A modified Ant Colony Optimization algorithm to solve a dynamic traveling salesman problem: A case study with drones for wildlife surveillance


Chowdhury S., Marufuzzaman M., TUNÇ H. , Bian L., Bullington W.

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, cilt.6, ss.368-386, 2019 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 6 Konu: 3
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.jcde.2018.10.004
  • Dergi Adı: JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
  • Sayfa Sayıları: ss.368-386

Özet

This study presents a novel Ant Colony Optimization (ACO) framework to solve a dynamic traveling salesman problem. To maintain diversity via transferring knowledge to the pheromone trails from previous environments, Adaptive Large Neighborhood Search (ALNS) based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes available in the literature. Numerical results indicate that the proposed immigrant schemes can handle dynamic environments efficiently compared to other immigrant-based ACOs. Finally, a real life case study for wildlife surveillance (specifically, deer) by drones has been developed and solved using the proposed algorithm. Results indicate that the drone service capabilities can be significantly impacted when the dynamicity of deer are taken into consideration. (C) 2018 Society for Computational Design and Engineering. Publishing Services by Elsevier.