Solving continuous optimization problems using the tree seed algorithm developed with the roulette wheel strategy

Abstract

The Tree seed algorithm (TSA) is a metaheuristic algorithm inspired by the relationship between trees and seeds. It has been proposed for very low-dimensional optimization problems and achieved promising results compared to other optimization algorithms. However, it has been determined that the performance of the TSA is lower than other algorithms for high-dimensional problems. This is due to the fact that TSA cannot scan the local optimum and search space effectively. A new TSA based on the roulette wheel strategy (R-TSA) has been proposed in this study to eliminate this disadvantage and solve high-dimensional problems. With this strategy, the trees selected at the seed production phase of TSA were diversified and the locations of the seeds were updated to prevent it from being stuck in local minima, with the aim of scanning the search space more effectively. The R-TSA was applied to high-dimensional (20, 50 and 100) benchmark functions and both convergence and box-plot graphs were obtained by using the results of these functions. Moreover, current algorithms in published literature were applied to these functions and the results obtained were compared with the R-TSA. It was observed from the analysis results that the performance of the R-TSA was higher than that of TSA.