Multi-Population-based Algorithms (MPAs) are algorithms for searching the solution space in which the population is divided into subpopulations (islands). One of the MPA methods is the Multi-population-based Nature-Inspired Algorithm (MNIA), in which each subpopulation is processed by a different Population-Based Algorithm (PBA). This integrates various approaches to searching the solution space and increasing the efficiency of exploration and exploitation. The aim of this paper is to investigate the influence of the selection of base algorithms in the MNIA on its efficiency. Knowledge in this area can be important in the context of designing mechanisms for the automatic selection of the MNIA formulas during its operation. The approaches considered in the simulations gave very good results and contributed to the formulation of several interesting conclusions.