下午好 ！
在 Latex 下，我创建了一个长表：
\documentclass[times]{iapress}
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\usepackage{colortbl}
\usepackage[margin=1cm]{caption}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{float}
\usepackage{graphicx}
\graphicspath{{figures/}}
\usepackage{longtable}
%\usepackage{algorithm}
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\usepackage[ ruled,vlined]{algorithm2e}
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\begin{document}
%% longtable of results
\usepackage{tikz}
\pagestyle{empty}
\usepackage{everypage}
\usepackage{lipsum}
\usepackage{afterpage}
\newcommand{\Lpagenumber}{\ifdim\textwidth=\linewidth\else\bgroup
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\renewcommand\thetable{11}
\setlength\LTleft{-1cm}
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\footnotesize
\begin{longtable}{|c|c|l|l|l|cl|}
\caption{Some simulation results of the suggested hybrid optimizer}
\\
\hline
Test function
& Study domain
& \multicolumn{2}{c|}{\begin{tabular}[c]{@{}c@{}}
initial obtained solution (Algorithm 2 )
\end{tabular}}
& \multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}
Obtained hybrid\\
optimizer solution\\
(Algorithm 3 )
\end{tabular}}
& \multicolumn{1}{l}{\begin{tabular}[c]{@{}c@{}}
Objective\\
function\\
value of the\\
solution
\end{tabular}}
& \multicolumn{1}{c|}{} \\*
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Sphere \\ ( dim = 13 ) \end{tabular}}
& \multirow{13}{*}{$ [-6,6]^{13} $ }
& $ Nbre\_iter=5 \;\; N=20 \; \; P=4 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=-0.553850768$ \\
$X2=-1.493074761$\\
$X3=0.6767506500$ \\
$X4=-0.602909613$ \\
$X5=-1.047292736$ \\
$X6=-0.191411525$ \\
$X7=-0.079137285$ \\
$X8=0.2588126665$ \\
$X9=-1.444664397$ \\
$X10=-0.92061842$ \\
$X11=0.424461785$ \\
$X12=-0.38351937$ \\
$X13=-0.15875219$ \end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=-4.9999e-09$ \\
$X2=-5.0000e-09$ \\
$X3=-5e-09$ \\
$X4=-5e-09$ \\
$X5=-5e-09$ \\
$X6=-5e-09$ \\
$X7=-5.000003e-09$ \\
$X8=-4.307553e-09$ \\
$X9=-5.0000006e-09$ \\
$X10=-5e-09$ \\
$X11=-5.000723e-09$ \\
$X12=-5.392374e-09$ \\
$X13=-5.00000000053859e-09$ \end{tabular}}
& \multirow{13}{*}{$ 3.22640e-16 $ } & \\*
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =50 $\\
$\alpha=0.1$\\
$\gamma=0.99$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Himmelblau’s
( dim = 2) \end{tabular}}
& \multirow{13}{*}{$ [-6,6]^{2} $ }
& $N\_ter=6 \; N=40 \; P=5 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=3.156640$ \\
$X2=2.194130$\\
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=2.99876$ \\
$X2=2.01136$ \\
\end{tabular}}
& \multirow{13}{*}{$ 0.001983 $ } & \\*
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =100 $\\
$\alpha=0.01$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Rastrigin function ( n = 8 )
\end{tabular}}
& \multirow{13}{*}{$ [-5.12,5.12]^{8} $ }
& $N\_ter=13\; N=40 \; P=10 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=0.2634143048$ \\
$X2=0.9917922607$\\
$X3=1.2036587692$ \\
$X4=1.0961852101$\\
$X5=1.1506293024 $ \\
$X6=0.0556164503$ \\
$X7=0.03749775658$ \\
$X8=0.14211747971 $
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=-0.007194304$ \\
$X2=1.0007044373$ \\
$X3= 0.994805762 $ \\
$X4=0.9897889558$ \\
$X5=0.9949586327$ \\
$X6=-0.004661976$ \\
$X7=0.00352526731$ \\
$X8=-0.00242884$ \\
\end{tabular}}
& \multirow{13}{*}{$ 4.00990032 $ } & \\*
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =50 $\\
$\alpha=0.1$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\newpage
\caption{Some simulation results of the suggested hybrid optimizer}
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Ackley ( dim = 2 )
\end{tabular}}
& \multirow{13}{*}{$ [-5,5]^{2} $ }
& $N\_iter=13 \;\; N=18 \;\; P=5 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=-0.1406797$ \\
$X2=-0.1101426$\\
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=-1.6473706210342e-08$ \\
$X2=-3.51273940349251e-08$ \\
\end{tabular}}
& \multirow{13}{*}{$ 1.097385e-07 $ } & \\*
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =50 $\\
$\alpha=0.01$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Beale
\end{tabular}}
& \multirow{13}{*}{$ [-4.5,4.5]^{2} $ }
& $N\_iter=13 \;\; N=20 \;\; P=5 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=2.77737$ \\
$X2=0.63832$\\
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=2.980414$ \\
$X2=0.494759 $ \\
\end{tabular}}
& \multirow{13}{*}{$ 6.54143085562211e-05 $ } & \\*
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =50 $\\
