mco (version 1.0.13)

nsga2: NSGA II MOEA

Description

Multicriterion optimization algorithm

Usage

nsga2(fn, idim, odim, ...,
        constraints = NULL, cdim = 0,
        lower.bounds = rep(-Inf, idim), upper.bounds = rep(Inf, idim),
        popsize = 100, generations = 100,
        cprob = 0.7, cdist = 5,
        mprob = 0.2, mdist = 10,
        vectorized=FALSE)

Arguments

fn

Function to be minimized

idim

Input dimension

odim

Output dimension

...

Arguments passed through to 'fn'

constraints

Constraint function

cdim

Constraint dimension

lower.bounds

Lower bound of parameters

upper.bounds

Upper bound of parameters

popsize

Size of population

generations

Number of generations to breed. If a vector, then the result will contain the population at each given generation.

cprob

Crossover probability

cdist

Crossover distribution index

mprob

Mutation probability

mdist

Mutation distribution index

vectorized

If TRUE, the objective and constraint functions must be vectorized, i.e. accept a matrix instead of a vector and return a matrix instead of a vector. The matrix is structured such that one individual parameter combination is c

Value

• If generation is an integer, a list describing the final population with components par, value and pareto.optimal. If generations is a vector, a list is returned. The i-th element of the list contains the population after generations[i] generations.

References

Deb, K., Pratap, A., and Agarwal, S.. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6 (8) (2002), 182-197.

See Also

zdt1 for more examples and a list of multiobjective test functions.

Examples

## Binh 1 problem:
binh1 <- function(x) {
  y <- numeric(2)
  y[1] <- crossprod(x, x)
  y[2] <- crossprod(x - 5, x - 5)
  return (y)
}
r1 <- nsga2(binh1, 2, 2,
           generations=150, popsize=100,
           cprob=0.7, cdist=20,
           mprob=0.2, mdist=20,
           lower.bounds=rep(-5, 2),
           upper.bounds=rep(10, 2))
plot(r1)

## VNT problem:
vnt <- function(x) {  
  y <- numeric(3)
  xn <- crossprod(x, x)
  y[1] <- xn/2 + sin(xn);
  y[2] <- (crossprod(c(3, -2), x) + 4)^2/8 + (crossprod(c(1, -1), x) + 1)^2/27 + 15
  y[3] <- 1/(xn + 1) - 1.1*exp(-xn)
  return (y)
}

r2 <- nsga2(vnt, 2, 3,
           generations=150, popsize=100,
           lower.bounds=rep(-3, 2),
           upper.bounds=rep(3, 2))
plot(r2)

## Example using constraints:
## minimize     f(x) = (x[1]^2, x[2]^2)
## subject to   g(x) = (sum(x) - 5) >= 0
f <- function(x) { x^2 }
g <- function(x) { sum(x) - 5 }
res <- nsga2(f, 2, 2, generations=500,
             lower.bounds=c(0, 0), upper.bounds=c(10, 10),
             constraints=g, cdim=1)
opar <-par(mfrow=c(1,2))
plot(res, xlab="y1", ylab="y2", main="Objective space")
plot(res$par, xlab="x1", ylab="x2", main="Parameter space")
par(opar)