An interactive multiobjective optimization design strategy for decision based multidisciplinary design

This research focuses on Multidisciplinary Design and Optimization (MDO) of large scale systems that have multiple objective functions. The primary goal is to develop and test an interactive multi-objective optimization algorithm for multidisciplinary system design that takes into account the Decision Maker's (DM's) preferences during the design process. An interactive Multi-Objective Optimization Design Strategy (iMOODS) developed in Tappeta and Renaud has been modified in this research to include an MDO algorithm to address both the multiobjective and multidisciplinary issues involved in system design. This interactive strategy (iMOODS with MDO capability) provides the DM with a formal means for efficient design exploration around a given Pareto point. The strategy has been successfully applied to two design problems which are multidisciplinary in nature and have multiple objective functions. The first problem is the design of an autonomous hovercraft system that has four complexly coupled disciplines or contributing analyses (CAs) and two objective functions. The second problem is an aircraft concept sizing problem that has three disciplines and three objective functions. The results indicate that the strategy is effective and efficient in capturing the DM's preferences and arriving at the optimum design.