SUMMARY Metaheuristic science is used to solve global optimization problems by employing a variety of algorithms that are inspired by nature in order to find the best solutions that maximize or minimize cost value. Nowadays, new trends point to the metaheuristic as a solution to a variety of real-world problems. As a result, researchers compete to provide the best solutions to these problems through the use of meta-heuristic algorithms, which can be improved in a variety of ways, including hybridization, integrating with known methods, improving the balance between exploration and exploitation, or introducing a new technique or other ways. This thesis introduces two improved or hybrid meta-heuristic algorithms: "An Elite Opposition Based Improved Salp Swarm Algorithm For Tackling Global Optimization Problems", where the salp swarm algorithm is combined with an elite opposition based learning technique, and "An Improved Meta-heuristic Equilibrium Optimizer Algorithm for Tackling Global Optimization Problems," where the equilibrium optimizer algorithm is integrated with an elite opposition based learning technique. There are four chapters in this thesis: Chapter 1 “Introduction” This chapter introduces the main concepts of metaheuristic and optimization. Chapter 2 “An Elite Opposition Based Improved Salp Swarm Algorithm for Tackling Global Optimization Problems” This chapter introduces a new improved metaheuristic algorithm for solving global optimization problems. This algorithm is improved in two ways: firstly, it is integrated with an elite opposition based learning (EOB) technique to improve the salps' exploration ability, and secondly, the exploitation phase is improved by using the trigonometric cosine function. In the statistical analysis, the proposed algorithm is first evaluated on 23 benchmark problems. The proposed algorithm is compared to a number of well-known algorithms. In almost every way, it outperforms the compared algorithms. Chapter 3 “An Improved Meta-heuristic Equilibrium Optimizer Algorithm for Tackling Global Optimization Problems” A new improved metaheuristic for talking global optimization is presented in this chapter is called IMEO. IMEO algorithm is an improvement of standard Equilibrium Optimizer (EO), it is improved by integrating with an elite opposition based learning (EOB) technique to improve the salps' exploration ability. In the statistical analysis, the proposed algorithm is first tested on 23 benchmark problems. The proposed algorithm is compared to a number of well-known algorithms. In almost every way, it outperforms the compared algorithms. Chapter 4 “Conclusions and Future Works” The thesis conclusions and future work directions are presented in this chapter.