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针对传统卫星多学科优化(MDO)方法存在优化问题容易陷入局部最优或无法收敛、计算量过大等缺陷,该文通过改善优化模式、引入遗传算法(GA)的方式对卫星协作优化方法(CO)进行了改进。针对协作优化算法在优化过程中容易陷入局部最优的问题,提出了一种基于局部搜索过程的混合遗传算法(GALS)。使用经典的Rosenbrock函数问题进行了性能测试,发现该算法是一种柔性灵活、能不断扩展和进步的开放式算法,能在保持各学科自治的基础上,异步并行地搜索系统最优解。该算法在实际卫星设计中已进行了多次应用验证,均取得了比传统优化算法更好的效果。
Aiming at the defects that the traditional multidisciplinary optimization (MDO) method is easy to fall into the local optimum or unable to converge and the calculation is too large, this paper improves the satellite cooperation optimization method (GA) by improving the optimization mode and introducing the genetic algorithm (GA) CO) has been improved. Aiming at the problem that the collaborative optimization algorithm is apt to fall into the local optimum in the optimization process, a hybrid genetic algorithm (GALS) based on the local search process is proposed. Performance tests on the classical Rosenbrock function problem show that the proposed algorithm is a flexible and flexible open-ended algorithm that can be expanded and improved. As a result, the optimal system solution can be searched asynchronously and in parallel based on the autonomy of all disciplines. The algorithm has been used in real satellite design for many times, and has achieved better results than traditional optimization algorithms.