# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.3 Restricted Tournament Selection (RTS)
Fuzzy-c mea766512 cvnzns was used for niche formation, and the algorithm was proposed for optimisation in a dynamic environment where the locations of peaks change over time. The authors of [176] adapted chaos optimisation to niching using a constrictable multi-search scopes paradigm, where chaos elements are allowed to navigate. This technique is integrated with crowding, which permits the insertion of coexisting and competing chaos elements, and clearing, which discards chaos elements which are approaching the same peak based on a clearing radius. The authors of [177; 178; 179] proposed an embedded semi-concurrent sequential niching algorithm which discovers multiple optima sequentially and exploits them concurrently using parallel computing, thus saving computation search time. The proposed algorithm is beneficial for efficient sequential niching in practical optimisation, but it is still faced with the limitation of efficient radius estimation. The authors of [138] proposed a clustering-based niching algorithm with efficient resource usage (MNC-EA). Initially, search individuals are clustered into subpopulations and exploited independently, whereby niches which represent the same modality have some search individuals reallocated elsewhere, and underpopulated niches are assisted with search agents from other niches to accelerate convergence. When all niches converge to their peaks, the parallel search algorithms are restarted. The proposed algorithm is oriented towards efficiently using resources to discover as many niches as possible. The restarting nature of these novel algorithms will qualify them as parallel and sequential niching paradigms. The authors of [180] proposed an ensemble multi-strategy multimodal genetic algorithm which combines clearing, sharing, clustering, restricted tournament selection, and crowding to exploit the search space with a co-evolving subpopulation. Initially, from the main population, an equal number of search agents is allocated to each niching scheme. The niching scheme which obtains better performance is assigned more resources (i.e., search individuals), while substandard niching schemes are maintained with a sub-minimum progressively. The authors of [181] proposed an adaptive ensemble niching differential evolution which selects a different niching scheme at each generation based on a quality measure which assesses fitness improvement and diversity. Three niching algorithms were used--deterministic crowding [42] (DC), restricted tournament selection [56] (RTS), and clearing [182] (Clr)--along with the original differential evolution. Initially, all niching schemes are given a chance to run on the population and ranked based on fitness improvement and diversity. They are then selected probabilistically based on quality score to dictate searching in the four coexisting populations. The proposed ensemble method was, however, parameter-intensive, using several configuration parameters. The authors of [22] proposed neighbourhood-based differential evolution (N.DE), which limits the mutation process of vectors within an m-member neighbourhood of similar vectors to form niches. This approach was combined with crowding, sharing, and speciation-based differential evolution. The authors of [183] proposed a multilevel sam \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Category** & **Definition** & **Temporal or Spatial** \\ \hline Embedded & Combines two or more niching paradigms or ad hoc techniques in sequence to leverage different strengths. & Temporal (sequential switching) \\ \hline Ensemble & Use multiple niching paradigms concurrently, allowing competition or collaboration. & Spatial (multiple paradigms runs in parallel in the same or different populations) \\ \hline Other Hybrid & Combine or integrate elements of both parallel and sequential paradigms in a between methods & Both temporal and spatial, depending on the strategy or dynamic switching between methods \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Taxonomy of Hybrid methods.