Abstarct: Optimization problems appear in many scientific fields, including engineering sciences. Solving optimization problems with the number of decision variables and also with a large number of constraints may not be possible with the usual mathematical methods or may be very time-consuming and complicated. Therefore, a high-precision solution method is of great importance. The main goal of this thesis is to propose a multi-objective mexta-heuristic genetic lightning search algorithm in order to increase the accuracy of solving various optimization problems. The proposed hybrid algorithm is implemented on seventeen standard test functions. These functions have a convex, concave, continuous or discrete beam front. The simulation results show the high accuracy of the proposed algorithm on continuous convex and then continuous concave problems. Also, the proposed algorithm is checked on discrete problems and its proper performance is confirmed. In this thesis, as an application of the proposed algorithm, a new perspective of the problem of optimal recloser placement is proposed considering manual section switches with the ability to disconnect under load. In addition, by proposing a Markov model, the probability of recloser failure is also considered. The results show the importance of considering the possibility of recloser failure. The proposed method is applied to two real cases. The first case is the RBTS-BUS2 network and the second case is part of the real Mazandaran network. The switch placement problem is solved with two single-objective and multi-objective approaches using the proposed method and is compared with the results of other algorithms. The simulation results show an increase in the accuracy of problem solving and the efficiency of the proposed algorithm. Electrical Engineering Faculty Control Department Developing an optimization method to switch placement in medium voltage network considering ring points and DGs Authur: Mohammad Zaher Ghorbani-Juybari