Gaussian fitting based optimal design of aircraft mission success space using multi-objective genetic algorithm  

作　　者：Yuan GAO Yongliang TIAN Hu LIU Xue SUN 

机构地区：[1]School of Aeronautic Science and Engineering,Beihang University,Beijing 100083,China

出　　处：《Chinese Journal of Aeronautics》2020年第12期3318-3330,共13页中国航空学报（英文版）

摘　　要：In order to obtain the optimized aircraft design concept which meets the increasingly complex operation environment at the conceptual design stage,System-of-systems(So S)engineering must be considered.This paper proposes a novel optimization method for the design of aircraft Mission Success Space(MSS)based on Gaussian fitting and Genetic Algorithm(GA)in the So S area.First,the concepts in the design and evaluation of MSS are summarized to introduce the Contribution to System-of-Systems(CSS)by using a conventional effectiveness index,Mission Success Rate(MSR).Then,the mathematic modelling of Gaussian fitting technique is noted as the basis of the optimization work.After that,the proposed optimal MSS design is illustrated by the multiobjective optimization process where GA acts as the search tool to find the best solution(via Pareto front).In the case study,a simulation system of penetration mission was built.The simulation results are collected and then processed by two MSS design schemes(contour and neural network)giving the initial variable space to GA optimization.Based on that,the proposed optimization method is implemented under both schemes whose optimal solutions are compared to obtain the final best design in the case study.

关 键 词：EVALUATION Gaussian fitting Genetic algorithm Mission success space Neural network System-of-systems 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] V37[自动化与计算机技术—控制科学与工程] V271.4[航空宇航科学与技术—航空宇航推进理论与工程]