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The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

Author

Listed:
  • Hongping Hu
  • Yangyang Li
  • Yanping Bai
  • Juping Zhang
  • Maoxing Liu

Abstract

The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO-BPNN, which is against the models: BPNN, SCA-BPNN, PSO-BPNN, MFO-BPNN, MVO-BPNN, and ALO-BPNN. It is shown that the predicted model IALO-BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems.

Suggested Citation

  • Hongping Hu & Yangyang Li & Yanping Bai & Juping Zhang & Maoxing Liu, 2019. "The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction," Complexity, Hindawi, vol. 2019, pages 1-12, August.
    • Handle: RePEc:hin:complx:1480392
    DOI: 10.1155/2019/1480392
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    References listed on IDEAS

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    1. Hu, Hongping & Wang, Haiyan & Bai, Yanping & Liu, Maoxing, 2019. "Determination of endometrial carcinoma with gene expression based on optimized Elman neural network," Applied Mathematics and Computation, Elsevier, vol. 341(C), pages 204-214.
    2. Mingyue Qiu & Yu Song, 2016. "Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-11, May.
    3. Ding, Yanming & Zhang, Wenlong & Yu, Lei & Lu, Kaihua, 2019. "The accuracy and efficiency of GA and PSO optimization schemes on estimating reaction kinetic parameters of biomass pyrolysis," Energy, Elsevier, vol. 176(C), pages 582-588.
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