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Ant Colony Optimization for Transportation Optimization Problems – UTC

Project #: 4W1618
Start Date: 06/01/2007
End Date: 12/31/2011
Status: Completed

WTI leads numerous research projects to improve transportation in rural areas. A typical challenge in locations is the absence of seamless or reliable communication, due to the difficulty of effectively placing infrastructure in remote areas or rugged terrain. A WTI researcher has conducted preliminary research into the use of an optimization technique called “Ant Colony Optimization,” to investigate whether it can be applied to select optimal placement of communications infrastructure along a roadside. The initial research has included computational analysis, integration of digital elevation models, and development of a working algorithm. Ant Colony Optimization (ACO) is an artificial intelligence algorithm and is a form of “Swarm Intelligence.” Ant Colony Optimization algorithms mimic the behavior of ants searching for food. Ants deposit pheromones on the paths they follow when searching for food. The ants that find food survive and retrace their paths back to their homes, making the pheromone deposited along trails leading to food even stronger. Other ants follow pheromone-laden trails leading to food, and ultimately the shortest paths to food are found by the collective ant colony. Similarly, ACO explores potential paths to solutions of problems and increases the weights of paths leading to good solutions. Ultimately, ACO algorithms converge to near-optimal solutions for very complex problems, such as the infrastructure placement problem. This project will allow the researchers of the Systems Engineering Group to build on the previous research through enhancement of the estimation techniques and algorithm, generation of test cases based on real locations and equipment, and identification of other applications for the techniques used in the research.


The overall objective of this project is to formalize, test and enhance propagation estimate techniques used to select the optimal placement of communications infrastructure using artificial intelligence.


  • Doug Galarus
    Doug Galarus