documentclass[energies,article,accept,moreauthors,pdftex,10pt,a4paper]{mdpi}
usepackage{booktabs}
usepackage{array} newcommand{PreserveBackslash}[1]{lettemp=\#1let\=temp}
newcolumntype{C}[1]{>{PreserveBackslashcentering}m{#1}}
newcolumntype{R}[1]{>{PreserveBackslashraggedleft}m{#1}}
newcolumntype{L}[1]{>{PreserveBackslashraggedright}m{#1}}
usepackage{booktabs}
usepackage{multirow}
begin{document}
begin{algorithm}[H]
    caption{EDE.}
    label{pseudoEDE}

         Parameters initialization ${Max.iter,CR, POP, and h}$;
        Population generation using Equation (ref{eq:7}) ;
        For {h = 1:H}
         {
         Compute mutant vector using Equation (ref{eq:8});
        For{iter= 1:Max.iter}
        { Compute first trial vector with CR 0.3;
        If {$rand() leq 0.3$}
         {$mu_{j}=upsilon_{j}$\
         else\
        {$mu_{j}=x_{j}$}
    }


         Compute second trial vector with CR 0.6;
        If {$rand() leq 0.6$}
        { $mu_{j}=upsilon_{j}$\
        else\
        { $mu_{j}=x_{j}$ }
    }

        Compute third trial vector with CR 0.9;
        If {$rand() leq 0.9$}
        {$mu_{j}=upsilon_{j}$\
        else\
        {$mu_{j}=x_{j}$}
    }

        Create $4^{th}$ and $5^{th}$ trial vector using Equations (ref{4_trial}) and (ref{5_trial});
        Findout trial vector which is best ;
        $X_{new} gets$ best of $ mu_{j}$ ;
        Compare trial vector with target vector;
        If {$f({X_{new}}) < f (X_{j})$}
        {$X_{j} = X_{new}$}
    }
}
end{algorithm}

begin{algorithm}[H]
    caption{GWO.}
    label{pseudoGWO}
    Parameters initialization ${Maxiter, POP, D, alpha, beta, delta}$;
    Initial population of gray wolves generation $X{i} (i=1,2,...,n)$;
    $X(i,j)= rand (POP, D)$;
    While {iter $<$ Maxiter}
    {
        For{i=1:POP}
        {
            Compute fitness using Equation (ref{eq:17});
            If {fitness $ < alpha_{score} $ }
            {$alpha_{score}$=fitness;
                $alpha_{Pos}$= $X(i, :)$;}
            If {fitness $ > alpha_{score}$ and fitness$<beta_{score }$}
            {$beta_{score}$=fitness;
                $beta_{Pos}$= $X(i, :)$;}
            If {fitness $ > alpha_{score}$ and fitness$>beta_{score }$ and fitness$<delta_{score }$}
            {$delta_{score}$=fitness;
                $delta_{Pos}$= $X(i, :)$;}
        }
        For {i = 1:POP}
        {For {j = 1:D}
            { Create $r1$ and $r2$ randomly with rand command;
                Compute fitness coefficients A and C using Equations (ref{eq:ENP3}) and (ref{eq:ENP4});
                Update values of ${alpha, beta$, and $delta}$ using Equation (ref{H5})--(ref{H7});
            }
        }
    }
end{algorithm}par
vspace{12pt}
end{document}

usepackage{algorithm2e} % <============================================
usepackage{pseudocode} % <=============================================

Update values of $alpha$, $beta$, and $delta$ using Equation (ref{H5})--(ref{H7});

documentclass[energies,article,accept,moreauthors,pdftex,10pt,a4paper]{mdpi}

%usepackage{booktabs}
%usepackage{array}
usepackage{algorithm2e} % <============================================
usepackage{pseudocode} % <=============================================
usepackage{blindtext} % <======================= dummy text in document
%newcommand{PreserveBackslash}[1]{lettemp=\#1let\=temp}
%newcolumntype{C}[1]{>{PreserveBackslashcentering}m{#1}}
%newcolumntype{R}[1]{>{PreserveBackslashraggedleft}m{#1}}
%newcolumntype{L}[1]{>{PreserveBackslashraggedright}m{#1}}
%
%usepackage{multirow}

