# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.3 Restricted Tournament Selection (RTS)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Year** & **Algorithm** & **Theoretical Ground** & **Type** \\ \hline
[153] & 2001 & C-UEGO &
\begin{tabular}{c} Combines clustering with niching in a global \\ optimiser to improve the reliability and performance of evolutionary algorithms. \\ \end{tabular} & Clustering \\ \hline
[154] & 2003 & CEPC &
\begin{tabular}{c} Combined evolutionary programming and \\ cluster analysis algorithm integrates evolutionary programming with cluster analysis to maintain multiple solutions in global optimisation. \\ \end{tabular} & Clustering \\ \hline
[140] & 2005 & DCBN-GA &
\begin{tabular}{c} Multi-subpopulation genetic algorithm using \\ density clustering. \\ \end{tabular} & Clustering \\ \hline
[141] & 2006 & MSDC-PSO &
\begin{tabular}{c} Multi-swarm PSO using density clustering for \\ niche formation. \\ \end{tabular} & Clustering \\ \hline
[155] & 2007 & NBPSO &
\begin{tabular}{c} Clustering with a weighted sum validity function using a niching PSO algorithm, which \\ integrates niching PSO with cluster validity functions to automatically determine the number of clusters and partition the data set. \\ \end{tabular} & Clustering \\ \hline
[139] & 2008 & KPSO &
\begin{tabular}{c} Cognitive model search, optimal k-means \\ clustering, and local PSO exploration. \\ \end{tabular} & Clustering \\ \hline
[156] & 2011 & NCBPGL &
\begin{tabular}{c} Basin identification on fixed-property generated landscapes evaluates clustering methods like nearest-better clustering and detect- \\ multimodal to manage multimodal optimisation landscapes. \\ \end{tabular} & Clustering \\ \hline
[157] & 2013 & VMOP &
\begin{tabular}{c} Variable mesh size optimisation dynamically \\ adjusts the granularity of the search space to \\ identify multiple optima, ensuring a balance \\ between exploration and exploitation. \\ \end{tabular} & Clustering \\ \hline
[158] & 2014 & ASMPSO &
\begin{tabular}{c} Accurate sub-swarm particle swarm optimisation \\ station dynamically partitions the swarm into \\ sub-swarms using fitness and distance criteria, enhancing exploration and exploitation in \\ service composition. \\ \end{tabular} & Clustering \\ \hline
[107] & 2014 & TN-CMA &
\begin{tabular}{c} Topological niching uses covariance matrix adaptation to explore multimodal landscapes effectively. \\ \end{tabular} & Clustering \\ \hline
[107] & 2014 & TN-CMA &
\begin{tabular}{c} Topological niching uses covariance matrix adaptation to explore multimodal landscapes effectively. \\ \end{tabular} & Clustering \\ \hline
[107] & 2014 & TN-CMA &
\begin{tabular}{c} Clustering-based model-building evolutionary algorithm integrates clustering techniques \\ with model building EA to maintain multiple solutions and improve convergence in \\ optimisation problems with binary and real-valued variables. \\ \end{tabular} & Clustering \\ \hline
[160] & 2017 & CNMMA &
\begin{tabular}{c} Combines cooperative co-evolution and \\ niching-based multi-modal optimisation with \\ adaptive clustering to handle large-scale optimisation problems. \\ \end{tabular} & Clustering \\ \hline
[161,162] & 2018 & HILVALLEA &
\begin{tabular}{c} Two-stage multi-population evolutionary algorithm using radius-free hill-valley clustering and exploitation via core search algorithms. \\ \end{tabular} & Clustering \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of niching algorithms: clustering.