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Particle swarm optimization for automatic creation of complex graphic characters

Author

Listed:
  • Fister, Iztok
  • Perc, Matjaž
  • Ljubič, Karin
  • Kamal, Salahuddin M.
  • Iglesias, Andres
  • Fister, Iztok

Abstract

Nature-inspired algorithms are a very promising tool for solving the hardest problems in computer sciences and mathematics. These algorithms are typically inspired by the fascinating behavior at display in biological systems, such as bee swarms or fish schools. So far, these algorithms have been applied in many practical applications. In this paper, we present a simple particle swarm optimization, which allows automatic creation of complex two-dimensional graphic characters. The method involves constructing the base characters, optimizing the modifications of the base characters with the particle swarm optimization algorithm, and finally generating the graphic characters from the solution. We demonstrate the effectiveness of our approach with the creation of simple snowman, but we also outline in detail how more complex characters can be created.

Suggested Citation

  • Fister, Iztok & Perc, Matjaž & Ljubič, Karin & Kamal, Salahuddin M. & Iglesias, Andres & Fister, Iztok, 2015. "Particle swarm optimization for automatic creation of complex graphic characters," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 29-35.
    • Handle: RePEc:eee:chsofr:v:73:y:2015:i:c:p:29-35
    DOI: 10.1016/j.chaos.2014.12.019
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    References listed on IDEAS

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    1. Luković, Mirko & Vanni, Fabio & Svenkeson, Adam & Grigolini, Paolo, 2014. "Transmission of information at criticality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 430-438.
    2. Akemi Gálvez & Andrés Iglesias, 2013. "Firefly Algorithm for Polynomial Bézier Surface Parameterization," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-9, September.
    3. Fister, Iztok & Perc, Matjaž & Kamal, Salahuddin M. & Fister, Iztok, 2015. "A review of chaos-based firefly algorithms: Perspectives and research challenges," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 155-165.
    4. Perc, Matjaž & Grigolini, Paolo, 2013. "Collective behavior and evolutionary games – An introduction," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 1-5.
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    Cited by:

    1. Jun, Luo & Liheng, Liu & Xianyi, Wu, 2015. "A double-subpopulation variant of the bat algorithm," Applied Mathematics and Computation, Elsevier, vol. 263(C), pages 361-377.

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