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Issue:InterCriteria Analysis of Ant Colony Optimization Application to GPS Surveying Problems

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Title of paper: InterCriteria Analysis of Ant Colony Optimization Application to GPS Surveying Problems
Author(s):
Stefka Fidanova
Institute of Information and Communication Technology, Bulgarian Academy of Science, Bulgaria
stefka@parallel.bas.bg
Olympia Roeva
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., 1113 Sofia, Bulgaria
olympia@biomed.bas.bg
Marcin Paprzycki
Systems Research Institute, Polish Academy of Sciences, Warsaw, 6 Newelska Str., Warsaw, Poland
Management Academy, Warsaw, Poland
marcin.paprzycki@ibspan.waw.pl
Published in: "Issues in Intuitionistic Fuzzy Sets and Generalized Nets", Volume 12 (2015/2016), pages 20-38
Download:  PDF (1104  Kb, File info)
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.
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