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The optimal power flow (OPF) is an important technique for optimizing the control parameters of modern power systems by taking into account the desired objective functions considering system constraints. This paper provides a developed particle swarm optimization (PSO) method for solving the OPF problem with a rigorous objective function of minimizing generation fuel costs for the utility and industrial companies while satisfying a set of system limitations. By reviewing previous OPF investigations, the developed PSO is used in the IEEE 30-bus test system to reduce overall fuel cost, active/reactive power losses, and exhaust. The obtained results are compared to those obtained using a typical PSO technique and other algorithms. In the IEEE 30-bus test system, one of the paper’s key findings is that the cost of fuel is computed as 800.41 $/h, 830.7779 $/h, 825.6922 $/h, 826.54 $/h, 826.3176 $/h, 823.3999 $/h, 786.03 $/h with the conventional PSO, backtracking search algorithm (BSA), hybrid SFLA-SA, differential evolution (DE), enhanced GA (EGA), monarch butterfly optimization (MBO), proposed algorithm, respectively. Moreover, there is a great reduction in the fuel cost by 4.358% compared with the robust MBO algorithm. MATLAB software is used to demonstrate the effectiveness and accuracy of the proposed technique. [ABSTRACT FROM AUTHOR]