Table 1. Settings of the algorithms used in the case studies.

Algorithm	Setting			Source
	Description	Abbreviation	Value
MC	Normal random sampling
LHS	Normal sampling along the HyperCube matrix			[3]
MLE	Percentage of repetitions dedicated as initial samples	burn-in	10%
MCMC	Percentage of repetitions dedicated as initial samples	burn-in	10%	[4]
SCE-UA	Number of parameters	dim		[1]
	Number of complexes	ngs	2(dim)
	Maximum number of evolution loops before convergence	kstop	50
	The percentage change allowed in kstop loops before convergence	pcento	10−5
	Convergence criterion	peps	10−4
SA	Starting temperature	Tini	10	[6]
	Number of trials per temperature	Ntemp	10
	Temperature reduction	alpha	0.99
DE-MC Z	Number of different chains to employ	nChains	2(dim)	[2]
	Number of pairs of chains to base movements	DEpairs	2
	Interval to save status	thin	1
	Factor to jitter the chains	eps	0.04
	Convergence criterion		0.9
	Automatic adaption		True
ROPE	Number of optimization cycles	subsets	5	[5]
	Acceptance ratio	percentage	0.05