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Ant Colony Optimization Application to GPS Surveying Problems: InterCriteria Analysis

  • Stefka Fidanova
  • Vassia Atanassova
  • Olympia Roeva
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 559)

Abstract

Ant Colony Optimization (ACO) has been used successfully to solve hard combinatorial optimization problems. This metaheuristics method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes between their colonies to feeding sources and back. In this paper, ACO algorithms are developed to provide near-optimal solutions for Global Positioning System surveying problem (GSP). In designing Global Positioning System (GPS) surveying network, a given set of earth points must be observed consecutively (schedule). The cost of the schedule is the sum of the time needed to go from one point to another. The problem is to search for the best order in which this observation is executed, minimizing the cost of the schedule. We apply InterCriteria Analysis (ICrA) on the achieved results. Based on ICrA we examine some relations between considered GSPs and ACO algorithm performance.

Keywords

InterCriteria Analysis Ant Colony Optimization GPS surveying 

Notes

Acknowledgements

This work was partially supported by two grants of the Bulgarian National Scientific Fund: DFNI-I-02/20 “Efficient Parallel Algorithms for Large Scale Computational Problems” and DFNI I02/5 “InterCriteria Analysis. A New Approach to Decision Making”.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Stefka Fidanova
    • 1
  • Vassia Atanassova
    • 2
  • Olympia Roeva
    • 2
  1. 1.Institute of Information and Communication TechnologyBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Institute of Biophysics and Biomedical EngineeringBulgarian Academy of SciencesSofiaBulgaria

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