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Generative Art with Swarm Landscapes.

Diogo de Andrade1, Nuno Fachada1,2,3, Carlos M Fernandes3

  • 1School of Communication, Arts and Information Technology, Lusófona University, 1749-024 Lisboa, Portugal.

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Summary
This summary is machine-generated.

This study introduces a generative art project using Particle Swarm Optimization (PSO) to create 3D animations from synthetic landscapes. The research explores various objective functions and visual display methods for PSO exploration.

Keywords:
aesthetics modelcomputational aestheticsfunction landscapesparticle swarm optimizationprocedural content generationswarm art

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Art

Background:

  • Generative art often utilizes algorithms for creation.
  • Particle Swarm Optimization (PSO) is an optimization technique with potential for artistic applications.
  • Synthetic landscapes can be generated using diverse objective functions.

Purpose of the Study:

  • To present a novel generative art project.
  • To explore the use of Particle Swarm Optimization (PSO) for creating 3D animations.
  • To investigate different methods for visualizing PSO's search space exploration.

Main Methods:

  • Developing a generative art system that employs PSO.
  • Generating synthetic landscapes using various objective functions (mathematical, Perlin noise, image-based).
  • Implementing and analyzing methods for aesthetically displaying particle swarm behavior.

Main Results:

  • Successful generation of 3D animations through PSO on synthetic landscapes.
  • Identification of visually interesting artifacts arising from the process.
  • Demonstration of diverse landscape generation techniques impacting animation output.

Conclusions:

  • PSO is a viable algorithm for generative 3D art creation.
  • The choice of objective function significantly influences the resulting animation aesthetics.
  • Novel visual artifacts can emerge from algorithmic art generation.