Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Information Sciences: an International Journal
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Parameter estimation, a key step in establishing the kinetic models, can be considered as a numerical optimization problem. Many optimization techniques including evolutionary algorithms have been applied to it, yet their efficiency needs further improvement. This paper proposes a hierarchical differential evolution (HDE) in which individuals are organized in a hierarchy and mutation base is selected based on the hierarchical structure. Additionally, the scaling factor of HDE is adjusted according to both the hierarchy and the search process, elaborately balancing the exploration and exploitation. To demonstrate the performance of HDE, experiments are carried out on kinetic models of two chemical reactions: pyrolysis and dehydrogenation of benzene as well as supercritical water oxidation. The results show that the proposed algorithm is an efficient and robust technique for kinetic parameter estimation.