# Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review
## 3 Major Initialisation Methods
### Hybrid with Other Metaheuristic Algorithms
The optimal solution of the optimal shortening of covering arrays (OSCAR) problem was used as the initialisation function of a metaheuristic algorithm [50]. [PERSON], [PERSON], and [PERSON] [51] used the PSO to solve the problem that hampered the Chan and Vese algorithm for image segmentation problems, which is low-performance if the contours are not well initialised; the contours are initialised simultaneously with the population. Their hybrid solution made contour initialisation irrelevant to the performance of the algorithm. Another effort was presented by [52], where a scheme to initialise the fuzzy c-means (FCM) clustering algorithm using the PSO was proposed. Finding the optimal cluster centres was set as the objective function of the PSO. A memetic algorithm that uses the greedy randomized adaptive search procedure (GRASP) metaheuristic and path relinking to initialise and mutate the population was proposed [53]. However, the scalability of the MA was untested. The authors [54] proposed an initialisation scheme that used both the Metropolis-Hastings (MH) and function domain contraction technique (FDCT). MH is helpful when generating the direct sequence of a PD that is difficult. However, MH is best for high multidimensional complex optimisations, as these are problem-dependent. In such a situation, the FDCT is then employed. The FDCT is a sequential three-step solution starting with a random solution generator; and if this is not feasible, then the GBEST PSO generator is applied. If the previous two fail, then the search space reduction technique (SSRT) is applied. These steps ensure that the initialised population leads to a better solution. Competitive swarm optimizer (CSO) is a variant of PSO used by [55] to improve the extreme learning machines (ELM) network by depending on the individual competition of the particles, which optimise its weights and structure. Although the results show great promise, it took more training time to generate effective models. [PERSON], [PERSON], and [PERSON] [56] evaluated an approach to initialise the cuckoo nest based on the correlation between the spectral band of the nest that was proposed. The goal is to ensure convergence by making sure the location of the nest does not repeat. The k-means clustering algorithm is used to select specific clusters on the band based on their correlation coefficient. Another approach is presented to resolve the lack of diversity of PSO and its sensitivity to initialisation, which quickly leads to premature convergence. The crown jewel defence (CJD) is used to escape being stocked in the local optima by relocating and reinitialising the global and local best position. However, the performance of this improvement is not tested in higher dimensions [57]. The DE and local search were combined to improve or enhance the chances of an optimal solution to the hybrid flow-shop scheduling problem [58]. The brainstorm optimisation (BSO) was improved in the work of [59] by implementing a scheme that allows for a reinitialisation scheme to be triggered, based on the current population. In the work of [60], those authors used FA to detect the maxima and number of image clusters through a histogram-based segmentation; the maxima are then used to initialise the parameter estimates of the Gaussian mixture model (GMM). In the work [61], the authors proposed a scheme that enhances the initial conditions of an algorithm by considering these initial conditions to be a sub-optimisation problem where the initial conditions are the parameters to be optimised by the MLA. Their obtained results showed improvements compared to the other algorithms used. The FA was also used in the work of [62] as an optimiser to obtain the initial location of the translation parameters for WNNs. This led to a reduction in the number of hidden nodes of WNN and significantly increased the simultaneous generalisation capability of WNNs. However, time and computational complexity may be a problem for this approach.