rvnds_hamming: Main VNS function
In MEIGOR: MEIGO - MEtaheuristics for bIoinformatics Global Optimization

Description Usage Arguments Details Value Examples

VNS Kernel function

1	rvnds_hamming(problem, opts, ...)

`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

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.

`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

	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);