Research Article
CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy
Algorithm 1
Biogeography-Based Optimization.
| | START | | Initialize the parameters G = M = 1, Tmaximum = 1, PS and Max_iteration | | Initialize the populations (arbitrary group of habitats) H1, H2,........., | | Evaluate fitness value (HSI) for every habitat | | WHILE (ending condition is not found) | | Evaluate αa, βa and Ta for every habitat | | Obtain a //Migration | | FOR every habitat (from minimum to maximum HSI values) | | Choose habitat Ha (SIV) stochastically proportional to βa | | IF random < βa and Ha (SIV) choose, then | | Choose habitat Hb (SIV) stochastically proportional to αa | | IF random < αa and Hb (SIV) choose, then | | Ha (SIV) = Hb (SIV) | | END IF | | END IF | | END FOR//MUTATION | | Choose Ha (SIV) with the help of mutation possibility proportional Sa | | IF random < Ta then | | Arbitrary change the SIVs in Ha (SIV) | | END IF | | Evaluate HSI value | | END WHILE | | STOP |
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