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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。
Fruit fly optimization algorithm (FOA) is a new swarm intelligence optimization algorithm with good global convergence. In order to further improve the optimization performance of FOA, it is introduced into the aerodynamic optimization design to develop an improved fruit fly optimization algorithm (IFOA). IFOA dynamically adjusts the search step by introducing an inertia weight function, which effectively realizes the dynamic balance between global search and local search, and improves the overall search efficiency and accuracy of the algorithm. For each multi-dimensional optimization problem, IFOA only randomly perturbs One of the decision variables, and in each iteration step, all the excellent Drosophila individuals (feasible solutions) are combined to generate a brand new Drosophila individual for a search, greatly accelerating the convergence rate of the algorithm. Function test results show that, IFOA significantly improve the FOA performance optimization. The application of IFOA to aerodynamic optimization design, airfoil inverse design and single / multi-objective optimization design shows that IFOA is a simple and efficient optimization method and can be widely used in aerodynamic optimization design.