We present two algorithms within the framework of the Ant Colony Optimization (ACO) metaheuristic. The rst algorithm seeks to increase the exploration bias of Gambardella et al.'s (2012) Enhanced Ant Colony System (EACS) model, a model which heavily increases the exploitation bias of the already highly exploitative ACS model in order to gain the bene t of increased speed. Our algorithm aims to strike a balance between these two models. The second is also an extension of EACS, based on Jayadeva et al.'s (2013) EigenAnt algorithm. EigenAnt aims to avoid the problem of stagnation found in ACO algorithms by, among other unique properties, utilizing a selective rather than global pheromone evaporation model, and by discarding heuristics in the solution construction phase. A performance comparison between our two models, the legacy ACS model, and the EACS model is presented. The Sequential Ordering Problem (SOP), one of the main problems used to demonstrate EACS, and one still actively studied to this day, was utilized to conduct the comparison.


Computer Science & Engineering Department

Degree Name

MS in Computer Science

Graduation Date


Submission Date

July 2013

First Advisor

Abdelbar, Ashraf

Committee Member 1

Rafea, Ahmed

Committee Member 2

Moustafa, Mohamed


62 p.

Document Type

Master's Thesis

Library of Congress Subject Heading 1

Mathematical optimization.

Library of Congress Subject Heading 2

Operations research.


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