Multi-Objective Optimization of Conceptual Design of Communication Satellites with a Two-Branch Tournament Genetic Algorithm

In spacecraft design, many specialized state-of-theart design tools are employed to optimize the performance of various subsystems. However, there is no structured system-level concept-definition process. Consequently, designers usually compromise some mission goals to satisfy only one primary design objectives. In this study, the conceptual stage of the spacecraft design process is formulated into a multiobjective discrete optimization problem. Such a problem is well suited for solution with a Genetic Algorithm, which is a global search technique that performs optimization-like tasks. The use of multiobjective design allows the designer to evaluate different design alternatives across the whole set of design objectives. This work addresses two key design objectives for the spacecraft design process: the minimization of total launch mass and the maximization of spacecraft overall reliability. To predict values for the objective and constraint functions, a satellite design tool, which includes a satellite sizing model and a deterministic reliability model, was built and integrated with a genetic algorithm that employs a two-branch tournament to address the dual objective problem. The multi-objective approach was successful in determining sets of discrete design parameters that would minimize the launch mass as well as maximize the reliability of a geostationary communications satellite, using specified payload requirements.