ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 147–154, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-147-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 147–154, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-147-2020

  03 Aug 2020

03 Aug 2020

ANT COLONY OPTIMIZATION PARAMETER SELECTION FOR SHORTEST PATH PROBLEM

N. Zarrinpanjeh1, F. Dadrass Javan2,3, H. Azadi3, P. De Maeyer3, and F. Witlox3 N. Zarrinpanjeh et al.
  • 1Department of Geomatics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
  • 2Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, Tehran, Iran
  • 3Department of Geography, Ghent University, Ghent, Belgium

Keywords: Ant Colony Optimization, Shortest Path Problem, Parameter Selection, Geo-Informatics

Abstract. The shortest path problem has been studied to be solved through diverse deterministic and also stochastic approaches such as Ant Colony Optimization. One of the most challenging issues with the implication of Ant Colony Optimization to solve the shortest path problem is parameter selection and tuning which is found crucial to improve the computational performance of problem-solving. To tune parameters, it is vital to observe the response of each parameter to different values and study their effect on the final results. In this research, two experiments are designed and conducted to study the behavior of parameters in terms of generated results and computational performance. In the first experiment, evaporation, updating, and transition rule parameters are studied by iterative execution of shortest path generation between nodes considering different parameter values. In the second experiment, the number of initial ants is studied. Inspecting the results, it is observed that to avoid premature stagnation decreasing α value is recommended. On the other hand, ρ is observed to be considered for tuning of speed and number of diffusions of the algorithm. Moreover, it is realized that a high Q value would result in more correct results. Inspecting the initial number of ants, a threshold is realized where increasing the number of ants over this threshold would drastically result in more optimized paths.