Multi-objective Multidisciplinary Design Optimization Methodology And It's Application In Satellite Constellation Design

With the modernization of space industry, it's necessary to develop a system methodology to get efficiency in design, manufacture and dispose satellite constellation. Multidisciplinary design optimization methodology has the capability to manage design information and harmonize several optimization processes. The core of multidisciplinary design optimization methodology is multidisciplinary design optimization algorithm.Multidisciplinary design optimization algorithm can be describe by the definitions of attribute analysis, subject and subject analysis, system analysis, subject cell and system cell, design and optimization. The challenges in multidisciplinary design optimization algorithm are information deficiency, disaccord in several optimization processes, and competitions in subjects. To solve these challenges, there are three approaches, to transform system to layers, to set accessorial objects and accessorial design attributes, expand design space.The author has get two derived Simulated Annealing algorithms, dispersed SA and SA-POWELL, the first is used for continuous and discrete variable mixing optimization, the second is used for continuous variable optimization. Aside, the author ameliorated the coding method and the evolution arithmetic operators of a evolution algorithm-NSGAH for multi-objective optimization.Objects decomposed multi-objective CO algorithm has been bring forward. It has adopted a balanceable computation framework, and decomposed system objects appropriate. It has used several optimization algorithms for different optimization problems, and has kept the subject self-governed and independent. Distributed realization of the algorithm has been discussed.Dividing design and decision-making can bring more self-determination in design, and make decision-making more justly. Apply objects decomposed multi-objective CO algorithm in the design of satellite constellation can make the relationships in subjects more clear, and make the information exchange more efficient. The example proved it's feasible and effective in actual engineering problems.