A Hybrid Model of Extreme Learning Machine Based on Bat and Cuckoo Search Algorithm for Regression and Multiclass Classification

Table 2

Steps of the learning algorithm.

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Step 1: initialize the basic parameters and set the loop termination criteria

Step 2: initialize the cuckoo individual, code the input weights and thresholds of ELM into the individual, and each individual represents an ELM network structure

Step 3: normalize the training data and random initial individual position and calculate the fitness value in line with equation (21)

Step 4: record the optimal position, obtain a group of new positions according to equation (15), calculate the fitness value, and determine the current optimal position

Step 5: compare the random numbers with ; if , update the position randomly; otherwise, it will not change

Step 6: take the new position as the starting point of BA, and randomly generate ; if , update the current optimal position; otherwise, go to step 7

Step 7: randomly generate ; if , replace with the current position or do not update

Step 8: calculate the fitness value of each individual, and determine the current optimal position and optimal value

Step 9: if the termination condition is met, proceed to the next step; otherwise, go to step 4

Step 10: the individual cuckoo is decoded into the input weights and thresholds of ELM; obtain the optimal ELM network structure according to these parameters

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