# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.2 Crowding  
While fitness sharing indirectly influences the formation of niches, crowding operates directly on the population by inserting new individuals which replace similar ones. The goal of crowding is to prevent overcrowded regions by discouraging dominance by a basin of convergence or chromosomal template. One state-of-the-art crowding approach is deterministic crowding (DC) [41], which restricts parent-offspring competition within local neighbourhoods to maintain local area exploitation over convergence to a dominant region. An offspring can replace a parent only when it is typically fitter and near the parent, thus encouraging local exploration and exploitation. The authors of [42] proposed a crowding technique which can be used as a parallel niching algorithm for multimodal genetic optimisation. The authors of [43] adapted crowding to differential evolution (CDE). The authors of [44] proposed a hybrid PSO with a recombination replacement crowding strategy (HN-PSO\({}_{\text{PGA}}\)) which uses the local best PSO together with a crowding replacement \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Year** & **Algorithm** & **Theoretical Ground** & **Type** \\ \hline
[6] & 2014 & FER-DE &
\begin{tabular}{c} Parameter free multi-peak differential evolution using the fitness-to-Euclidean ratio to select differential vectors. \\ \end{tabular} & Fitness sharing \\ \hline
[8] & 2019 & DNRBA &
\begin{tabular}{c} Extends the bat algorithm with a dynamic niche radius, adjusting the niche size based on the fitness landscape to prevent convergence on the same optima and maintain multiple peaks. \\ \end{tabular} & Fitness sharing \\ \hline
[35] & 2019 & SNRBF &
\begin{tabular}{c} Sharing niching technique trains RBF neural network to achieve global optimal nodes, combined with an improved NSGA-II using immune operators. \\ \end{tabular} & Fitness sharing \\ \hline
[36] & 2019 & N-PSO &
\begin{tabular}{c} Particle swarm optimisation for multimodal buckling maximisation in composite laminates, incorporating niching to maintain diversity and avoid premature convergence. \\ \end{tabular} & Fitness sharing \\ \hline
[37] & 2020 & FDLSEDE &
\begin{tabular}{c} Fitness- and distance-based local search with adaptive differential evolution employs fitness sharing and distance metrics to enhance local search and maintain multiple solutions in multimodal optimisation. \\ \end{tabular} & Fitness sharing \\ \hline
[9] & 2022 & NC-CHIMP &
\begin{tabular}{c} Robust niching in chip optimisation algorithm identifies multiple peaks by penalising overcrowded areas and rewarding diverse solutions. \\ \end{tabular} & Fitness sharing \\ \hline
[38] & 2022 & NCO &
\begin{tabular}{c} Niching chimp optimisation algorithm incorporates constraint handling to optimise multiple objectives in engineering problems. \\ \end{tabular} & Fitness sharing \\ \hline
[39] & 2023 & EABC-DPS &
\begin{tabular}{c} Elitist artificial bee colony algorithm with dynamic population size adapts to the search environment, using fitness sharing to maintain diversity and find multiple optima. \\ \end{tabular} & Fitness sharing \\ \hline
[40] & 2023 & FMDE &
\begin{tabular}{c} Fitness-based niching division with adaptive mutation in differential evolution balances exploration and exploitation to prevent premature convergence and maintain diversity in global optimisation. \\ \end{tabular} & Fitness sharing \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._strategy to encourage exploration of a search space towards searching for multiple peaks. Table 3 gives an exhaustive overview of the surveyed methods. Algorithm 3 provides a pseudo-code of deterministic crowding, a template commonly used in the crowding niching paradigm.