nsga2R: R Based Non-dominated Sorting Genetic Algorithm II

In nsga2R: Elitist Non-Dominated Sorting Genetic Algorithm

R Based Non-dominated Sorting Genetic Algorithm II

Description

A fast and elitist multiobjective genetic algorithm based on R.

Usage

nsga2R(fn, varNo, objDim, lowerBounds = rep(-Inf, varNo), upperBounds = rep(Inf, varNo),
  popSize = 100, tourSize = 2, generations = 20, cprob = 0.7, XoverDistIdx = 5,
  mprob = 0.2, MuDistIdx = 10)

Arguments

fn	Objective functions to be minimized
varNo	Number of decision variables
objDim	Number of objective functions
lowerBounds	Lower bounds of each decision variable
upperBounds	Upper bounds of each decision variable
popSize	Size of population
tourSize	Size of tournament
generations	Number of generations
cprob	Crossover probability
XoverDistIdx	Crossover distribution index, it can be any nonnegative real number
mprob	Mutation probability
MuDistIdx	Mutation distribution index, it can be any nonnegative real number

Value

The returned value is a 'nsga2R' object with the following fields in additional to above NSGA-II settings:

parameters	Solutions of decision variables found
objectives	Non-dominated objective function values
paretoFrontRank	Nondomination ranks (or levels) that each non-dominated solution belongs to
crowdingDistance	Crowding distance of each non-dominated solution

Author(s)

Ching-Shih (Vince) Tsou [e-mail]

References

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002), " A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, 6(2), 182-197.

Examples

# find the non-dominated solutions of zdt3 test problem
results <- nsga2R(fn=zdt3, varNo=30, objDim=2, lowerBounds=rep(0,30), upperBounds=rep(1,30),
  popSize=50, tourSize=2, generations=50, cprob=0.9, XoverDistIdx=20, mprob=0.1,MuDistIdx=3)

plot(results$objectives)

nsga2R documentation built on May 23, 2022, 5:06 p.m.