控制理论与应用2011，Vol.28Issue(4)：561-566,6.
基于非支配排序遗传算法的多学科鲁棒协同优化方法
Multidisciplinary robust collaborative optimization based on non-dominated sorting genetic algorithm
摘要
Abstract
To the robust collaborative optimization(RCO) scheme with two-level multiobjective optimization structure, a solution strategy employing the non-dominated sorting genetic algorithm(NSGA-II) is proposed. In the process of non-dominated sorting,the feasibility of an individual is determined by its infeasibility degree and the threshold of infeasibility degree. The threshold of infeasibility degree is reduced gradually in the process of evolution. At the initial stage of genetic evolution, the individuals with smaller values of objective function and standard deviation are more likely to be preserved to ensure the optimization process for reaching the neighborhood of the global extremum. In the following stages of genetic evolution, the individuals with smaller value of infeasibility degree are more likely to be preserved to enhance the interdisciplinary compatibility. The convergence of the results of RCO to the local extremum is usually avoided while keeping the desired interdisciplinary consistency. The results of validation by using typical examples show that the proposed approach is efficient.关键词
鲁棒协同优化/NSGA-Ⅱ算法/多目标/学科间一致性Key words
robust collaborative optimization/ NSGA-Ⅱ algorithm/ multiobjeetive/ interdisciplinary consistency分类
信息技术与安全科学引用本文复制引用
李海燕,马明旭,井元伟..基于非支配排序遗传算法的多学科鲁棒协同优化方法[J].控制理论与应用,2011,28(4):561-566,6.基金项目
国家"863"计划资助项目(2009AA04Z104). （2009AA04Z104）
