MEIGOR (version 1.6.0)

rvnds_hamming: Main VNS function

Description

VNS Kernel function

Usage

rvnds_hamming(problem, opts, ...)

Arguments

problem

List containing problem settings definition.

opts

List containing options (if set as opts <- numeric(0) default options will be loaded).

...

Additional variables passed to the objective function

Value

fbest

Best objective function value found after the optimization

xbest

Vector providing the best function value

cpu_time

Time in seconds consumed in the optimization

func

Vector containing the best objective function value after each iteration

x

Matrix containing the best vector after each iteration

time

Vector containing the cpu time consumed after each iteration

neval

Vector containing the number of function evaluations after each iteration

numeval

Number of function evaluations

Details

problem$f: Name of the file containing the objective function (String). problem$x_L: Lower bounds of decision variables (vector). problem$x_U: Upper bounds of decision variables (vector). problem$x_0: Initial point(s) (optional; vector or matrix). problem$f_0: Function values of initial point(s) (optional). These values MUST correspond to feasible points. User options:

opts$maxeval: Maximum number of function evaluations (Default 1000). opts$maxtime: Maximum CPU time in seconds (Default 60). opts$maxdist: Percentage of the problem dimension which will be perturbed in the furthest neighborhood (varies between 0 and1, default is 0.5). opts$use_local: Uses local search (1) or not (0). The default is 1. The following options only apply when the local search is activated: opts$use_aggr: Aggressive search. The local search is only applied when the best solution has been improved (1=aggressive search, 0=non-aggressive search, default:0). opts$local search type: Applies a first (=1) or a best (=2) improvement scheme for the local search (Default: 1). opts$decomp: Decompose the local search (=1) using only the variables perturbed in the global phase. Default: 1.

Examples

	rosen10<-function(x){
		f<-0;
		n=length(x);
		for (i in 1:(n-1)){
			f <- f + 100*(x[i]^2 - x[i+1])^2 + (x[i]-1)^2;
		}
		return(f)
	}

	nvar<-10;

	problem<-list(f="rosen10", x_L=rep(-5,nvar), x_U=rep(1,nvar))

	opts<-list(maxeval=2000, maxtime=3600*69, use_local=1, aggr=0, local_search_type=1, decomp=1, maxdist=0.5)

	algorithm<-"VNS";

	Results<-MEIGO(problem,opts,algorithm);