量子黏菌算法在路径规划中的应用OA

To address the issues of low optimization accuracy and susceptibility to local optima in the standard slime mold algorithm(SMA)for path planning,this paper proposes a novel quantum-classical hybrid algorithm,the quantum slime mold algorithm(QSMA).QSMA integrates quantum computing principles into the SMA framework,leveraging the superposition property of qubits to enhance solution space coverage and utilizing single quantum measurement for effi-cient solution mapping.Key features include a weighted transformation mechanism facilitating information exchange among individuals and a position update mechanism based on a combined quantum channel model(amplitude and phase damping),complemented by an adaptive quantum rotation gate strategy to steer the search towards the global optimum.Crucially,simulation results on grid maps demonstrate the effectiveness of the algorithm and notable noise robustness under quantum noise interference.Even with added noise,QSMA consistently finds paths of the same optimal length as SMA,maintains stable convergence,and exhibits only a modest increase in iteration count.This innovative quantum-classical hybrid design enables the successful application of QSMA to path planning problems in grid map environments.