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Finding a promising solution for high-dimensional functions is a challenging task. Evolutionary algorithms that mimic the change in the heritable characteristics of biological populations over successive generations have been proposed for this task. The main interesting point about these algorithms is that they ensure that the next generation is at least as good as the current one. However, the accuracy of the majority of evolutionary algorithms is limited. To alleviate this issue, this paper presents a novel optimization algorithm that mimics the monkeypox replication while hijacking body cells, referred to as the monkeypox optimization (MO) algorithm. First, MO starts by trying to attack and penetrate a cell. Second, after the penetration, MO starts replicating itself at a higher rate while ensuring that the new copies are much better, ensuring its convergence towards a desirable solution. For validation, MO is compared against seven recent optimization algorithms from its category using five standard benchmark functions from the IEEE congress of evolutionary. The results are impressive. In particular, MO has a higher convergence speed than its competitors while achieving promising results concerning the best, worst and average solutions.

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