Smith, Brian K.
Czarnecki, Joby M.
Hamilton, Michael A.
Date of Degree
Graduate Thesis - Open Access
Industrial and Systems Engineering
Master of Science
James Worth Bagley College of Engineering
Department of Industrial and Systems Engineering
A novel Ant Colony Optimization (ACO) framework for a dynamic environment has been proposed in this study. This algorithm was developed to solve Dynamic Traveling Salesman Problems more efficiently than the current algorithms. Adaptive Large Neighborhood Search based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes in literature to maintain diversity via transferring knowledge to the pheromone trails from previous environments. Numerical results indicate that the proposed algorithm can handle dynamicity in the environment more efficiently compared to other immigrant-based ACOs available in the literature. A real-life case study for wildlife surveillance by unmanned aerial vehicles has also been developed and solved using the proposed algorithm.
Bullington, William, "Modified Ant Colony Algorithm for Dynamic Optimization: A Case Study with Wildlife Surveillance" (2017). Theses and Dissertations MSU. 3169.