In this paper, a new optimization algorithm called Hybrid Leader Based Optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested in finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in the global search to accurately scan different areas of search space. The quality of the results obtained from HLBO is compared with the results of eight well-known algorithms. The simulation results show the superiority of HLBO in further convergence towards the global solution as well as the passage of optimally localized areas of the search space against eight competitor algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving.