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             \author[1]{Satish  Gajawada}

             \affil[1]{  Indian Institute of Technology Roorkee (IIT Roorkee)}

\renewcommand\Authands{ and }

\date{\small \em Received: 11 June 2021 Accepted: 4 July 2021 Published: 15 July 2021}

\maketitle


\begin{abstract}
        


"Artificial Excellence" is a new field which is invented in this article. Artificial Excellence is a new field which belongs to Artificial Human Optimization field. Artificial Human Optimization is a sub-field of Evolutionary Computing. Evolutionary Computing is a sub-field of Computational Intelligence. Computational Intelligence is an area of Artificial Intelligence. Hence after the publication of this article, "Artificial Excellence (AE)" will become popular as a new branch of Artificial Intelligence (AI). A new algorithm titled "Artificial Satish Gajawada and Durga Toshniwal Algorithm (ASGDTA)" is designed in this work. The definition of AE is given in this article followed by many opportunities in the new AE field. The Literature Review of Artificial Excellence field is shown after showing the definition of Artificial Intelligence. The new ASGDTA Algorithm is explained followed by Results and Conclusions.

\end{abstract}


\keywords{artificial excellence, artificial human optimization, evolutionary computing, computational}

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\begin{textblock*}{10cm}(1.05cm,3cm)
{{\textit{CrossRef DOI of original article:}} \underline{10.34257/GJCSTDVOL21IS1PG35}}
\end{textblock*}\let\tabcellsep& 	 	 		 
\section[{I. Definition of Artificial Excellence Field}]{I. Definition of Artificial Excellence Field}\par
he basic entities in Particle Swarm Optimization, Artificial Soul Optimization and Artificial God Optimization are Artificial Birds, Artificial Souls and Artificial Gods respectively. Similarly, the basic entities in Artificial Human Optimization field algorithms are Artificial Humans. "Artificial Excellence (AE)" is a subfield of Artificial Human Optimization field. Hence the basic entities in AE field are also Artificial Humans only. But there is a difference. Artificial Human Optimization is about imitating Humans in general. There is no concept of imitating particular Human beings. AE is based on imitating particular Human beings. The basic entities in AE field algorithms are particular Human beings. Every Human is different. Hence imitating Humans in general (Artificial Human Optimization) and imitating particular Human beings (Artificial Excellence) will yield different results. If we take particular Human being (Say Ankush Mittal) then we can design algorithm "Artificial Ankush Mittal Algorithm" where the search space consists of Artificial Ankush Mittals and this Ankush Mittal Algorithm belongs to Artificial Excellence (AE) field. Section 5 of this article designs and describes world's first AE field algorithm. This algorithm is named as "Artificial Satish Gajawada and Durga Toshniwal Algorithm (ASGDTA Algorithm)". The basic entities in ASGDTA Algorithm are Artificial Satish Gajawadas and Artificial Durga Toshniwals. Just like Satish Gajawada and Durga Toshniwal move in real world and solves problems. Similarly, Artificial Satish Gajawadas and Artificial Durga Toshniwals move in search space and solves optimization problems.   
\section[{II.}]{II.} 
\section[{Opportunities in the New Artificial Excellence Field}]{Opportunities in the New Artificial Excellence Field} 
\section[{Artificial Intelligence}]{Artificial Intelligence}\par
The following is the definition of Artificial Intelligence according to Investopedia shown in double quotes as it is:\par
"Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving" \hyperref[b13]{(Investopedia, 2020)}.\par
IV.   
\section[{Literature Review}]{Literature Review} 
\section[{V. The Artificial Satish Gajawada and Durga Toshniwal Algorithm}]{V. The Artificial Satish Gajawada and Durga Toshniwal Algorithm}\par
This section explains Artificial Satish Gajawada and Durga Toshniwal Algorithm (ASGDTA). Figure \hyperref[fig_2]{1} shows ASGDTA. All Artificial Satish Gajawadas and Artificial Durga Toshniwals are initialized in line number   
\section[{Results}]{Results}\par
The benchmark functions are taken from article (Gajawada, S., and Hassan Mustafa, 2019a). The ASGDTA and PSO are applied on 5 benchmark functions shown in figure \hyperref[fig_3]{2} to figure 6.        
\section[{Conclusions}]{Conclusions}\par
A new field titled "Artificial Excellence (AE)" is invented and defined in this work. Researchers in Artificial Intelligence field can follow the path shown in this paper and create algorithms like "Artificial Narendra Modi Algorithm", "Artificial Abdul Kalam Algorithm", "Artificial Mahatma Gandhi Algorithm", "Artificial Mother Teresa Algorithm" and "Artificial Raju Algorithm" by imitating particular humans like Narendra Modi, Abdul Kalam, Mahatma Gandhi, Mother Teresa and Raju respectively. If there are 100 crores population then we can imitate all these population and create more than 100 crores algorithms. If there are 20 people in a project solving real world problems. Then we can create a AE field algorithm imitating these particular 20 people. If we have particular Humans Raju and Rani in real world and AE field algorithm size is 20 then there will be multiple particular Artificial Humans in search space like 10 Artificial Rajus and 10 Artificial Ranis. Hence from this article it is clear that there are INFINITE articles and INFINITE opportunities possible in the new AE field invented in this work.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-6.png}
\caption{\label{fig_5}Figure 3 :}\end{figure}
    \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.13734756097560977\textwidth}P{0.7126524390243902\textwidth}}
5)\tabcellsep for each particle i do\\
6)\tabcellsep if ( generate\textunderscore random\textunderscore number (0,1) < DurgaToshniwalProbability ) then // Durga Toshniwal\\
7)\tabcellsep Update Velocity of Artificial Durga Toshniwal\\
8)\tabcellsep Update Position of Artificial Durga Toshniwal\\
9)\tabcellsep else // Satish Gajawada\\
10)\tabcellsep if ( random(0,1) < HelpOfDurgaToshniwalProbability) then // Satish Gajawada with Help\\
11)\tabcellsep Update Velocity of Artificial Satish Gajawada\\
12)\tabcellsep Update Position of Artificial Satish Gajawada\\
13)\tabcellsep else // Satish Gajawada without help does nothing\\
14)\tabcellsep \\
15)\tabcellsep end if\\
16)\tabcellsep end if\\
17)\tabcellsep end for\\
18)\tabcellsep generations (iterations) = generations (iterations) + 1\\
\multicolumn{2}{l}{19) while ( termination\textunderscore condition not reached is true)}\end{longtable} \par
 
