融合量子启发与多层邻域的改进APO路径规划算法OA

To address the problems of local optima traps,slow convergence speed,and poor path smoothness in mobile robot path planning,this paper developed an improved artificial protozoa optimizer(IAPO).The algorithm introduced a multi-level adaptive neighborhood structure optimization strategy,which balanced global exploration and local exploitation through a layered mechanism and adaptive radius adjustment.A quantum-inspired state transition mechanism realized intelligent switching of behavioral strategies based on quantum superposition and entanglement properties to enhance the ability to escape local opti-ma.A chaos-fractal hybrid dynamical system improved population ergodicity and solution accuracy using hybrid chaotic se-quences and dynamic fractal dimensions.IEEE CEC2022 benchmark tests(F6～F11)show that IAPO outperforms mainstream algorithms such as PSO,ACO,and COA in convergence accuracy by one to three orders of magnitude.Path planning experi-ments on 20 × 20,30 × 30,and 50 × 50 grid maps demonstrate that IAPO achieves shorter and smoother paths while strictly satisfying obstacle avoidance constraints,effectively resolving the corner-cutting collision and wall-passing failure problems.The results confirm the efficiency and robustness of IAPO for mobile robot path planning in complex environments.