An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach
Authored by Gudrun Wallentin, Francis Oloo
Date Published: 2017
DOI: 10.3390/ijgi6010027
Sponsors:
Austrian Science Fund (FWF)
Platforms:
No platforms listed
Model Documentation:
ODD
Flow charts
Model Code URLs:
Model code not found
Abstract
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.
Tags
Agent-based modelling
Genetic Algorithms
Adaptation
Simulation
Dynamics
Optimization
ecology
systems
Wildlife
Odd protocol
Adaptive rulesets
Sensors
Data-management
Aware