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Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries
Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries
Theses and Dissertations
This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich's MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning and SARSA, with the modified ant colony optimization method, Ant-Q. …
Parallelization Of Ant Colony Optimization Via Area Of Expertise Learning, Adrian A. De Freitas
Parallelization Of Ant Colony Optimization Via Area Of Expertise Learning, Adrian A. De Freitas
Theses and Dissertations
Ant colony optimization algorithms have long been touted as providing an effective 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 areas of the search space …