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A dynamic adaptive firefly algorithm with globally orientation

Author

Listed:
  • Liu, Jingsen
  • Mao, Yinan
  • Liu, Xiaozhen
  • Li, Yu

Abstract

This paper proposes a dynamic adaptive firefly algorithm to overcome the disadvantages of the standard firefly algorithm, to improve the convergence rate and solution precision, and to avoid the premature algorithm trapping at the local extreme. It has a global-oriented moving mechanism and can dynamically adjust the step size and attractiveness. First, through the adaptive deviation degree strategy of optimal distance combining with the Gaussian distribution, it optimizes the fixed step-factor to balance the exploration and excavation capabilities of the algorithm and improves the diversity of the population. Second, minimum attractiveness is introduced to the algorithm, and is adaptively changed with the number of iterations, which can avoid random walk due to lack of traction between fireflies. Finally, this paper improves the mobility mechanism based on the position of the current optimal firefly. It enables firefly move with global orientation and also expands the sharing of information between fireflies to improve the overall evolutionary optimization performance of the algorithm. Theoretical analysis proves the convergence and time complexity of the improved algorithm. The simulation results of several test functions and engineering constraint optimization problems show that the improved algorithm has better solution performance, and clearly improves the convergence speed and solution accuracy.

Suggested Citation

  • Liu, Jingsen & Mao, Yinan & Liu, Xiaozhen & Li, Yu, 2020. "A dynamic adaptive firefly algorithm with globally orientation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 174(C), pages 76-101.
    • Handle: RePEc:eee:matcom:v:174:y:2020:i:c:p:76-101
    DOI: 10.1016/j.matcom.2020.02.020
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    References listed on IDEAS

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    1. Yu, Shuhao & Zhu, Shenglong & Ma, Yan & Mao, Demei, 2015. "A variable step size firefly algorithm for numerical optimization," Applied Mathematics and Computation, Elsevier, vol. 263(C), pages 214-220.
    2. Gherbi, Yamina Ahlem & Bouzeboudja, Hamid & Gherbi, Fatima Zohra, 2016. "The combined economic environmental dispatch using new hybrid metaheuristic," Energy, Elsevier, vol. 115(P1), pages 468-477.
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