Aiming at the problem that the algorithm is sensitive to initial value,easy to fall into local optimum and slow in conver-gence when training MLP,an improved algorithm IAVOA was proposed for the heuristic algorithm African vulture optimization algorithm.The logistic chaotic mapping was introduced when initializing the population to increase the diversity of the popula-tion.Adaptive coefficients were added to the optimal vultures and sub optimal vultures,and the guiding effects of these two types of vultures on ordinary vultures were automatically adjusted.The improved algorithm IAVOA was used to train MLP.To im-prove the accuracy of MLP,the average value of mean square error was used as the fitness function to find the best combination of MLP connection weight and deviation.Four classification datasets were selected to compare the performance of MLP for data classification between IAVOA algorithm and existing classical algorithm after MLP training.Simulation results show that the MLP trained using IAVOA algorithm has better performance indicators in data classification,and the improved algorithm has the advantages of strong global search capability,high convergence speed and high convergence accuracy.
维普
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