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Modified Firefly Algorithm

Author

Listed:
  • Surafel Luleseged Tilahun
  • Hong Choon Ong

Abstract

Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.

Suggested Citation

  • Surafel Luleseged Tilahun & Hong Choon Ong, 2012. "Modified Firefly Algorithm," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:467631
    DOI: 10.1155/2012/467631
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    References listed on IDEAS

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    1. Mishra, SK, 2006. "Repulsive Particle Swarm Method on Some Difficult Test Problems of Global Optimization," MPRA Paper 1742, University Library of Munich, Germany.
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    Cited by:

    1. Akemi Gálvez & Andrés Iglesias, 2013. "Firefly Algorithm for Polynomial Bézier Surface Parameterization," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    2. Shuhao Yu & Shanlin Yang & Shoubao Su, 2013. "Self‐Adaptive Step Firefly Algorithm," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    3. Phinit Tontragunrat & Sujin Bureerat, 2013. "Antioptimisation of Trusses Using Two‐Level Population‐Based Incremental Learning," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    4. Yi Han & Ikou Kaku & Jianhu Cai & Yanlai Li & Chao Yang & Lili Deng, 2013. "Shuffled Frog Leaping Algorithm for Preemptive Project Scheduling Problems with Resource Vacations Based on Patterson Set," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).

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    1. Mishra, SK, 2006. "Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multi-modal Benchmark Functions," MPRA Paper 1743, University Library of Munich, Germany.

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