$\alpha=0.01$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Shekel ( dim = 4 )
\end{tabular}}
& \multirow{13}{*}{$ [0,10]^{4} $ }
& $N\_iter=3 \;\; N=20 \;\; P=5 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=3.964629112$ \\
$X2=3.287228306$\\
$X3=4.047133078$ \\
$X4=4.159858556$
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=4.0060974456$ \\
$X2=3.9907111167$ \\
$X3=4.0069265117$ \\
$X4=3.9947221534$
\end{tabular}}
& \multirow{13}{*}{$-10.51979349 $ } & \\*
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =10^3 $\\
$\alpha=0.01$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\newpage
\caption{Some simulation results of the suggested hybrid optimizer}
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Brown ( dim = 10 )
\end{tabular}}
& \multirow{13}{*}{$ [-1,4]^{10} $ }
& $N\_iter=5 \;\; N=40 \;\; P=5 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=0.306416265$ \\
$X2=-0.09236981$\\
$X3=-0.172197343$ \\
$X4=1.225200175$\\
$X5=-0.009701219$ \\
$X6=0.244447773$ \\
$X7=0.101638211$ \\
$X8=-0.34120112$ \\
$X9=-0.599277565$ \\
$X10=-0.31747776$
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=0.0061166589$ \\
$X2=-0.005133607$\\
$X3=-0.00524210132$ \\
$X4= 0.00489577184$\\
$X5=-0.0051129652$ \\
$X6=-0.004793115$ \\
$X7=-0.0056673315$ \\
$X8=-0.00363736016$ \\
$X9=0.00454737504$ \\
$X10=0.0049526585$
\end{tabular}}
& \multirow{13}{*}{$ 0.000447717474 $ } & \\
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =10^3 $\\
$\alpha=0.01$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Rosenbrock ( dim=9 )
\end{tabular}}
& \multirow{13}{*}{$ [-7,7]^{9} $ }
& $N\_iter=13 \;\; N=60 \;\; P=5 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=0.003125323$ \\
$X2=0.376382705$\\
$X3=0.471540395$ \\
$X4=0.723433923$\\
$X5=0.885845371$ \\
$X6=1.092223856$ \\
$X7=1.282321586$ \\
$X8=1.436033536$ \\
$X9=1.860066613$
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=1.00956272$ \\
$X2=0.99225927$\\
$X3=1.00972756$ \\
$X4=1.00866214$\\
$X5=1.01872538$ \\
$X6=1.03898545$ \\
$X7=1.08676107$ \\
$X8=1.17564322$ \\
$X9=1.38090932$
\end{tabular}}
& \multirow{13}{*}{$ 0.197018127 $ } & \\*
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =1000 $\\
$\alpha=0.01$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Alpine ( dim = 10)
\end{tabular}}
& \multirow{13}{*}{$ [0,10]^{10} $ }
& $N\_iter=18 \;\; N=25 \;\; P=10 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=-1.44697297481$ \\
$X2=-0.5245679914 $\\
$X3=-1.06582528$ \\
$X4=-0.38062335$\\
$X5=1.680703443$ \\
$X6=-0.519615052$ \\
$X7=0.90813055351$ \\
$X8=2.1929963152$ \\
$X9=1.3502745645$ \\
$X10=0.4988162137$
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=0.0049088240673$ \\
$X2=0.02934661029$\\
$X3=-0.013279784336$ \\
$X4=-0.013808317649$\\
$X5=-0.02789379609$ \\
$X6=-0.0899579504$ \\
$X7=0.04198229397$ \\
$X8=3.242242583$ \\
$X9=-0.0798609148$ \\
$X10=-0.1128582965$
\end{tabular}}
& \multirow{13}{*}{$ 0.02013423150 $ } & \\
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =100 $\\
$\alpha=0.1$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\newpage
\caption{Some simulation results of the suggested hybrid optimizer}
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Colville ( dim = 4)
\end{tabular}}
& \multirow{13}{*}{$ [-10,10]^{4} $ }
& $N\_iter=13 \;\; N=40 \;\; P=10 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=1.5033964240414$ \\
$X2=1.518321258364$\\
$X3=1.282880998239$ \\
$X4=1.814449944069$\\
$ $
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=1.0495214623$ \\
$X2=1.1309573138$\\
$X3=0.97205952961$ \\
$X4=0.960630041902$
\end{tabular}}
& \multirow{13}{*}{$ 0.199087493 $ } & \\*
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =100 $\\
$\alpha=0.1$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\multirow{13}{*}{\begin{tabular}[c]{@{}c@{}} Schaffer ( dim = 2 ) \end{tabular}}
& \multirow{13}{*}{$ [-100,100]^{2} $ }
& $N\_iter=17 \;\; N=20 \;\; P=5 $
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=0.73493131146$ \\
$X2=0.8971795174$\\
$ $ \\
$ $\\
$ $
\end{tabular} }
& \multirow{13}{*}{\begin{tabular}[c]{@{}l@{}}
$X1=1.4414812e-06$ \\
$X2=-1.871135e-06$\\
$ $
\end{tabular}}
& \multirow{13}{*}{$ 5.551115123e-15 $ } & \\*
\cline{3-3}
&
& \begin{tabular}[t]{@{}l@{}}
$ \text{Iter} =10^{3} $\\
$\alpha=0.1$\\
$\gamma=0.9$
\end{tabular}
& & & & \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
&&&&&& \\
\hline
\end{longtable}
\end{document}
得出以下结果：
我正在寻找适合页边距的表格。
使用的 iapress 类文件可以在这里找到：
https://drive.google.com/file/d/1M3ZEjdt6PSXOuzN9d2Ex4CYdOjtBAGNI/view?usp=sharing
谢谢你的帮助 ！