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Open AccessArticle

An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

Department of Geoinformatics (Z_GIS), University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria
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Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(1), 27; https://doi.org/10.3390/ijgi6010027
Received: 14 October 2016 / Revised: 9 January 2017 / Accepted: 16 January 2017 / Published: 21 January 2017
Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used “at the end” to validate the conceptual understanding. In today’s data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis. View Full-Text
Keywords: adaptive rulesets; genetic algorithms; sensors; agent-based modelling; optimization adaptive rulesets; genetic algorithms; sensors; agent-based modelling; optimization
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    Link: https://www.openabm.org/model/5359
    Description: A copy of the model specified in this research can be accessed online from the indicated link
MDPI and ACS Style

Oloo, F.; Wallentin, G. An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach. ISPRS Int. J. Geo-Inf. 2017, 6, 27. https://doi.org/10.3390/ijgi6010027

AMA Style

Oloo F, Wallentin G. An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach. ISPRS International Journal of Geo-Information. 2017; 6(1):27. https://doi.org/10.3390/ijgi6010027

Chicago/Turabian Style

Oloo, Francis; Wallentin, Gudrun. 2017. "An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach" ISPRS Int. J. Geo-Inf. 6, no. 1: 27. https://doi.org/10.3390/ijgi6010027

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