Preliminary Design of an Autonomous Underwater Vehicle Using a Multiple-Objective Genetic Optimizer

The process developed herein uses 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 complete synthesis model is comprised of an input module, the three primary AUV synthesis modules, a constraint module, three objective modules, and a genetic algorithm. The effectiveness rating 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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Aerospace and Ocean Engineering

Department dc:contributor.department

Aerospace and Ocean Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2008

Author dc:creator

• Martz, Matthew

Chair dc:contributor.committeechair

• Neu, Wayne L.

Committee members dc:contributor.committeemember

• Stilwell, Daniel J.
• Brown, Alan J.

• MDO
• Optimization
• Genetic
• Genetic Algorithm
• AUV
• Autonomous Underwater Vehicle

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-05272008-153619

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/33291