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The Artificial Fish Swarm Algorithm Improved by Fireworks Algorithm

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Abstract

The artificial fish swarm algorithm can achieve good convergence effects in the early stage, but in the late stage, the algorithm has the problems of slow convergence speed and low optimization accuracy, and it is easy to fall into local extremes, making the algorithm’s convergence effect poor. Therefore, the characteristics of fireworks algorithm are used to improve the deficiency of fish swarm algorithm that is easy to fall into local extreme value in the late stage, and FWA-artificial fish swarm algorithm is put forward. When the effect of artificial fish swarm algorithm is poor, the explosion, mutation, mapping, and selection operations of fireworks algorithm are introduced to increase the variability of artificial fish, so as to enhance the optimization speed and ability of the algorithm. Finally, the improved algorithm is tested by four typical complex functions which are difficult to find the optimal solution by traditional method. Simulation results prove that the algorithm has the advantages of faster optimization speed, higher precision, and stronger stability.

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Authors and Affiliations

  1. Information Engineering College, Tianjin University of Commerce, 300134, Tianjin, China

    Liyi Zhang & Teng Fei

  2. Economic College, Tianjin University of Commerce, 300134, Tianjin, China

    Mingyue Fu & Hongbo Li

Authors
  1. Liyi Zhang
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  2. Mingyue Fu
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  3. Teng Fei
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  4. Hongbo Li
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Correspondence to Teng Fei.

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Liyi Zhang, Fu, M., Fei, T. et al. The Artificial Fish Swarm Algorithm Improved by Fireworks Algorithm. Aut. Control Comp. Sci. 56, 311–323 (2022). https://doi.org/10.3103/S0146411622040101

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