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In view of the problems of the connection weights and thresholds of the extreme learning machine are randomly generated before training and remain unchanged during the training process, the number of hidden layer nodes is pre-allocated, and the hidden layer parameters are randomly selected. Too many hidden layer nodes not only make the network more complex but also reduce the generalization ability of the algorithm. Aiming at this problem, an improved crow search algorithm is proposed to optimize the extreme learning machine. Based on the analysis of the limitations of the original crow search algorithm, a particle swarm algorithm search strategy is proposed to enhance the global search capability. In the latter part of the algorithm iteration, Gaussian function is added, and the penalty coefficient of the function is used for local disturbance, gradually reducing the amplitude of the search trajectory, and then adaptively adjusting the parameters to avoid being attracted by local extremum. Finally, the improved crow search algorithm is used to optimize the hidden layer neurons and connection weights of the extreme learning machine neural network, so as to obtain accurate prediction results. Through function fitting, regression data set fitting and classification data set for classification experiment verification, the proposed algorithm has higher training speed and efficiency. At the same time, this method is not only significantly higher than the traditional ELM method, but also obtains a more compact network structure, which is an effective neural network optimization algorithm.

Improved Crow Search Algorithm Optimized Extreme Learning Machine Based on Classification Algorithm and Application