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(Chairman) date Dr. Gilbert L. Peterson (Member) date Dr. Gary B. Lamont (Member) date AFIT/GCS/ENG/07-15 Ant colony optimization algorithms have long been touted as providing an effec-tive and efficient means of generating high quality solutions to NP-hard optimization problems. Unfortunately, while the structure of the algorithm is easy to parallelize, the nature and amount of communication required for parallel execution has meant that parallel implementations developed suffer from decreased solution quality, slower runtime performance, or both. This thesis explores a new strategy for ant colony parallelization that involves Area of Expertise (AOE) learning. The AOE concept is based on the idea that individual agents tend to gain knowledge of different ar-eas of the search space when left to their own devices. After developing a sense o