Applying to aerodynamic optimization an enhanced particle swarm optimization algorithm based on parallel exchange

2022

Xibei Gongye Daxue Xuebao, Vol 40, Iss 3, Pp 493-503 (2022)

The particle swarm optimization (PSO) algorithm is easy to implement and can obtain high-quality solutions to optimization problems. It is widely applied to nonlinear and difficult problems such as aerodynamic optimization. However, to solve multi-modal problems, it easily falls into locally optimal values, showing that its robustness is poor. In order to improve the robustness of the PSO algorithm, an enhanced particle swarm optimization algorithm based on parallel exchange (EPSOBPE) is proposed. The algorithm enhances the optimization capability and its robustness through the parallel evolution of the cuckoo search algorithm (CSA), PSO population, hierarchical exchange operation and reinforcement learning strategy. Therefore, the algorithm has both the global search capability of the CSA and the local capability of the PSO algorithm, thus making the EPSOBPE very robust. Functional test results show that the EPSOBPE has stronger robustness and adaptability to different problems than other intelligent optimization algorithms. Moreover, the EPSOBPE is applied to the aerodynamic optimization design of the RAE2822 airfoil and the M6 wing. Compared with other algorithms, the EPSOBPE is more robust, and its optimization capability is better.

article

Chinese

EDP Sciences, 2022.

粒子群优化算法, 布谷鸟搜索算法, 气动优化设计, 全局优化, Motor vehicles. Aeronautics. Astronautics, TL1-4050