###### Abstract
Various traditional optimization methods including quadratic programming [13], the interior point method [14], and nonlinear programming [15] have been introduced and applied to solve the OPF problem; however, with the deficient performance, these methods provided locally optimal solutions and consumed a large amount of effort and time. Metaheuristic algorithms are one of the methods which have been introduced to deal with these problems. In the beginning, most of the metaheuristic algorithms were proposed to solve single-objective optimization problems, including single-objective OPF (SOOPF) problems considering only one objective function at a time. Examples of these algorithms include evolutionary programming (EP) [16], ant colony optimization (ACO) [17], stochastic genetic algorithm (SGA) [18], grasshopper optimization algorithm (GOA) [19], and particle swarm optimization (PSO) [19]. With the requirement for power system improvements to satisfy electricity consumers in more than one aspect, the problem becomes multi-objective OPF (MOOPF) problems which consider two or three objective functions. Over the past few decades, to solve multi-objective problems, various methods consisting of indicator-based methods [20; 21], decomposition-based methods [22; 23; 24], and Pareto-dominance-based methods [25; 26] have been proposed. However, these algorithms consider all decision variables at a time resulting in performance deterioration when the number of decision variables increases. In the past few years, many metaheuristic algorithms have been introduced to overcome the problems and successfully solve MOOPF problems. Some of the algorithms are the slime mould algorithm (SMA) [27], fuzzy adaptive hybrid configuration oriented to a joint self-adaptive PSO and differential evolution algorithm (FAHSPSO-DE) [28], Harris hawks' optimization (HHO) [29], hybrid firefly-Jaya algorithm [30], and modified pigeon-inspired optimization algorithm (MPIO) [31]. Recently, due to the continuous rise in electricity demand, environmental consciousness, and power security requirements, solving SOOPF and MOOPF problems are insufficient for modern power system management and operation. The problem then develops into many-objective OPF (MaOPF) problems in which more than three objective functions are simultaneously considered as part of the objective functions. MaOPF problems are more complicated than MOOPF problems, so a high-performance algorithm is needed to provide efficient solutions. In many-objective optimization problems, the optimal tradeoffs between each conflict objective are called Pareto optimal solutions or Pareto fronts [32]. Various algorithms such as the improved competitive PSO (ICPSO) [33], weight-based ensemble machine learning algorithm (WBELA) [34], and kriging-assisted two-archive algorithm (KTA2) [35]have been introduced to solve many-objective optimization problems in different fields in the last few years. However, with the complication of the many-objective optimization problems including MaOPF problems, it is difficult to find high-quality Pareto optimal solutions providing great values in all objectives. So, only a few algorithms comprising the multi-objective evolutionary algorithm with many-stage dynamical resource allocation strategy (MOEA/D-MRA) [36], many-objective gradient-based optimizer (MaOGBO) [37], improved non-dominated sorting genetic algorithm III (I-NSGA-III) [38], and knee point-driven evolutionary algorithm (KnEA) [39] have been introduced to solve MaOPF problems. Recently, several efficient metaheuristic algorithms have been proposed to successfully solve various optimization problems [40; 41; 42; 43; 44]. In [40], the African cultures' optimization algorithm (AVOA) was proposed to solve benchmark functions and engineering problems. The Arithmetic optimization algorithm (AOA) [41] was introduced to solve welded beam design, tension/compression spring design, and some more mechanical problems.