# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.3 Restricted Tournament Selection (RTS)
Figure 4: Research contribution per niching paradigm. Figure 3: Research contribution per niching family. Sequential niching algorithms have received dramatically little interest in the literature, accounting for only 2% of theoretical research works. The major challenge of the technique lies in its slow detection of basins of convergence by committing all population agents to the discovery and exploitation of promising regions one at a time. A computationally efficient adaptive radius estimation remains a challenge in this family. The hill-valley concavity test has been a viable proposition to repel search agents when approaching a peak. However, the technique can be computationally intensive, especially for problem types with a high cost in function evaluations. Nevertheless, the technique still presents some good features. Unlike parallel niching, the sequential niching peak detection capacity is not limited by the population size. Because the algorithm restarts while banning previously discovered basins of convergence, it can indefinitely suggest new peaks over time. Moreover, sequential niching exhibits a good exploitation capacity, owing to its use of the full population size. Hybrid niching algorithms have received substantial interest (24.9%) in the literature. These techniques generally combine the strengths of different parallel niching algorithms either used in sequence (embedded) or in competition (ensemble) dynamically. Embedded techniques have been the most commonly used subparadigm in the family (16.6%), followed by ensemble methods (5.4%) and other hybrid techniques. No mention of restricted tournament selection [56; 180] as a standalone niching mechanism has been recorded in our database. In as much as most hybrid methods make use of parallel niching algorithms, these algorithms can still be considered parallel niching methods. Fewer techniques, such as those in [138; 179], combine parallel niching subfamilies with sequential niching to benefit from its scalability. In terms of niching paradigm performance, fewer studies in recent years, as per our survey, have compared niching paradigms across all major families. The authors of [219] compared eight niching algorithms from six representative niching paradigms: a clustering algorithm, deterministic and probabilistic crowding, restricted tournament selection, sharing, a speciation genetic algorithm, and two clearing methods on evolutionary algorithms. In all, the niching paradigms performed well in single-dimension problems with low numbers of peaks. As the number of peaks increased, fitness sharing, deterministic crowding, and restricted tournament selection maintained reliable peak detection performance at a dimensionality of 10 with a growing number of peaks (20, 30, 40, and 50). Original clearing, speciation, and clustering had fair peak detection accuracy with an increasing number of peaks. Above 10 dimensions, only the modified clearing strategy yielded stable performance but with a tremendous computational cost. In terms of computational cost, clustering exhibited the highest computational cost, while deterministic crowding exhibited

Figure 5: Publication type count per 3 year interval. the lowest computational cost. It is worth noting, however, that dimensional scalability was performed on a single scalable function type. The study was conducted on three function types only. The author of [220], in his study on the scalability of niching paradigms, compared two representation speciation-based methods (NichePSO and adaptive NichePSO), four variant clustering-based evolutionary algorithms, and two simple multi-restart sequential methods in terms of peak detection count [221] on three test functions at dimension counts of 10 and 30. The clustering-based paradigm ranked first in peak detection counts in its PSO variant, followed by the sequential methods and then the speciation approaches.