algorithms, the proposed improved PSO algorithm increases the target discovery success rate by 35.2%, reduces the average discovery time by 42.7%, and improves the area coverage by 28.5%. In addition, the algorithm exhibits good obstacle avoidance performance and communication maintenance capability, indicating its effectiveness and robustness in complex environments. The proposed method provides an efficient and practical solution for cooperative search and target discovery in multi-UAV systems operating in complex environments.