bbo: Biogeography-Based Optimization

In bbo: Biogeography-Based Optimization

Description

Solves global optimization problems via Biogeography-Based Optimization method.

Usage

bbo(fn, lower, upper, DisplayFlag = TRUE, RandSeed, control = bbo.control(), ...)

Arguments

fn	the function to be optimized (minimized).
lower, upper	two vectors specifying scalar real lower and upper bounds on each parameter to be optimized, so that the i-th element of lower and upper applied to the i-th parameter. The implementation searches between lower and upper for the global optimum (minimum) of fn.
DisplayFlag	TRUE or FALSE, whether or not to display, default is TRUE
RandSeed	random number seed
control	a list of control parameters; see bbo.control.
...	further arguments to be passed to fn

Details

Given an objective function, this method performs biogeography-based optimization and returns the minimum cost for the given objective function.

Value

The output of the function bbo is a list containing the following elements:
prop, a list of control parameters for BBO for the current run:

• pModify

• pMutate

• KEEP

• popSize

• maxGen

• numVar

• orderDep

minCost, a list containing the following elements:

• bestMember: the best set of parameters found.

• bestValue: the value of fn corresponding to bestMember.

bestHabitat a list containing the following elements:

• itersBestValue: the best value of fn at each iteration.

• itersBestMember: the best member at each iteration.

• itersAvg: the average population cost at each iteration.

Author(s)

For package bbo: Sarvesh Nikumbh<[e-mail]> Maintainer: Sarvesh Nikumbh<[e-mail]>

For BBO method: Prof. D. Simon, Cleveland State University, Ohio.

References

D. Simon, "Biogeography-Based Optimization", IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702-713, December 2008.

See Also

bbo.control for control arguments

Examples

	## --------------------
	## Rosenbrock function:
	## --------------------
	## It has a global minimum f(x) = 0 at (1,1).  
	## Kindly note that the first parameter passed to the 
	## objective function should be the vector of parameters
	## to be optimized.
	Rosenbrock <- function(x){
	  x1 <- x[1]
	  x2 <- x[2]
	  return(  100 * (x2 - x1 * x1)^2 + (1 - x1)^2 )
  	}

	bbo(Rosenbrock, -5, 5, control = 
		bbo.control(pMutate = 0.4, 
				numVar = 2, 
				popSize = 50, 
				KEEP = 5, 
				maxGen = 20))

bbo documentation built on May 1, 2019, 10:53 p.m.