Btukfyl

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

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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

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