Multidisciplinary Design Optimization(MDO) method that considers principles in various fields affecting big scale structure and system design at the same time is used. Because most variables are connected many engineering phenomena under the classic optimized design method(all-in-one design approach), it is hard to judge the meaning of final design solution obtained, and there are cases where all variables converge before reaching the optimal design value in large-scale design problems with many variables. Collaborative Optimization (CO) method, the most advanced MDO approach, is used to efficiently solve these optimum problems, to efficiently analyze design problems involving numerous design variables and constraints and in which various engineering phenomena occur. However, the application of the MDO problem to CO introduces a number of numerical problems by destroying the numerical properties of the original optimal design problem. Therefore, this study researches one solution by listing the problems of CO after organizing various approaches of MDO.

In system design, it is not always possible that all decision makers can cooperate fully and thus avoid conflict. They each control a specified subset of design variables and seek to minimize their own cost functions subject to their individual constraints. However, a system management team makes every effort to coordinate multiple disciplines and overcome such noncooperative environment. Although full cooperation is difficult to achieve, noncooperation also should be avoided as possible. Our approach is to predict the results of their cooperation and generate approximate Pareto set for their multiple objectives. The Pareto set can be obtained according to the degree of one's conceding coupling variables in the other's favor. We employ approximation concept for modelling this coordination and the mutiobjective genetic algorithm for exploring the coupling variable space for obtaining an approximate Pareto set. The approximation management concept is also used for improving the accuracy of the Pareto set. The exploration for the coupling variable space is more efficient because of its smaller dimension than the design variable space. Also, our approach doesn't force the disciplines to change their own way of running analysis and synthesis tools. Since the decision making process is not sequential, the required time can be reduced comparing to the existing multidisciplinary design optimization. This approach is applied to some mathematical examples and structural optimization problems.

다분야 통합 시스템의 설계문제는 다량의 설계변수와 구속조건으로 구성되며 다수의 공학적 현상으로 연관되어 있다. 다분야 통합 최적설계 문제를 효과적으로 다루기 위해서는 다양한 해석분야의 공학적 설계원리를 동시에 고려하여 균형 있고 유기적인 방법으로 최적의 설계를 결정하는 체계적인 설계자동화기술이 요구된다. 다분야 통합 설계문제를 위한 효율적인 설계방법론으로 분리기반 최적화 기법이 적용되는데 이 방법은 한 단위의 대규모 설계문제를 여러 개의 하부시스템으로 분리하여 독립적으로 최적화를 수행하고 각 하부 시스템으로부터의 설계해 사이의 중재 및 통합화를 거쳐 최종적으로 수렴된 최적설계를 찾는 방법이다. 본 논문에서는 분리기반 최적화기법을 다분야 통합최적 설계문제에 적용하는데 필요한 시스템분리기법을 유전알고리즘 및 다층 역전 파 신경회로망을 이용하여 정립하였다. 시스템분리기법을 검증하기 위해 최근 미국 Boeing사에서 개발중인 고속 민간항공기인 HSCT의 시뮬레이션기반 설계문제를 이용하였다. 대규모 설계시스템의 분리결과는 전체 설계문제의 특성을 파악하기 위한 자료로 활용되며 향후, 분리기반 최적화과정에서 최종적으로 통합된 최적설계를 탐색하는데 필요한 기반구조를 제공한다.