Optimal solution of a desired optimization problem is mostly obtained via minimizing or maximizing a real function considering several predefined limitations. Selection of proper optimization algorithm as an optimizer tool plays a key role on the solution process. In this respect, current study intends to compare the performances of two different prevalent metaheuristic optimization algorithms. These are integrated particle swarm optimizer (iPSO) and teaching and learning based optimizer (TLBO). The former method is a single-phase algorithm while the latter one is the double-phase algorithm. Capabilities of both algorithms were compared separately on some mathematical benchmark test problems. Furthermore, to exhibit and compare their performance on solving more complex problems, size and topology optimization of the structural systems are also examined. Achieved results demonstrate the superiority of iPSO in comparison with TLBO in both search capability and convergence rate.