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通过引入快速非支配排序算法、拥挤距离以及拥挤距离比较算子等对基本遗传算法进行改进,并结合massage passing interface(MPI)并行编程技术,发展了主从式并行多目标遗传算法(PMGA).将PM-GA与排气系统型面参数化设计方法、Navier-Stokes方程求解器相结合建立了分开式排气系统气动优化设计平台.应用该平台对某型分开式排气系统进行了多目标优化设计,得到了一组在三个目标上都优于初始设计的Pareto最优设计.将典型的Pareto最优设计和初始设计进行分析、比较,证明了该气动优化设计平台的高效性和可靠性.
This paper improves the basic genetic algorithm by introducing fast non-dominated sorting algorithm, crowding distance and crowding distance comparison operators, and develops the master-slave parallel multi-objective genetic algorithm (PMGA) by combining with the parallel programming technique of massage passing interface (MPI). The PM-GA is combined with the exhaust gas system parametric design method and the Navier-Stokes equation solver to establish a pneumatic optimization design platform for the exhaust system of a split exhaust system.Using this platform, a multi-objective The Pareto optimal design, which is superior to the original design on all three targets, is optimized through the optimization design.Comparing and analyzing the typical Pareto optimal design and initial design, it proves that the Pneumatic optimization design platform is efficient and reliable Sex.