<p>The chimp optimization algorithm (ChOA) is a recently introduced metaheuristic algorithm inspired by nature. This algorithm identified four types of chimpanzee groups: attacker, barrier, chaser, and driver, and proposed a suitable mathematical model for them, based on the various intelligence and sexual motivations of chimpanzees. However, this algorithm is not more successful in the convergence rate and escaping of the local optimum trap in solving high-dimensional problems. Although it and some of its variants use some strategies to overcome these problems, it is observed that it is not sufficient. Therefore, in this study, a newly expanded variant is described. In the algorithm, called Ex-ChOA, hybrid models are proposed for position updates of search agents, and a dynamic switching mechanism is provided for transition phases. This flexible structure solves the slow convergence problem of ChOA and improves its accuracy in multi-dimensional problems. Therefore, it tries to achieve success in solving global, complex, and constrained problems. The performance of the proposed algorithm was analyzed on a total of 34 benchmark functions and a total of 17 real-world optimizations, including classical, constrained, and modern engineering problems. According to the results obtained, the proposed algorithm performs better or equivalent performance than the compared algorithms.</p>