Abstract: Aiming at the defects of osprey optimization algorithm, such as weak global search ability, unbalanced exploration and exploitation, and easy to fall into local optimum, a hybrid strategy improved osprey optimization algorithm is proposed. A high-altitude flying exploration strategy is designed for the behavior of the osprey after the failure of fishing, which improves the global search ability of the algorithm, and then enhances the ability of the algorithm to get rid of the local extremum. An adaptive balance energy factor is introduced in the exploitation stage of the algorithm to balance the exploration and exploitation of the algorithm. In order to verify the performance of the proposed algorithm, it is experimentally compared with seven optimization algorithms on the CEC2017 test set, CEC2019 test set and CEC2022 test set, and the Wilcoxon rank sum test and Friedman test are performed on the experimental results. The experimental results show that the proposed algorithm has better optimization accuracy, convergence speed, and robustness. The effectiveness and practicality of the proposed algorithm are further verified by simulation experiments on 12 real-world engineering constrained optimization problems and robot path planning problems.

Key words: osprey optimization algorithm, high-altitude flying exploration, balance energy factor, engineering optimization problems, path planning

摘要： 针对鱼鹰优化算法存在的全局搜索能力弱、探索与开发不平衡和易陷入局部最优等缺陷，提出一种混合策略改进的鱼鹰优化算法。对于鱼鹰捕鱼失败后的行为设计一种高空翱翔勘探策略，提高算法的全局搜索能力，进而增强算法摆脱局部极值的能力；在算法的开发阶段引入一种自适应平衡能量因子，平衡算法的探索与开发。为验证所提算法的性能，将其与7种优化算法在CEC2017测试集、CEC2019测试集以及CEC2022测试集上进行实验对比，并对实验结果进行Wilcoxon秩和检验与Friedman检验。实验结果表明，所提算法具有更好的寻优精度、收敛速度以及鲁棒性。通过对12个现实世界工程约束优化问题和机器人路径规划问题进行仿真实验，进一步验证了所提算法的有效性和实用性。

关键词: 鱼鹰优化算法, 高空翱翔勘探, 平衡能量因子, 工程优化问题, 路径规划