Abstract:

Aiming at the shortcomings of the original Aquila Optimizer （AO）， such as insufficient local development ability， low optimization accuracy and slow convergence speed， a Multi-Strategy Improved AO （MSIAO） for robot path planning was proposed. Firstly， the Sobol sequence was introduced to initialize the Aquila population， which was conducive to diversity of the initial population and improved the convergence speed. Secondly， the local search method was improved by using golden sine operator and idea of self-learning and social learning of particle swarm， which enhanced exploitation ability of the algorithm and reduced the possibility of falling into the local optimum. Meanwhile， a non-linear balance factor was used as switching condition of the two stages， which made better communication among the populations， and was able to balance the global exploration and local exploitation more effectively. Finally， multiple experiments were carried out. Through the simulation on 12 benchmark functions and 10 CEC2017 complex functions， it can be seen that the proposed improvement strategies enhance the global optimization ability of MSIAO greatly. Results of applying MSIAO to robot path planning show that MSIAO can obtain shorter and more reliable moving paths. In 20×20 grid map， the average path of MSIAO is shortened by 2.53%， 3.83%， and 6.70% compared to those of Particle Swarm Optimization （PSO） algorithm， the original AO， and Butterfly Optimization Algorithm （BOA）， respectively； and in 40×40 grid map， the average path of MSIAO is shortened by 10.65%， 5.27%， and 14.88% compared to those of the above three algorithms， verifying that the path-finding of MSIAO is more efficient.

Key words: Aquila Optimizer (AO), Particle Swarm Optimization (PSO) algorithm, Sobol sequence, numerical optimization, path planning

摘要：

针对原始天鹰优化器（AO）存在局部开发能力不足、寻优精度低以及收敛速度慢等缺陷，提出一种用于机器人路径规划的多策略融合改进的天鹰优化器（MSIAO）。首先，引入Sobol序列对天鹰种群进行初始化，从而有利于初始种群的多样性，并提高收敛速度；其次，利用黄金正弦算子和粒子群的自我学习与社会学习的思想改进局部搜索方式，以增强算法的开发能力，并降低陷入局部最优的可能；同时，采用一种非线性平衡因子作为两阶段的切换条件，使种群之间的交流更充分，并能更有效地均衡全局搜索与局部开发。通过在12个基准测试函数、10个CEC2017复杂函数上的仿真实验可知，所提改进策略极大地增强了MSIAO的全局优化能力。将MSIAO应用于机器人路径规划的结果表明，MSIAO可以获得更短且更安全可靠的移动路径。在20×20栅格地图中，MSIAO的平均路径相较于粒子群优化（PSO）算法、原始的AO和蝴蝶优化算法（BOA）分别缩短了2.53%、3.83%和6.70%；在40×40栅格地图中，MSIAO的平均路径相较于上述3种算法分别缩短了10.65%、5.27%和14.88%。可见MSIAO的寻径更高效。

关键词: 天鹰优化器, 粒子群优化算法, Sobol序列, 数值优化, 路径规划