###### Abstract  
Since the increases in electricity demand, environmental awareness, and power reliability requirements, solutions of single-objective optimal power flow (OPF) and multi-objective OPF (MOOPF) (two or three objectives) problems are inadequate for modern power system management and operation. Solutions to the many-objective OPF (more than three objectives) problems are necessary to meet modern power-system requirements, and an efficient optimization algorithm is needed to solve the problems. This paper presents a many-objective marine predators algorithm (MaMPA) for solving single-objective OPF (SOOPF), multi-objective OPF (MOOPF), and many-objective OPF (MaOPF) problems as this algorithm has been widely used to solve other different problems with many successes, except for MaOPF problems. The marine predators algorithm (MPA) itself cannot solve multi- or many-objective optimization problems, so the non-dominated sorting, crowding mechanism, and leader mechanism are applied to the MPA in this work. The considered objective functions include cost, emission, transmission loss, and voltage stability index (VSI), and the IEEE 30- and 118-bus systems are tested to evaluate the algorithm performance. The results of the SOOPF problem provided by MaMPA are found to be better than various algorithms in the literature where the provided cost of MaMPA is more than that of the compared algorithms for more than 1000 USD/h in the IEEE 118-bus system. The statistical results of MaMPA are investigated and express very high consistency with a very low standard deviation. The Pareto fronts and best-compromised solutions generated by MaMPA for MOOPF and MaOPF problems are compared with various algorithms based on the hypervolume indicator and show superiority over the compared algorithms, especially in the large system. The best-compromised solution of MaMPA for the MaOPF problem is found to be greater than the compared algorithms around 4.30 to 85.23% for the considered objectives in the IEEE 118-bus system. m 20  
In [6], a modified shuffle frog-leaping algorithm (MSFLA) was presented to solve the OPF problem considering economical and emission issues. The OPF problem was solved by hybrid particle-swarm optimization and a shuffle frog-leaping algorithm (PSO-SFLA) where the economic issues involving the prohibited zones, valve point effect and multi-fuel type of generation units, and emission problems were considered [7]. In [8], an improved particle-swarm optimization (IPSO) was introduced to solve the OPF problem considering cost, loss, voltage stability, and emission impacts as the objectives. In addition to the cost, loss, and environmental awareness, power blackouts often happen in many countries, such as North America and Europe [9], southern Sweden and eastern Denmark [10], and Italy [11; 12], resulting in wide damages to power companies and people. One of the main causes of power blackouts is voltage instability consequently leading to voltage collapse generally occurring when the power demand significantly rises. Hence, voltage stability requires to be enhanced to improve power system reliability. One solution to enhance system voltage stability is to employ voltage stability indices (VSIs). VSIs are indicators applied to observe the proximity of power systems to voltage collapse. The values of VSIs are normally between 0 representing a no-load condition and 1 indicating a voltage collapse. System voltage stability can thus be enhanced if a VSI is used as the objective function to be minimized. From the aforementioned problems, four objectives including fuel cost, emission, transmission line loss, and VSI are considered as part of the objective functions in the OPF problem. Solving the OPF problem is a challenging task that has attracted many researchers in the past decades.