基于改进莱维飞行的狼群算法及其在翼型气动优化设计中的应用

智能种群算法在翼型气动优化领域获得越来越多的关注。提出了一种基于改进莱维飞行的狼群算法,并对其寻优性能进行了测试和验证。为平衡算法的局部搜索和全局搜索能力,将局部搜索性能强但易早熟的狼群算法与莱维飞行相结合,在增加收敛速度的同时,保证算法的全局搜索能力。为克服原始莱维飞行效率低、精度差的缺陷,引入高斯核函数自适应调节莱维飞行的搜索步长,以增加其搜索效率;引入四元法,以增加其搜索精度;通过标准测试函数和标准翼型气动优化设计算例验证,表明改进算法在优化效率和全局寻优能力方面均优于原始算法。

An Improved Levy Flight based Grey Wolf Optimization Algorithm for Aerodynamic Design Problem

Intelligent swarm algorithm has attracted more and more attention in the field of aerodynamic shape optimization. In this paper, an improved levy flight based Grey Wolf optimizer (ILGWO) has been proposed, and its optimization performance is tested and verified. In this paper, the levy flight is combined with Grey Wolf optimization method, aiming to increase the convergence speed and ensure the global search ability of the algorithm. Besides, in order to overcome the disadvantages of low efficiency and poor precision of the original cuckoo search algorithm, gauss kernel function is introduced to adjust the search step of levy flight adaptively, so as to increase its search efficiency. Quaternion method is also introduced to increase the search precision. To balance the global search and local search ability of the algorithm, With the function tests and a benchmark airfoil design optimization problem, it is shown that the optimization performance of the improved algorithm is significantly improved, and the optimization efficiency and the global optimization ability are better than the original algorithm.