# Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method
## 1 Introduction
Metaheuristic optimization approaches have been proposed to address a variety of OPF problems, including tabu search (TS) [5], a new self-adaptive TLBO with various distributions [6], Harris Hawks optimization (HHO) [7], a grasshopper optimization algorithm (GOA) for a grid-connected microgrid OPF controller [8], multi-objective differential evolution (DE) algorithm [9], teaching-learning-studying-based optimizer (TLSBO) [10], an improved colliding bodies optimization (ICBO) [11], artificial bee colony (ABC) [12], chaotic ABC algorithm [13], adaptive group search optimization (AGSO) [14], modified honey bee mating optimization (MHBMO) [15], moth swarm algorithm (MSA) [16], a hybrid particle swarm optimization (PSO) based on DE (DEEPSO) in the controllable wind-photovoltaic energy systems [17], an enhanced self-adaptive DE [18], slime mold algorithm (SMO) [19], resolving the OPF issue with increased renewable energy penetration via equilibrium optimizer algorithm (EOA) [20], a hybrid PSO and SFLA algorithm [21], differential search algorithm (DSA) [22], a hybrid HHO based on DE (HHODE) [23], improved artificial bee colony (IABC) [24], grey wolf optimizer (GWO) [25], chaotic invasive weed optimization (CIWO) algorithms [26], a Sine-Cosine algorithm [27], TLBO algorithm enhanced with Levy mutation (LTLBO) [28], an improved strength Pareto evolutionary algorithm [29], ant lion algorithm (ALA) [30], interior search algorithm (ISA) [31], Java and modified Jaya algorithm [32; 33], a gaussian bare-bones levy-flight firefly algorithm (GBLFA) [34], an new improved adaptive DE [35], a new modified interior search algorithm (MISA) [36], social spider optimization (SSO) [37], glow-worm swarm optimization (GSO) algorithm [38], an adaptable online primary regulation is formulated in [39] in order to optimize operation for microgrid to minimizing power losses, white shark optimizer (WSO) for OPF of hybrid systems with renewable energy sources [40], an enhanced equilibrium optimizer (EEO) [41], a developed turbulent flow of a water-based optimizer (TFWO) [42] to OPF with single and multi-objective functions applied on the IEEE 30-bus and 57-bus systems, teaching learning-based algorithm (TLBA) for losses and fuel costs optimization of hybrid alternating current (AC) and multi-terminal direct current (DC) power grids [43], etc., which verified the effectiveness of these methods in solving the OPF problems. Studies in this field show that each article has tried to provide an algorithm that can work better than previous methods. This shows the importance of the OPF problem and the need for new methods that can perform better in avoiding local-optimal solutions while converging rapidly. As a result of the students' learning and the teacher's instruction in a typical classroom, [PERSON] et al. proposed a new efficient algorithm in 2012 [44]. Several implementations of this fundamental algorithm have been proposed in several disciplines over the past few decades, and many updated versions have been offered. Still, there is evidence from the literature that TLBO can converge to the optimal local solution [10; 28]. Alternatively, the metaheuristic algorithms can be modified to avoid local optima, although generally, these algorithms have the issue of a relatively slow convergence rate. Therefore, this paper develops a new version of the TLBO algorithm, which combines TLBO with adaptive Gaussian mutation to enhance local optima avoidance ability and fast convergence features. The following are the most significant contributions of the proposed AGTLBO algorithm:  
1. A new adaptive Gaussian mutation is integrated into the conventional TLBOs to enhance its exploration and exploration capabilities. The proposed algorithm can resolve a wide range of OPF problems for IEEE 30-bus, 57-bus, and 118-bus standard power systems with better outcomes than the state-of-the-art metaheuristic algorithms.