# Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review
## 3 Major Initialisation Methods
### Ad Hoc Knowledge of the Domain  
In the ad hoc knowledge of the domain approach, the authors used background knowledge of the domain to design the initialisation scheme of an algorithm. The nature of the problem is what influences the diversity and spread of the initial population. The scheme proposed in the work of [86] used this scheme to generate initial solutions, serving as the initial point for the metaheuristic method. Their results were better and, in some cases, competitive; however, we believe that this method is excessively problem-dependent as such a generalisation is impossible. In the same vein, ref. [87] proposed the initialisation of the bats method, based on ad hoc knowledge of the PV domain. Precisely, they used the peaks with similar duty ratios that occur at the power versus duty ratio of the boost converter curve. [PERSON] et al. [88] used the objective function to minimise the wear and tear of the actuators when initialising the population.  
The clans in EHO [89] were initialised by considering the acoustic decay model that is used to obtain the distance between the sensor and the noise source. Depending on the noise level, the intersection of the source coordinates will be at the radii, which is less likely to be single. The clans are initialised, while being based at the centre of the intersection. The technique suffers from being problem-dependent and requires much adaptation before being used in other domains. Finally, a scheme to help PSO avoid reinitialisation to capture the global peaks, when PSO changes its position and value in the P-V curve, was developed  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Reference** & **Year** & **Initialisation Scheme** & **Optimisation Problem** \\ \hline
[71] & 2014 & Chaotic Sequence & Benchmark function \\ \hline
[70] & 2017 & Logistic Chaotic Function & Enhancement of satellite images \\ \hline
[72] & 2018 & Chaotic Sequence & Benchmark test functions \\ \hline
[73] & 2019 & Chaotic Sequence & Synthesis and structure optimization of antenna arrays \\ \hline
[74] & 2020 & Chaotic Theory & Analog circuits intelligent fault diagnosis \\ \hline
[75] & 2017 & Chaotic Theory & Medical diagnoses \\ \hline
[76] & 2013 & Chaotic Theory & Benchmark test function \\ \hline
[77] & 2011 & Chaotic Theory & Benchmark test function \\ \hline
[78] & 2014 & Chaotic Theory & Benchmark test function \\ \hline
[79] & 2014 & Chaotic Theory & Benchmark test function \\ \hline
[80] & 2017 & Chaotic Theory & Benchmark test function \\ \hline
[82] & 2018 & Chaotic Theory & Global optimization and feature selection \\ \hline
[83] & 2020 & Chaotic Theory & Medical diagnosis problems \\ \hline
[84] & 2014 & Chaotic Theory & Truss structures \\ \hline
[85] & 2012 & Chaotic Theory & Image matching \\ \hline
[81] & 2018 & Chaotic Theory & Benchmark test function \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of chaotic based methods.  
by [90]. Particles are sent to areas of anticipated peaks; once located, particles are sent there to cater for them. Table 6 gives a summary of this approach.