# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.3 Restricted Tournament Selection (RTS)
The authors of [222] comprehensively discussed performance measures in niching algorithms and conducted a computational experiment on multimodal test functions to assess the peak detection accuracy between five speciation-based niching techniques, a multiobjective optimisation evolutionary method, and one established fitness-to-Euclidean ratio niching technique [17]. The study concluded that there was no statistically significant difference in peak detection accuracy, which speaks to their exploitation abilities. The study was, however, conducted with most problems at a dimensionality of two. These studies explored niching paradigms which perform satisfactorily well in lower dimensional spaces, and their performance in higher dimensional spaces has yet to be put to the test conclusively. More rigorous investigation should be performed on the scalability of niching algorithms in terms of peak detection ratio, niche preservation, and computational cost and across various problem types. ## 4 Challenges and Future Research Directions

Towards Space-Scale Niching Methods: Performance in Large Dimensional Spaces and Growing Space Deformation

Comparative studies [12; 219; 222] suggest that in a lower dimensional space and with fewer peaks, all niching paradigms perform fairly satisfactorily. However, the actual enigma remains in their (stable) performance in higher dimensional spaces and with a growing increase in peaks. Niching algorithms should also be assessed by their scalability (the peak detection ratio, success rate, convergence speed, and computational cost) as the problem dimension and the number of peaks increase. The non-restart nature of parallel niching methods puts a theoretical upper bound on their capacity to obtain \(n\) number of optima, albeit with a fast peak detection rate [1]. The number of optima is not known a priori, and thus a parallel niching algorithm can only discover up to \(p\) peaks, with \(p\) being the population size. Multi-restart parallel niching algorithms should be encouraged, rendering the technique embedded and parallel-sequential [138]. In addition, niching algorithms should also be assessed with benchmarking functions of diverse shapes with scalable dimensions and numbers of peaks. Established niching algorithms such as NichePSO [5] have only been tested in lower dimensional spaces. The constraints mentioned here predict that their performance in higher dimensional spaces is not guaranteed [3]. ### Towards Time-Scalable Niching Methods: Rewarding an Increase in Computational Resources

One major challenge of unimodal classical metaheuristics such as particle swarm optimisation, genetic algorithm, differential evolution, and similar metaheuristics leading to niching is a drastic decline in diversity as the iteration search continues. Search agents, due to social interaction and inbreeding, become homogenous, abandoning exploration for constriction into one basin of convergence. In such a set-up, an increase in computation time does not scale with an increase in optimum solution generation. The authors of [220] suggested that global optimisation methods, and niching methods in particular, should scale with time. They should return more local optima the longer they run, thus rendering extensive computation resources and rewarding optimisation. Thus, time scalability should be considered a major criterion to assess modern optimisation methods (i.e., how well they scale in peak generation when more function evaluations are provided) [13]. The current study also encourages the design of parallelisable niching methods which can benefit from modern computing architectures. ### Towards Efficient and Reliable Parameterless niching methods

Setting the niching radius is a common issue in a category of niching paradigms (i.e., sequential niching, fitness sharing, and clearing, a category of clustering and speciation methods). Implicit niching in multiobjectivisation, deterministic crowding, and FER methods circumvent this problem [3; 6; 17], but with no guarantee of preservation of peaks.