Abstract
This paper applies two new meta-heuristic algorithms proposed in 2022 for solving different optimization problems, including the driving training-based algorithm (DTBA) and the average and subtract-based algorithm (ASBA). The considered optimization problems employed in this paper are characterized by different quantities of the dimensions and different involved constraints at various degrees of complexity. The results obtained by the two algorithms are illustrated by three types of convergences, including the minimum, average, and maximum convergences. By analyzing the results obtained by the two applied methods on four different optimization algorithms, DTBA proved itself to be the better applied method over ASBA. Particularly, DTBA has reached the optimal fitness value much faster than ASBA, regardless of how complicated the optimization problem is. From these analyses, DTBA is acknowledged to be the effective algorithm for dealing with the considered optimization problems.
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