A Novel BBO Algorithm Based on Local Search and Nonuniform Variation for Iris Classification

Algorithm 1

The main procedure of NBBO.

	Begin
	Algorithmic parameters setting:
	Population size = 50,
	Maximum number of iterations G = 500,
	Maximum immigration rate ,
	Maximum emigration rate ,
	Mutation probability ,
	Number of elites retained .
	/ Initialization /
	Random generation of a set of initial habitats constitutes the initial population.
	Calculate the fitness value of each habitat in the population.
	/ is the condition for the end of the iteration /
	Sort the habitat in descending order according to the fitness value.
	The immigration rate and emigration rate of each habitat were calculated according to formula (1).
	/ End of initialization/
	/ Migration /
	Select according to the immigration probability
	/ is dimension /
Select according to the migration probability
	Randomly select a characteristic variable from
	Replace a random characteristic variable in with
	else
	Perform a local search on the characteristic variable
	according to formula (6) can get .
	Replace random characteristic variable in with .
	end if
	end for
	end for
	/ End of migration /
	/ Mutation /
	Calculate the probability of mutation according to and
	Selection of unmutated habitat based on
	Replace with a randomly generated by formula (8)
	Replace with a randomly generated by (9)
	end if
	end if
	end if
	end for
	/ End of mutation /
	Recalculate the habitat fitness values
	end while
	end