Journal of System Simulation
Abstract
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.
Recommended Citation
Chen, Yongzhang and Mo, Yuanbin
(2025)
"Gaussian Chaotic Fire Hawk Optimization Algorithm for Solving Dynamic Optimization Problems,"
Journal of System Simulation: Vol. 37:
Iss.
2, Article 11.
DOI: 10.16182/j.issn1004731x.joss.23-1128
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss2/11
First Page
436
Last Page
449
CLC
TQ021.8; TP391
Recommended Citation
Chen Yongzhang, Mo Yuanbin. Gaussian Chaotic Fire Hawk Optimization Algorithm for Solving Dynamic Optimization Problems[J]. Journal of System Simulation, 2025, 37(2): 436-449.
DOI
10.16182/j.issn1004731x.joss.23-1128
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