Driving Training-Based Optimization (DTBO) algorithm is a metaheuristic algorithm based on the simulation of driving training process. Improved version of the DTBO is proposed in this paper for solving Optimal Power Flow (OPF) problem. The Improved Driving Training-Based Optimization (IDTBO) algorithm includes the Crowding Distance Technique for more diverse driver and learner selection and incorporates the Levy Flight distribution for better exploration and local optima avoidance. OPF is considered as one of the most difficult optimization problems and is very important for the control of electrical network. The objective of this study is finding the best control variables while minimizing the total generation fuel cost and considering equality and inequality constraints of the system. The standard IEEE 30-bus network is used for evaluating the performance of the IDTBO algorithm for solving OPF problem. For solving conventional power flow equation, Newton Raphson algorithm is considered. Compared to Modified Driving Training-Based Optimization (MDTBO), Teaching Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithms, the proposed method is more accurate and is better in convergence speed. The performance of the IDTBO is very useful for finding the most secure operating point of any electric power system and its convergence speed contributes to improving the dynamic management of a smart electricity grid.
RANDRIAMORA et al. (2026) studied this question.