一种改进的果蝇优化算法及其在气动优化设计中的应用

果蝇优化算法(FOA)是一种新的群体智能优化算法,具有良好的全局收敛特性。为进一步提高FOA的寻优性能,将其引入到气动优化设计中,发展形成了改进的果蝇优化算法(IFOA)。IFOA通过引入惯性权重函数动态调整搜索步长,有效实现了算法全局搜索和局部搜索之间的动态平衡,提高了算法整体搜索效率和寻优精度;对于多维优化问题,IFOA每次搜索仅随机扰动其中一个决策变量,并在每个迭代步内将所有优秀果蝇个体(可行解)结合产生一个全新的果蝇个体进行一次搜索,大大加快了算法的收敛速度。函数测试结果表明,IFOA显著提高了FOA的寻优性能。将IFOA应用到气动优化设计中,翼型反设计和单/多目标优化设计的算例表明,IFOA是一种简单高效的优化方法,可广泛应用于气动优化设计。

An improved fruit fly optimization algorithm and its application in aerodynamic optimization design

As a new swarm intelligence optimization algorithm,fruit fly optimization algorithm (FOA) has a good property of global convergence.In order to further improve the searching performance of FOA and use it for aerodynamic optimization design,a new algorithm named improved fruit fly optimization algorithm (IFOA) is presented.The search step is modified by introducing an inertia weight function to IFOA,and the dynamical balance between the global and the local search is satisfied.The searching efficiency and accuracy of algorithm is integrally improved.For multi-dimensional problems,only one decision variant is randomly changed for producing a new solution in each search,and then a new individual fruit fly is produced to give a search by combining all excellent individuals in the iteration.The convergence speed can thus be greatly accelerated.Function test results show that IFOA has obviously improved the searching performance of FOA.IFOA is applied to aerodynamic optimization design,and the examples of airfoil inverse design and single/multi-objective optimization design demonstrate that IFOA is a simple and efficient optimization method,and can be widely used in aerodynamic optimization design.