Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Marufuzzaman, Mohammad
Committee Member
Smith, Brian K.
Committee Member
Czarnecki, Joby M.
Committee Member
Hamilton, Michael A.
Date of Degree
5-6-2017
Document Type
Graduate Thesis - Open Access
Major
Industrial and Systems Engineering
Degree Name
Master of Science
College
James Worth Bagley College of Engineering
Department
Department of Industrial and Systems Engineering
Abstract
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.
URI
https://hdl.handle.net/11668/17565
Recommended Citation
Bullington, William, "Modified Ant Colony Algorithm for Dynamic Optimization: A Case Study with Wildlife Surveillance" (2017). Theses and Dissertations. 3169.
https://scholarsjunction.msstate.edu/td/3169
Comments
drone routing||adaptive large neighborhood search||immigrant schemes||dynamic travelling salesman problem||Dynamic optimization||wildlife surveillance