ABSTRACT These days, there are a sizable number of meta-heuristic algorithms that are utilized to address many problems with numerous variables and huge complexity. One of the most popular swarm intelligence-based meta-heuristic methods is Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting. This paper proposes a Weighted ChOA (WChOA) alternative to tackle two main issues that occur in large-scale numerical optimization problems such as low convergence speed and trapping in local optima in solving high-dimensional problems. The main difference between the standard ChOA and WChOA is that a position-weighted equation is offered to enhance convergence speed and avoid local optima. Moreover, the balance between exploration and exploitation is carried out in the proposed method that is crucial in the swarm intelligence-based algorithms. The presented WChOA method is evaluated in different conditions to prove that it is the best. For this purpose, a classical set of 30 unimodal, multimodal, and fixed-dimension multimodal benchmark functions is applied to investigate the pros and cons of characteristics of WChOA. Besides, WChOA is tested on the IEEE Congress of Evolutionary Computation benchmark test functions (CECC06, 2019 Competition). To shed more light on probing the performance of WChOA in large-scale numerical optimization and real-world problems, WChOA is examined by 13 high-dimensional and 10 real-world optimization problems. The results show that the WChOA outperforms in terms of convergence speed, the probability of getting stuck in local minimums, exploration, and exploitation compared to state-of-the-art methods in literature such as ChOA, PSO, BBO, WOA, BH, ALO, GA, SCA, and GWO.