Title{Title}
newcommand{orcidauthorA}{0000-0000-000-000X}
Author{Firstname Lastname $^{1,dagger,ddagger}$orcidA{}, Firstname Lastname $^{1,ddagger}$ and Firstname Lastname $^{2,}$*}

% Authors, for metadata in PDF
AuthorNames{Firstname Lastname, Firstname Lastname and Firstname Lastname}

% Affiliations / Addresses (Add [1] after address if there is only one affiliation.)
address{%
$^{1}$ quad Affiliation 1; e-mail@e-mail.com\
$^{2}$ quad Affiliation 2; e-mail@e-mail.com}

pubvolume{xx}
issuenum{1}
articlenumber{5}
pubyear{2019}
copyrightyear{2019}
history{Received: date; Accepted: date; Published: date}



begin{document}
blindtext
begin{algorithm}%[H]
    caption{EDE.}
    label{pseudoEDE}

         Parameters initialization ${Max.iter,CR, POP, and h}$;
        Population generation using Equation (ref{eq:7}) ;
        For {h = 1:H}
         {
         Compute mutant vector using Equation (ref{eq:8});
        For{iter= 1:Max.iter}
        { Compute first trial vector with CR 0.3;
        If {$rand() leq 0.3$}
         {$mu_{j}=upsilon_{j}$\
         else\
        {$mu_{j}=x_{j}$}
    }


         Compute second trial vector with CR 0.6;
        If {$rand() leq 0.6$}
        { $mu_{j}=upsilon_{j}$\
        else\
        { $mu_{j}=x_{j}$ }
    }

        Compute third trial vector with CR 0.9;
        If {$rand() leq 0.9$}
        {$mu_{j}=upsilon_{j}$\
        else\
        {$mu_{j}=x_{j}$}
    }

        Create $4^{th}$ and $5^{th}$ trial vector using Equations (ref{4_trial}) and (ref{5_trial});
        Findout trial vector which is best ;
        $X_{new} gets$ best of $ mu_{j}$ ;
        Compare trial vector with target vector;
        If {$f({X_{new}}) < f (X_{j})$}
        {$X_{j} = X_{new}$}
    }
}
end{algorithm}
blindtext
begin{algorithm}%[H]
  caption{GWO.}
  label{pseudoGWO}
  Parameters initialization ${Maxiter, POP, D, alpha, beta, delta}$;
  Initial population of gray wolves generation $X{i} (i=1,2,...,n)$;
  $X(i,j)= rand (POP, D)$;
  While {iter $<$ Maxiter}
  {
    For{i=1:POP}
    {
      Compute fitness using Equation (ref{eq:17});
      If {fitness $ < alpha_{score} $ }
        {$alpha_{score}$=fitness;
         $alpha_{Pos}$= $X(i, :)$;}
      If {fitness $ > alpha_{score}$ and fitness$<beta_{score }$}
        {$beta_{score}$=fitness;
         $beta_{Pos}$= $X(i, :)$;}
      If {fitness $ > alpha_{score}$ and fitness$>beta_{score }$ and fitness$<delta_{score }$}
        {$delta_{score}$=fitness;
         $delta_{Pos}$= $X(i, :)$;}
    }
    For {i = 1:POP}
      {For {j = 1:D}
        { Create $r1$ and $r2$ randomly with rand command;
          Compute fitness coefficients A and C using Equations (ref{eq:ENP3}) and (ref{eq:ENP4});
          Update values of $alpha$, $beta$, and $delta$ using Equation (ref{H5})--(ref{H7});
        }
     }
  }
end{algorithm}
blindtext
end{document}