Abstract
Describes an evolved artificial neural network that is evolved by the particle swarm optimisation (PSO) algorithm. Compared with previous evolved ANNs, both the architecture and weights of this ANN are evolved by PSO, it means that the network architecture is adaptively adjusted, then the PSO algorithm is employed to evolve the nodes of ANNs with a given architecture. This process is repeated until the best network is accepted or the maximum number of generations has been reached. Some techniques, such as partial training algorithm (PT) and evolving added nodes (EAN), are used to maintain a closer behavioural link between the parents and their offspring, which will improve the efficiency of evolving ANNs. An ANN evolved is used in modelling a product quality estimator for a fractionator of the hydrocracking unit in the oil refining industry. The results show that the evolved ANN has good accuracy and generalisation ability.
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