Özet:

Optimum solution of an anticipated problem is generally reached through minimizing or maximizing a governing real function while sometimes should satisfy various predefined limitations. Selecting an algorithm as a main optimizer plays a key role on the solution process. In this respect, current study intends to compare the performances of two different common metaheuristic optimization algorithms as integrated particle swarm optimizer (iPSO) and teaching and learning based optimizer (TLBO). The TLBO is two-phase algorithm while the iPSO is a single-phase algorithm. Their capabilities are compared over some benchmark cases including mathematical functions and structural optimization problems. To increase the complexity of the test problems both size and topology specifications of the structural systems are simultaneously taken as the decision variables. Achieved results demonstrate the superiority of the iPSO in comparison with TLBO in both search capability and convergence rate.

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