Journal of System Simulation ›› 2025, Vol. 37 ›› Issue (2): 436-449.doi: 10.16182/j.issn1004731x.joss.23-1128

• Papers • Previous Articles    

Gaussian Chaotic Fire Hawk Optimization Algorithm for Solving Dynamic Optimization Problems

Chen Yongzhang, Mo Yuanbin   

  1. School of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
  • Received:2023-09-12 Revised:2023-10-19 Online:2025-02-14 Published:2025-02-10
  • Contact: Mo Yuanbin

Abstract:

There are many important chemical processes in the chemical industry rely on dynamic optimization with factors such as nonlinearity and discontinuity. In order to find a more efficient solution algorithm, Gaussian Chaotic fire hawk optimization algorithm is proposed based on the fire hawk optimization algorithm, which is used to solve such problems after parameterizing the control variables. The original way of initializing the populations is replaced using tent chaotic mapping in order to make more sense of the initial distribution of the algorithm; a more targeted update method has been proposed in the analysis of fire hawk location updates and prey location updates, enhancing the ability to develop and explore algorithms, Gaussian sampling is also embedded to improve the diversity of the population, further enhancing the algorithm's local search and dynamic adaptation capabilities. The results show the effectiveness of the algorithm in solving chemical dynamic optimization problems.

Key words: chemical dynamic optimization, Gauss sampling, control variable parameterization, chaotic mapping, fire hawk optimization algorithm

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