Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge

Algorithm 2

The framework of HCBBO algorithm.

  input: habitat size , maximum migration rate and (emigration and immigration rate), the maximum mutation rate ;

Initialize set of MLPs (habitats) by chaos maps on formula Eq. (8);

For each habitat, calculate its mean square error by relative parameters based on formulas (9). And the basic rule of fitness

function is the better performance maintains the smaller value of MSE. Then elite habitats are identified by the values of

HSI.

Combing MLPs according to immigration and emigration rates based on Eq. (6)

Probabilistically use immigration and emigration to modify each non-elite habitat based on Eq. (7).

Select number of MLPs and recomputed (mutate) some of their weights or biases by chaos maps.

Save some of the MLPs with low MSE;

This loop will be terminated if a predefined number of generations are reached or an acceptable problem solution has been

found, otherwise go to step (3) for the next iteration.

  output: the MLP with minimum MSE (HSI).