Ant Colony Optimization in the City of Chicago
Every day we rely on algorithms that solve complex problems and efficiently execute tasks. Some of the real-life examples are optimizing traffic lights or public transport schedules. In this project, data design studio CLEVER°FRANKE created a visual experience showing the multiple levels of decision-making driven by algorithms to showcase how an algorithm works.
CLEVER°FRANKE decides to visualize a complex algorithm that mimics nature and bridges abstract technology and the real world. They chose an Ant Colony Optimization (ACO) algorithm to bring their idea to life. The ACO algorithm imitates the behavior of ants seeking a path between their colony and food sources and is used to solve various optimization problems.
The final result is a digital art installation that visualizes the complexity of a moving Ant Colony that tries to find the shortest route between natural resources, in this case, all the parks in Chicago. To have ants walk the streets of the city, CLEVER°FRANKE created a high-resolution digital map. Using data from OpenStreetMaps, they color-coded places in each individual park or leisure area.
CLEVER°FRANKE’s visualization software ensured Ant Algorithm’s problem fit the layout and size of the 150 Media Stream perfectly and allowed for the different levels of problem-solving to be shown. To handle the incredibly high resolution of the video wall, they wrote custom software to run massive video files in parallel.
Credits
Thomas Clever — Creative Direction
Gert Franke — Creative Direction
Wouter van Dijk — Creative Direction
Roel de Jonge — Visual Design
Jonas Groot-Kormelink — Visual Design; Application Design/Architecture
Agathe Lenclen — Application Design/Architecture
Wilco Tomassen — Application Design/Architecture