计算机应用研究2026，Vol.43Issue(8)：2316-2324,9.DOI:10.19734/j.issn.1001-3695.2025.11.0497
融合量子启发与多层邻域的改进APO路径规划算法
Improved APO path planning algorithm fusing quantum inspiration and multi-level neighborhood
摘要
Abstract
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.关键词
移动机器人/路径规划/人工原生动物优化算法/多层次自适应邻域/量子启发机制Key words
mobile robot/path planning/artificial protozoa optimizer/multi-level adaptive neighborhood/quantum-inspired mechanism分类
信息技术与安全科学引用本文复制引用
王继超,回振桥,张瀚予,刘昕彤..融合量子启发与多层邻域的改进APO路径规划算法[J].计算机应用研究,2026,43(8):2316-2324,9.基金项目
国家自然科学基金资助项目(62273033) （62273033）
河北省教育厅科学研究资助项目(ZC2025095) （ZC2025095）
沧州市自然科学基金资助项目(23241002014N) （23241002014N）
河北水利电力学院基本科研业务费专项资金资助项目(SYKY2308) （SYKY2308）
