# Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review
###### Abstract  
A situation where the set of initial solutions lies near the position of the true optimality (most favourable or desirable solution) by chance can increase the probability of finding the true optimality and significantly reduce the search efforts. In optimisation problems, the location of the global optimum solution is unknown a priori, and initialisation is a stochastic process. In addition, the population size is equally important; if there are problems with high dimensions, a small population size may lie sparsely in unpromising regions, and may return suboptimal solutions with bias. In addition, the different distributions used as position vectors for the initial population may have different sampling emphasis; hence, different degrees of diversity. The initialisation control parameters of population-based metaheuristic algorithms play a significant role in improving the performance of the algorithms. Researchers have identified this significance, and they have put much effort into finding various distribution schemes that will enhance the diversity of the initial populations of the algorithms, and obtain the correct balance of the population size and number of iterations which will guarantee optimal solutions for a given problem set. Despite the affirmation of the role initialisation plays, to our knowledge few studies or surveys have been conducted on this subject area. Therefore, this paper presents a comprehensive survey of different initialisation schemes to improve the quality of solutions obtained by most metaheuristic optimisers for a given problem set. Popular schemes used to improve the diversity of the population can be categorised into random numbers, quasirandom sequences, chaos theory, probability distributions, hybrids of other heuristic or metaheuristic algorithms, Levy, and others. We discuss the different levels of success of these schemes and identify their limitations. Similarly, we identify gaps and present useful insights for future research directions. Finally, we present a comparison of the effect of population size, the maximum number of iterations, and ten (10) different initialisation methods on the performance of three (3) population-based metaheuristic optimizers: bat algorithm (BA), Grey Wolf Optimizer (GWO), and butterfly optimization algorithm (BOA). initialisation; metaheuristics; metaheuristics optimisers; population size +
Footnote †: journal: Applied  
Many attributed the popularity of nature-inspired and bio-inspired metaheuristics optimization algorithms to their ability to find near-optimal solutions [2]. This success can be attributed to how these nature-inspired and bio-inspired metaheuristics optimizers mimic natural phenomena [3]. These natural phenomena have inspired the development of almost all of the metaheuristic algorithms. Evolutionary techniques are gaining popularity in the same vein, too, with many novel techniques developed regularly. The performance of evolutionary techniques matches the nature-inspired or bio-inspired algorithms [4]. The successes of these metaheuristic optimizers on real-world problems come with tremendous challenges. These challenges arise from the fact that real-world optimisation problems are complex and have multiple nonlinear constraints. The ability of optimisers to navigate these challenges and achieve optimality depends heavily on how the initial population is distributed, especially for gradient-based optimizers [5]. Though metaheuristic optimizers are gradient-free, they must also be initialised; thus, they are greatly influenced by the nature of the initial population, especially the large-scale multimodal problems. Population-based metaheuristic algorithms show varying abilities to reach a global optimum when the initialisation scheme is varied [6]. Interestingly, in the last decade, there has been an exponential growth in the number of proposed nature-inspired optimisation algorithms.