我想在 beamer 框架中包含以下算法。我的 MWE 如下所示
\documentclass[11pt]{beamer}% http://ctan.org/pkg/beamer
\mode<presentation>
\usetheme{Madrid}
\usepackage{float}
\usepackage{adjustbox}
\usepackage{algpseudocode,algorithm,algorithmicx}
\setbeamertemplate{frametitle}[default][center]
\usepackage{xkeyval}
\usepackage{etoolbox}
\usepackage{ragged2e}
\apptocmd{\frame}{}{\justifying}{} % Allow optional arguments after frame.
\hypersetup{colorlinks=true,allcolors=blue}
\setbeamertemplate{caption}[numbered]
\setbeamertemplate{bibliography entry title}{}
\setbeamertemplate{bibliography entry location}{}
\setbeamertemplate{bibliography entry note}{}
\usepackage{hyperref}
\usepackage{graphicx}
\usepackage{epstopdf}
\begin{frame}
\frametitle{\centerline{\textbf{INTRODUCTION}}}
%\framesubtitle{\centerline{frame subtitle}}
\begin{algorithm}[H]
\caption{Pseudocode for the GWO}
\label{gwo}
\begin{algorithmic}[1]
% \Procedure{Hybrid BAT-Genetic Algorithm}{}
\\Input :Grey wolf population:$X_{i}(i = 1, 2, ..., n)$,Maximum Number of iteration:Max\_it
\\Output :$X_{a}$ : Optimal Position(Optimized filter coefficients)
\\Objective function : PSNR
\hrule
\\Initialize the Grey wolf population $X_{i} = (i=1,2, ...,n)$
\\Initialize the coefficient vectors a, A, and C
\State $\vec{A}=2\vec{a}\vec{r_1}-\vec{a}$
\State $\vec{C}=2\vec{r_2}$
\Comment where components of are linearly decreased from 2 to 0 over the course of iterations and,\\ are random vectors in $[0,1]$
\For{all $X_{i}$}
\State Calculate fitness $F(X_{i})$ of all $X_{i}$
\EndFor
\State Rank the gray wolf in descending order based on the fitness
\State $X_{\alpha}$ = the first search agent
\State $X_{\beta}$ = the second search agent
\State $X_{\delta}$ = the third search agent
\State t=1;
\While{t\textless Max\_it}
\For{i=1:n}
\State Update the position of the current search agent $\vec{F}(t+1) = \frac{\vec{F_{1}}+\vec{F_{2}}+\vec{F_{3}}}{3}$
\EndFor
\State Update $a$,$A$, and $C$
\For{all $X_{i}$}
\State Calculate fitness $F(X_{i})$ of all $X_{i}$
\EndFor
\State Update $X_{\alpha}$, $X_{\beta}$, and $X_{\delta}$
\State t=t+1;
\EndWhile
\State Return the first best agent $X_{\alpha}$ found so far
\\Post-processing the results and visualization
% \EndProcedure
\end{algorithmic}
\end{algorithm}
\end{frame}
\end{document}
答案1
我把你的算法分成两个框架......
以下是代码：
\documentclass[11pt]{beamer}% http://ctan.org/pkg/beamer
\mode<presentation>
\usetheme{Madrid}
\usepackage{float}
\usepackage{adjustbox}
\usepackage{algpseudocode,algorithm,algorithmicx}
\usepackage{algcompatible}
\setbeamertemplate{frametitle}[default][center]
\usepackage{caption}
\usepackage{xkeyval}
\usepackage{etoolbox}
\usepackage{ragged2e}
\apptocmd{\frame}{}{\justifying}{} % Allow optional arguments after frame.
\hypersetup{colorlinks=true,allcolors=blue}
\setbeamertemplate{caption}[numbered]
\setbeamertemplate{bibliography entry title}{}
\setbeamertemplate{bibliography entry location}{}
\setbeamertemplate{bibliography entry note}{}
\usepackage{hyperref}
\usepackage{graphicx}
\usepackage{epstopdf}
\begin{document}
\begin{frame}
\frametitle{\centerline{\textbf{INTRODUCTION}}}
%\framesubtitle{\centerline{frame subtitle}}
\begin{algorithm}[H]
\caption{Pseudocode for the GWO}
\label{gwo}
\begin{algorithmic}[1]
% \Procedure{Hybrid BAT-Genetic Algorithm}{}
\\Input :Grey wolf population:$X_{i}(i = 1, 2, ..., n)$,Maximum Number of iteration:Max\_it
\\Output :$X_{a}$ : Optimal Position(Optimized filter coefficients)
\\Objective function : PSNR
\hrule
\\Initialize the Grey wolf population $X_{i} = (i=1,2, ...,n)$
\\Initialize the coefficient vectors a, A, and C
\State $\vec{A}=2\vec{a}\vec{r_1}-\vec{a}$
\State $\vec{C}=2\vec{r_2}$
\Comment where components of are linearly decreased from 2 to 0 over the course of iterations and,\\ are random vectors in $[0,1]$
\For{all $X_{i}$}
\State Calculate fitness $F(X_{i})$ of all $X_{i}$
\EndFor
\algstore{myalg}
\end{algorithmic}
\end{algorithm}
\end{frame}
\begin{frame}
\frametitle{\centerline{\textbf{INTRODUCTION}}}
\begin{algorithm}[H]
\ContinuedFloat
% \caption{Pseudocode for the GWO}
\begin{algorithmic}[1]
\algrestore{myalg}
\State Rank the gray wolf in descending order based on the fitness
\State $X_{\alpha}$ = the first search agent
\State $X_{\beta}$ = the second search agent
\State $X_{\delta}$ = the third search agent
\State t=1;
\While{t\textless Max\_it}
\For{i=1:n}
\State Update the position of the current search agent \mbox{$\vec{F}(t+1) = \frac{\vec{F_{1}}+\vec{F_{2}}+\vec{F_{3}}}{3}$}
\EndFor
\State Update $a$,$A$, and $C$
\For{all $X_{i}$}
\State Calculate fitness $F(X_{i})$ of all $X_{i}$
\EndFor
\State Update $X_{\alpha}$, $X_{\beta}$, and $X_{\delta}$
\State t=t+1;
\EndWhile
\State Return the first best agent $X_{\alpha}$ found so far
\\Post-processing the results and visualization
% \EndProcedure
\end{algorithmic}
\end{algorithm}
\end{frame}
\end{document}