Due to development of new computational technologies, simulation is becoming a very popular tool in designning of large, complex and random systems. Despite of the analytical methods are not capable of solving such problems, however, flexibility of simulation often leads to models with high computational and to obtain a statistical estimation of the system is needed to run a large number of simulations in order to replacing experiments. If the number of experiments is high, the cost of simulation is very high. Therefore, simulated optimization is an issue that has preoccupied by many researchers. Simulation Optimization conducted in the allocation of computing resources intelligently. In fact, the simulation optimization by examining a few of the experiment can achieve a near-optimal performance. The Most real-world problems are multi-objective and to achieve an optimized solution requires optimization of two or more objective function simultaneously. Traditional simulation optimization methods in the case of multi-objective are ineffective and do not cover the entire Pareto frontier. So, the new multi-objective simulation optimization methods must be created. The main purpose of this research is providing new approaches in this area which is created by a combination of tools such as: discrete event simulation, data envelopment analysis, Cuckoo Optimization Algorithm, response surface method, interactive method. The main advantage and the main reason for the popularity of hybrid simulation optimization approach are use of the benefits of all methods at the same time. Five new hybrid approaches are presented in this research, which includes interactive and non-interactive methods. These algorithms are ESCOA, SRC, two-stage PPRC, IDEA and IDRC. Due to the importance of taking into account the views of decision-makers in the process of problem solving and achieving to the preferred solutions, interactive solving methods should be established. It is noteworthy that all the proposed methods in this research are implemented and evaluated to practical problems such as supplier selection, production planning and so on. The hybrid proposed methods are presented of different points of view; each of them focuses on a part of the scope of multi-objective simulation optimization. Because to the Cuckoo Optimization Algorithm is applied in the proposed methods in this research, the improved approach of Cuckoo optimization algorithm is presented. In general, research in this study have a continuous form and new features are added to the algorithm on each approach. Improved Cuckoo Algorithm is used as optimizer in the hybrid proposed algorithms. The first three algorithms which that mentioned above, are non-interactive and interactive mode added to the extended algorithms.