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An Advanced DNA‐Inspired Gray Wolf Algorithm for Kinetic Parameter Estimation in Supercritical Water Oxidation

Author

Listed:
  • Zhenhua Qin
  • Qilai Liang
  • Xiang Fu

Abstract

Inspired by the genetic evolution mechanism of DNA, a hybrid DNA Gray Wolf Optimizer (hDNA‐GWO) is proposed to develop an accurate kinetic model. This algorithm incorporates innovative DNA encoding, selection, crossover, and mutation operators inspired by genetic processes. We adopt the roulette‐wheel method to select individuals with greater environmental adaptability from the current population to form the next population. The crossover operation involves swapping gene segments between paired chromosomes to create new individuals and maintain the population diversity. The mutation operator can maintain the diversity of the population, avoid the phenomenon of “premature” convergence, and effectively improve the local search capability. The performance of hDNA‐GWO is investigated on typical benchmark functions compared to GWO, PSO, GWO‐PSO, and GWO‐GA. In addition, the superior search capabilities of our model are validated by kinetic parameter estimation using experimental data from supercritical water oxidation processes. The results indicate that the hDNA‐GWO can overcome premature convergence and obtain higher‐quality global optimal solutions.

Suggested Citation

  • Zhenhua Qin & Qilai Liang & Xiang Fu, 2025. "An Advanced DNA‐Inspired Gray Wolf Algorithm for Kinetic Parameter Estimation in Supercritical Water Oxidation," Complexity, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:complx:v:2025:y:2025:i:1:n:5523778
    DOI: 10.1155/cplx/5523778
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    References listed on IDEAS

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    1. Narinder Singh & S. B. Singh, 2017. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance," Journal of Applied Mathematics, Hindawi, vol. 2017, pages 1-15, November.
    2. Narinder Singh & S. B. Singh, 2017. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance," Journal of Applied Mathematics, John Wiley & Sons, vol. 2017(1).
    3. Shasha Yang & Anjie Jin & Wen Nie & Cong Liu & Yu Li, 2022. "Research on SSA-LSTM-Based Slope Monitoring and Early Warning Model," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
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