A novel improved fruit fly optimization algorithm for aerodynamic shape design optimization

Abstract

This paper presents a novel improved fruit fly optimization algorithm (IFOA) to effectively solve the aerodynamic design optimization problems, when used in conjunction with computational fluid dynamics (CFD). In IFOA, a new inertia weight function is employed to balance the global exploration and local fine-tuning by adjusting the search scope dynamically. To make this algorithm more universal and effective for continuous optimization problems, especially for the high-dimensional and multimodal complex optimization problems, a new clustering evolution strategy that incorporates the cooperative learning search and crossover operation introduced from real coded GA is developed to enhance the exploration ability and convergence rate of the original FOA. The numerical experiments and comparisons on several benchmark functions are provided, which indicates that the proposed IFOA significantly outperforms the original FOA as well as other competition evolutionary algorithms from literature in terms of comprehensive performance. The IFOA was finally integrated with CFD solver to perform three practical aerodynamic shape design optimization where the objective and constraint functions are highly nonlinear. The inverse design case has been successfully solved to illustrate the algorithm’s convergence capability and computational efficiency. The drag minimization problem with constraints on the lift coefficient and geometry is carefully conducted on a transonic airfoil and an isolated wing, and a remarkable drag reduction of 22.79% and 17.41%, respectively, is achieved, confirming the potential of IFOA for aerodynamic design optimization.