A comparison of modified tree–seed algorithm for high-dimensional numerical functions

Optimization methods are used to solve many problems and, under certain constraints, can provide the best possible results. They are inspired by the behavior of living things in nature and called metaheuristic algorithms. The population-based tree–seed algorithm (TSA) is an example of these algorithms and is used to solve continuous optimization problems that have recently emerged. This method, inspired by the relationship between trees and seeds, produces a certain number of seeds for each tree during each iteration. In this study, during seed formation in the TSA, trees were selected using the tournament selection method rather than by random means. Efforts were also made to enhance high-dimensional solutions, utilizing problem dimensions, D, of 20, 50, 100 and 1000 by optimizing the search tendency parameter within the structure of the algorithm, resulting in a modified TSA (MTSA). Empirical test data, convergence graphs and box plots were obtained by applying the MTSA to numerical benchmark functions. In addition, the results of the current algorithms in the literature were compared with the MTSA and the statistical test results were presented. The results from this analysis demonstrated that the MTSA could achieve superior results to the original TSA.