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Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning

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  • Xin-She Yang

    (National Physical Lab, UK)

Abstract

Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm further to introduce chaos-enhanced firefly algorithm with automatic parameter tuning, which results in two more variants of FA. The author first compares the performance of these algorithms, and then uses them to solve a benchmark design problem in engineering. Results obtained by other methods will be compared and analyzed.

Suggested Citation

  • Xin-She Yang, 2011. "Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 2(4), pages 1-11, October.
    • Handle: RePEc:igg:jsir00:v:2:y:2011:i:4:p:1-11
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    Cited by:

    1. Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    2. 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.
    3. Sujata Dash & Ajith Abraham & Ashish Kr Luhach & Jolanta Mizera-Pietraszko & Joel JPC Rodrigues, 2020. "Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477198, January.

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