\caption{\label{tab_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2021\\
( ) D\end{longtable} \par
 
\caption{\label{tab_3}Table 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.5237373737373737\textwidth}P{0.27045454545454545\textwidth}P{0.055808080808080814\textwidth}}
\tabcellsep Artificial Satish\tabcellsep \\
Benchmark Function / Algorithm\tabcellsep Gajawada and Durga Toshniwal Algorithm\tabcellsep PSO Algorithm\\
\tabcellsep (ASGDTA)\tabcellsep \\
Ackley Function\tabcellsep \tabcellsep \\
Beale Function\tabcellsep \tabcellsep \\
Bohachevsky Function\tabcellsep \tabcellsep \\
Booth Function\tabcellsep \tabcellsep \\
Three-Hump Camel Function\tabcellsep \tabcellsep \\
VII.\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 1 :}\end{figure}
 		 		\backmatter   			 
\subsection[{Acknowledgments}]{Acknowledgments}\par
Thanks to everyone (and everything) who directly or indirectly helped me to reach the stage where I am now today. Thanks to EXCELLENT Editorial Team and Reviewers for accepting my new invention titled "Artificial Excellence field". 			  			  				\begin{bibitemlist}{1}
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\bibitem[Kumar and Durga Toshniwal ()]{b14}\label{b14} 	 		‘A data mining approach to characterize road accident locations’.  		 			S Kumar 		,  		 			Durga Toshniwal 		.  		 \xref{http://dx.doi.org/10.1007/s40534-016-0095-5}{10.1007/s40534-016-0095-5}.  		 \url{https://doi.org/10.1007/s40534-016-0095-5}  	 	 		\textit{J. Mod. Transport}  		2016. 24 p. .  	 
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\bibitem[Al-Shaikhi et al. ()]{b1}\label{b1} 	 		‘A Hybrid Particle Swarm Optimization Technique for Adaptive Equalization’.  		 			A A Al-Shaikhi 		,  		 			A H Khan 		,  		 			A T Al-Awami 		.  		 \xref{http://dx.doi.org/10.1007/s13369-018-3387-8}{10.1007/s13369-018-3387-8}.  		 \url{https://doi.org/10.1007/s13369-018-3387-8}  	 	 		\textit{Arab J Sci Eng}  		2019. 44 p. .  	 
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\end{bibitemlist}
 			 		 	 
\end{document}
