## 5 Conclusions  
A new meta-heuristic optimization technique has been developed in this paper, based on the hybridization of two methods, Jellyfish Search (JS) and Meth Flame Optimizer (MFO), called JS-MFO. This new hybrid approach is designed to improve the solution quality when dealing with complex Optimal Power Flow (OPF) problems. To improve the performance of the JS method in the exploitation mechanism, a spiral path of the MFO method on the best solutions found after the exploration phase is introduced. In this way, we can benefit from the advantages of the JS and MFO algorithms. The efficiency and precision of JS-MFO have been confirmed by applying it to several complex OPF optimization problems. These problems deal with the minimization of total power generation cost, gas emissions from power plants, total voltage deviation, and total transmission losses. Two test grids were employed to solve these problems: the popular IEEE 30-bus grid and the Mauritanian RIM 27-bus grid. For the IEEE 30-bus test system, JS-MFO presented a percent cost saving of 11.40% (optimization of the quadratic total fuel cost) with respect to the base case. This benefit is the highest when it is compared to those of the JS and MFO algorithms. It results in an annual cost saving of 16,951 USD/year with respect to the optimal value given by the JS algorithm. The percent gas emission reduction when dealing with the environmental optimization problem is about 14.38% for the JS-MFO algorithm (the highest value compared to those of JS and MFO) with reference to the base case. The JS-MFO algorithm shows a gas emission reduction of 1.752 tons/year with respect to the value given by the JS algorithm, and of 0.876 tons/year with respect to the amount given by the MFO algorithm. By optimizing the active transmission losses, a significant percent loss saving of 50.60% is assigned to the JS-MFO algorithm (greater than those of JS and MFO) with reference to the base case. For the RIM 27-bus power system, the loss percent saving using JS-MFO is 50.67% compared to the base case. The optimization of the total voltage deviation using JS-MFO showed a reduction of 62.44% with respect to the base case. The results obtained prove the capacity of the proposed approach to dominate the basic meta-heuristic techniques, JS, MFO, and others recently developed in the literature based on the comparison report for the same test network and the same objective functions. The aptitude of this hybrid JS-MFO technique to deal with complex OPF problems makes it competitive for (i) treating more problems in the power system, such as a variety of cases with complex objective functions, security constraints, prohibited zones, and different test systems, and (ii) offering prospects for improving the performance of the Mauritania RIM 27-bus grid by optimizing the location and sizing of new renewable sources. In view of the potential and superior characteristics of the proposed JS-MFO algorithm, a multi-objective algorithm based on JS-MFO is recommended to be developed and applied to solve multi-objective OPF problems. Figure 11: Voltage profile for each RIM 27-bus case using the JS-MFO algorithm. **Author Contributions:** Conceptualization: C.M. and A.S.; methodology: A.S., D.N., and C.S.E.K. ; software: C.M., A.S., and F.Z.A. ; validation: R.A.E.-S., C.A., and D.N. ; formal analysis: C.M., R.A.E.-S., F.H., and F.Z.A. ; investigation: A.S., R.A.E.-S., D.N., and C.S.E.K. ; resources: C.A. and C.S.E.K. ; data curation: C.M., R.A.E.-S., and C.A. ; writing--original draft preparation: C.M., F.Z.A., and F.H. ; writing--review and editing: C.M., F.Z.A., F.H., and R.A.E.-S; visualization: F.H., D.N., and C.S.E.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** All required data are included in the text. **Conflicts of Interest:** The authors declare no conflicts of interest. **Nomenclature**