Abstract
Parameter identification is a key step in establishing kinetic models. Aimed at the above problem, it can be transformed into an optimization problem by constructing objective function that minimizes simulation errors. In this study, a novel swarm intelligence optimization algorithm-artificial bee colony algorithm is used. In the experiments, each variable is optimized according to its own reasonable scope. Then, two examples of kinetic models are analyzed and their computation results are compared with that of modified genetic algorithm, standard particle swarm optimization and its modified algorithms. The results show that artificial bee colony algorithm has good adaptability to various problems and better optimization precision. Moreover, it needs few control parameters of algorithm. So it is an effective optimization method.
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Chen, G., Wang, J., Li, C. (2012). Identification of Parameters in Kinetic Models Using Artificial Bee Colony Algorithm. In: Zeng, D. (eds) Advances in Information Technology and Industry Applications. Lecture Notes in Electrical Engineering, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-26001-8_42
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DOI: https://doi.org/10.1007/978-3-642-26001-8_42
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