# 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} \hline \hline
**Authors** & **Year** & **Algorithm** & **Theoretical Ground** & **Type** \\ \hline
[215] & 2002 & GGA & Gradient-guided niching enhances local search capabilities by guiding solutions towards gradient directions. \\ \hline
[216] & 2008 & PNIGA & Parallel genetic algorithm with niching technique enhances nuclear reactor core design optimisation by maintaining diversity and improving computational performance. \\ \hline
[217] & 2014 & VCGA & Variant-constrained genetic algorithm solves conditional nonlinear optimal perturbations using niching strategies to handle multiple constraints and maintain diversity. \\ \hline
[218] & 2015 & CDQ & Composite differential evolution uses queuing selection to maintain multiple solutions. \\ \hline
[23] & 2019 & QIGA & Quantum-inspired genetic algorithm integrates quantum computing principles with genetic algorithms for enhancing multimodal optimisation. It leverages quantum parallelism and superposition to explore multiple optima effectively. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of niching algorithms: other hybrid methods. \begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline
**Category** & **Niche Formation** & **Parameter** & **Representative Works** \\ \hline Sequential & Multiple restarts of unimodal metaheuristic searching scheme with region banning. & Niche radius or hill-valley concavity test & [1,179] \\ \hline Speciation & Aggregation around domination particles using m-neighbourhood or distance measures. & Neighbourhood radius or m members & [5] \\ \hline Crowding & Replacement of search individuals with similar ones to maintain local exploration. on offspring can replace a parent only when it is typically fitter and near the parent, thus maintaining diversity. & Parameterless (i.e., deterministic crowding) & [41] \\ \hline Clustering & Niche formation by clustering analysis using nearness proximity estimation. & Based on k-cluster estimation methods & [10,139] \\ \hline Clearing & Identify dominant search individuals and penalise all neighbouring individuals within a clearing radius to ensure they explore elsewhere. & Clearing radius and optional niche capacity & [19] \\ \hline Fitness Sharing & Downgraded fitness of overcrowed individuals or encourage attraction towards fitter, closer neighbourhood. & Niche radius and scaling factor or parameters dispersion-fitness ranking scores & [6,17] \\ \hline Multicobjectivitation & Design a multi-objective cost function which rewards the fittest and most spread out search agents. Use solution quality ranking map to guide agents mutation and crossover or collaboration. & Parameterless & [171] \\ \hline Embedded & Combines two or more niche paradigms or ad hoc techniques in sequence to leverage different strengths. & Inherited from underlying paradigms & [175] \\ \hline Ensemble & Use multiple niching paradigms concurrently, allowing competition or collaboration. & Inherited from underlying paradigms & [180,181] \\ \hline Other Hybrid & Combines or integrates elements of both parallel and sequential paradigms in a unique way. & Inherited from underlying paradigms & [23,218] \\ \hline \end{tabular}
\end{table} 
Table 13: Theoretical overview of niching paradigms. ## 3 Features, Trends, and Performances in Niching Paradigms

Parallel niching algorithms have undoubtedly received the most attention in the literature (see Figure 3), with 73% of theoretical works discussing them. The benefit of parallel niching algorithms lies in their ability to locate multiple optima simultaneously, unlike their sequential counterpart. In this family of methods (see Figure 4), speciation (37.4%), followed by clustering (14.8%), received the most attention, followed by crowding (8.4%), fitness sharing (6.4%), clearing (3.4%) and multiobjectivisation (3%). Figure 5 shows that in recent years, speciation, embedded hybrid methods, and clustering-based niching methods have continued to receive the most attention in the research literature.