Multi-objective optimization of an autonomous underwater vehicle

A design optimization process for an autonomous underwater vehicle (AUV) is developed using a multiple objective genetic optimization (MOGO) algorithm. The optimization is implemented in ModelCenter (MC) from Phoenix Integration. It uses a genetic algorithm that searches the design space for optimal, feasible designs by considering three measures of performance (MOPs): cost, effectiveness, and risk. The synthesis model is comprised of an input module, three primary AUV synthesis modules, a constraint module and three objective modules. The effectiveness determined by the synthesis model is based on nine attributes identified in the US Navy´s UUV Master Plan and four performance-based attributes calculated by the synthesis model. To solve multi-attribute decision problems the Analytical Hierarchy Process (AHP) is used. Once the MOGO has generated a final generation of optimal, feasible designs the decision-maker(s) can choose candidate designs for further analysis. A sample AUV Synthesis was performed and five candidate AUVs were analyzed.