Improved QPSO with Darwinian evolution theory for three-dimensional path planning of robots
Mengfan Wang, Xinglin Chen, Siyu Liu, Sisindisiwe Nomalanga Ncube, Ruotong Ming, Fuqiang Liu, Jun Luo, Huayan Pu · Swarm and Evolutionary Computation · 2026
Effective three-dimensional (3D) path planning is crucial for robots to achieve missions in complex environments, where swarm optimization algorithms, which are important methods in path planning, face challenges such as local optimum and high sensitivity to initial particle positions. Therefore, this paper proposes a competitive-assimilation strategy based on Darwinian evolution theory and combines it with the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm to solve the challenges of optimization in 3D path planning. Simultaneously, a novel adaptive law and the Coyote Optimization Algorithm (COA) are both introduced into the algorithm for further strengthening the local search capability. By comparing with the latest related algorithms in simulation experiments and statistical validation tests, the two proposed methods demonstrate superior path quality (achieving mean value reductions of 1.51% and 3.44%, respectively) and enhanced algorithmic stability (with standard deviation reductions of 55.41% and 86.88%, respectively). These results substantiate the effectiveness and superiority of the proposed methodology in 3D path planning applications.