# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.3 Restricted Tournament Selection (RTS)
Using non-dominated sorting and hypervolume measure-based sorting, the authors of [171], using bi-objective differential evolution (MOBIDE), achieved improved peak detection ratios when testing against crowding DE, speciation DE, speciation DE, and FERPSO, with improved peak success rates. Table 7 gives an exhaustive overview of the surveyed methods. Algorithm 8 provides a pseudo-code of the multiobjectivisation niching paradigm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Year** & **Algorithm** & **Theoretical Ground** & **Type** \\ \hline
[168] & 2023 & NASRM & Adaptive space reconstruction method uses niching to partition the search space, allowing efficient exploration of multiple aerodynamic designs in airfoil optimisation. & Clustering \\ \hline
[169] & 2023 & MSTNDE & Minimum spanning tree niching-based differential evolution uses knowledge-driven updates to efficiently explore multimodal landscapes. & Clustering \\ \hline
[170] & 2023 & RDDE-IT & Reachability distance-based differential evolution with individual transfer employs RDP niching to divide the population, ISM strategy for mutation, and AIM strategy to manage trapped individuals. & Clustering \\ \hline
[146] & 2024 & RLNS-MMOP & Local neighbourhood formation using reinforcement learning (RL)-based clustering. & Clustering \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._

### Hybrid Niching Paradigms

Sequential and parallel niching perceptively have two distinct niching paradigms. While sequential niching is temporal (niching in time), parallel niching is spatial (niching concurrently in the search space). Hybrid families can emerge from these niching techniques which utilise the strengths of both and diminish their weaknesses. We can distinguish three types of algorithms within the hybrid section: **embedded methods, ensemble methods**, and **other hybrid methods**. An embedded method is a hybrid method which combines two or more native niching paradigms or ad hoc niching approaches, typically in sequence one after another, to form a newer method which combines the strength of each approach. An ensemble method tends to concurrently use each native niching paradigm, often in competition or collaboration during the optimisation process, and other hybrid methods are any other novel niching method which is neither an embedded nor ensemble method. Table 8 highlights elements of difference between the three sub-families. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Year** & **Algorithm** & **Theoretical Ground** & **Type** \\ \hline
[21] & 2010 & BOEA & Converts single-objective multimodal optimisation into bi-objective optimisation to identify all optimal solutions as part of a weak Pareto-optimal set. & MO \\ \hline
[171] & 2012 & MOBIDE & Bi-objective differential evolution using non-domination sorting and hypervolume sorting. & MO \\ \hline
[7] & 2016 & AMCA & Adaptive multimodal continuous ant colony optimisation enhances pheromone updating and selection strategies to locate multiple global optima efficiently. & MO \\ \hline
[172] & 2018 & TRIDE & Tri-objective differential evolution approach balances three objectives for effective multimodal optimisation. It leverages niching strategies to maintain diverse solutions across multiple peaks. & MO \\ \hline
[173] & 2023 & NMPSO & Integrates sensor ontologies with niching multi-objective particle swarm optimisation to enhance search efficiency and solution diversity in complex environments. & MO \\ \hline
[174] & 2023 & NSGA-III & Eliminates non-dominated sorting in NSGA-III, using penalty boundary intersection niching to emphasise the best non-dominated solutions, enhancing performance for many-objective problems. & MO \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of niching algorithms: multiobjectivisation (MO). The authors of [175] proposed an embedded clustering-based differential evolution (CbDE-wCA) which incorporates crowding to encourage exploration of unexplored areas and clearing to eliminate redundant solutions.