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针对传统的迭代算法在求解航空发动机非线性模型存在的受初值影响不易收敛问题,引入粒子群算法进行求解,并针对粒子群算法的局部收敛现象对其进行改进,设计一种分层克隆粒子群算法:将种群分为顶层和底层两个种群,通过对顶层粒子的克隆选择和底层粒子的混沌变异,分别提高算法的局部搜索能力和全局搜索能力,有效避免了出现局部收敛等问题。对测试函数求解的结果表明改进算法性能较遗传算法和粒子群算法有显著提高。将改进算法应用于某型混合排气涡扇发动机性能仿真,也得到满意的结果。
Aiming at the problem that the traditional iterative algorithm is not easy to converge in solving the initial value of the nonlinear model of aeroengine, the particle swarm optimization is introduced to solve the problem and the local convergence of PSO is improved. A layered clone particle Swarm algorithm: the population is divided into top and bottom two populations. Through the clonal selection of the top particle and the chaotic variation of the underlying particles, the local search ability and the global search ability of the algorithm are improved respectively, which effectively avoids the problem of local convergence. The results of solving the test function show that the performance of the improved algorithm is significantly higher than the genetic algorithm and the particle swarm optimization algorithm. The improved algorithm is applied to simulate the performance of a certain hybrid exhaust turbofan engine and satisfactory results are also obtained.