# 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 \multirow{4}{*}{[61]} & \multirow{4}{*}{2010} & \multirow{4}{*}{CBC} & Context-based clearing procedure within genetic algorithms uses heterogeneity in subpopulations to dynamically adjust clearing radius, enhancing efficiency in finding multiple optima. & Clearing \\ \hline \multirow{4}{*}{[62]} & \multirow{4}{*}{2013} & \multirow{4}{*}{DANADE} & Dynamic archive niching differential evolution maintains multiple niches using an archive-based niching mechanism and differential evolution, enhancing the exploration and exploitation capabilities. & Clearing \\ \hline \multirow{4}{*}{[63]} & \multirow{4}{*}{2013} & \multirow{4}{*}{VMO-NC} & Variable mesh optimisation with niche clearing strategy dynamically adjusts the search space and employs clearing techniques to maintain multiple solutions in multi-modal optimisation. & Clearing \\ \hline \multirow{4}{*}{[60]} & \multirow{4}{*}{2014} & \multirow{4}{*}{(M)TLDE} & Differential evolution with self-adaptive parameter with niching based on clearing. & Clearing \\ \hline \multirow{4}{*}{[64]} & \multirow{4}{*}{2017} & \multirow{4}{*}{CMA-ES} & Covariance matrix self-adaptation evolution strategy with repelling subpopulations to maintain diversity and prevent premature convergence. & Clearing \\ \hline \multirow{4}{*}{[65]} & \multirow{4}{*}{2017} & \multirow{4}{*}{DT-CLEARING} & Enhanced clearing-based niching method using Delaunay triangulation, which divides the search space into simplices to improve the identification and maintenance of multiple niches. & Clearing \\ \hline \multirow{4}{*}{[66]} & \multirow{4}{*}{2017} & \multirow{4}{*}{NGA} & Niching genetic algorithm enhances SiC crystal growth by maintaining high population diversity and preventing premature convergence through the use of clearing procedures, ensuring effective exploration of the search space and addressing both single-objective and multi-objective optimisation problems. & Clearing \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of niching algorithms: clearing. The authors of [5] proposed NichePSO, one of the earliest speciation niching methods. Initially, all particles are independent following a cognitive search topology. When a particle's cognitive experiences stall, it forms a niche and can absorb neighbouring particles around it using a statistically estimated adaptive radius. Each formed niche followed a variant of the gBest topology to exploit its region. Formed niches have the potential to merge by configuration and when merging criteria are met. The authors of [67] proposed a type of speciation-based dynamic niching PSO similar to NichePSO [5], where particles are identified as species leaders (i.e., self masters) if they have the highest fitness in their neighbourhoods, forming a local gBest topology with their subordinate particles. Any two close subswarms are merged, and independent particles navigate using a cognitive model. The authors of [20] proposed a speciation-based PSO algorithm (SPSO) which identifies at each iteration a radius-based local neighbourhood for each particle to form independent niches or species which only interact locally and adaptively converge into multi-swarms. Each species uses its local gBest as its social experience component. This technique adapts the original PSO algorithm [68] to multimodal optimisation. The authors of [69] proposed a co-evolutionary multi-agent system with sexual selection to create multiple species in an evolutionary algorithm. Two types of evolutionary agents coexist--males and females--and they can reproduce only if they are located around the same optima. The hill-valley function concavity test [28] is used to determine whether two individuals share the same peak or not. Intermediate mutation and recombination with self-adaption are used for each gender type, allowing them to explore the solution space.