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

1-1-2017

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

Comments

drone routing||adaptive large neighborhood search||immigrant schemes||dynamic travelling salesman problem||Dynamic optimization||wildlife surveillance

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