In this paper, an efficient and reliable evolutionary-based approach is employed to solve the optimal
power flow (OPF) problem. This approach utilizes the global and local exploration capabilities of
particle swarm optimization (PSO) to search for optimal setting of control variables for the OPF which
is formulated as a nonlinear constrained objective optimization problem with both equality and
inequality constraints. To illustrate how the proposed method has an efficient role, the standard IEEE
30-bus power system is studied and the PSO is used to minimize each of the total fuel cost, system
power loss and voltage deviations. Two different types of FACTS devices are embedded with the test
system, SVC and STATCOM, to achieve the objectives functions under certain constraints. The results
show the effectiveness of STATCOM with optimal settings over the SVC with same conditions.