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$f : D\rightarrow \mathbb{R}$, where $D \in \mathbb{R}^n$.

x_{opt} = arg \ min_{x \in D}f(x)

$D = [l_1,u_1]×[l_2,u_2]×...×[l_n,u_n]$.

$f_{opt} = f(x_{opt})$
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Optimization algorithm is a method of producing a series of points $x_1,x_2,\ldots,x_m \in D$.
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$f_{best} = min_{i\in \{1,2,...,m\} } f (x_i )$.
$f_{best} - f{opt}$ is called optimization error.
The “best” optimization algorithm solves a problem accurately (effectively) and fast (efficiently).