Date of Graduation

5-2016

Document Type

Thesis

Degree Name

Bachelor of Science

Degree Level

Undergraduate

Department

Computer Science and Computer Engineering

Advisor/Mentor

Gashler, Michael

Committee Member/Reader

Patitz, Matthew

Committee Member/Second Reader

Panda, Brajendra

Abstract

Ant Colony Optimization (ACO) is an optimization algorithm designed to find semi-optimal solutions to Combinatorial Optimization Problems. The challenge of modifying this algorithm to effectively optimize over a continuous domain is one that has been tackled by several researchers. In this paper, ACO has been modified to use several variations of the algorithm for continuous spaces. An aspect of ACO which is crucial to its success when optimizing over a continuous space is choosing the appropriate object (solution component) out of an infinite set to add to the ant's path. This step is highly important in shaping good solutions. Important modifications to this component were made in this research include using a Gaussian distribution as well as incorporating vector direction (Informative Pheromone) when evaluating the expected pheromone amount at any given candidate solution component. The results show that any variation of the algorithm herein which utilizes Informative Pheromone provides more accurate results than the others.

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