# Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review
## 3 Major Initialisation Methods
### Hybrid with Other Metaheuristic Algorithms  
Most researchers used another metaheuristic algorithm to find an optimal solution for the initial position of the population in this approach. Metaheuristic algorithms with a high convergence rate in a specific problem domain are often used to find an initial solution. These solutions are then fed into the other metaheuristic algorithms as the initial conditions. A hybridization of ABC and TS was proposed in the work of [39], where the bee population was initialised using the randomized breadth-first search. The performance of their hybrid was better than the algorithms they compared it with; however, it suffers from the time complexity problem of BFS. The authors [40] initialised the monarch butterfly algorithm by equally partitioning the search space and in the F and T random distribution to mutate the divided population. The results showed significant improvements. The Krill in the work of [41] were initialised using the pairwise linear optimisation, which uses fuzzy rules to create clusters that are used as the initial point for the KH. However, the results showed that this improvement would only suit systems based on fuzzy approximators. The CRO was improved using the VNS algorithm with a new processor selection model for the initialisation. The results are promising; however, parameter sensitivity still needs to be resolved [42]. The cuckoo population was initialised using quasi-opposition-based learning (QOBL) [43]. Reaching the optimal search is enhanced by considering a guess and its quasi-opposite guess. The initialisation schemes of BA are improved using a quasirandom sequence with low discrepancy called Torus [25]. Their results were good; however, the results were not evaluated for higher-dimensional problems. Four (4) different dispatching rules (DR)-based initialisation strategies were used by [44], with varying advantages and disadvantages. The best result was obtained when all of the strategies were used together, which means that the diversity of the population contributed less to the algorithm's overall performance. In [45], a scheme inspired by SAM was developed, and it is a simplified heuristic model that begins the swarm search with an initial set of high-quality solutions. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Year** & **Initialisation Scheme** & **Optimisation Problem** \\ \hline
[32] & 2017 & Stochastic fractal search technique & Dynamic state estimation (DSE) problem at the filtering stage \\ \hline
[13] & 2020 & Variants of Beta distribution, uniform distribution, normal distribution, exponential distribution, Rayleigh distribution, Weibull distribution, and Latin hypercube Sampling & Benchmark function \\ \hline
[31] & 2017 & Latin hypercube sampling & Structural components under fatigue \\ \hline
[34] & 2011 & Stochastic demands & Vehicle routing problem \\ \hline
[35] & 2012 & Smart sampling & Benchmark functions \\ \hline
[36] & 2018 & Log logistic & Training of artificial neural network \\ \hline
[37] & 2016 & Neural fuzzy inference & Flood susceptibility modeling \\ \hline
[38] & 2019 & Adaptive neuro-fuzzy inference & Groundwater potential mapping \\ \hline
[33] & 2016 & Stochastic random a sampling of simpler fat-tailed distributions enhanced with scaled-chaotic sequences & Complex wellbore trajectories \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of PDF category. ABC was used to find the optimal cluster centre of the FCM [46]. An improved ABC was also proposed to solve the vehicle routing problem (VRP) [47]. Among other improvements, the bees were initialised using push forward insertion. An improved DE, named the enhanced differential evolution algorithm (EDE), used the opposition-based learning for the initialisation, along with other improvements, in order to enhance the performance of DE [48]. The optimised stream clustering algorithm (OpStream) used an optimal solution of a metaheuristic algorithm to initialise the first set of the cluster [49].