Monkeypox optimizer: A TinyML bio-inspired evolutionary optimization algorithm and its engineering applications

High-dimensional optimization remains a key challenge in computational intelligence, especially under resource constraints. Evolutionary algorithms, which mimic the change in heritable characteristics of biological populations, have been proposed to address this. These algorithms apply selection pressure to favor better solutions over generations, and stochastic variations may occasionally introduce suboptimal candidates to preserve population diversity. However, they often struggle to balance exploration and exploitation, leading to suboptimal solutions, premature convergence, and significant computational demands, making them unsuitable for resource-constrained environments. This paper introduces Monkeypox Optimization (MO), a novel evolutionary algorithm inspired by the infection and replication lifecycle of the monkeypox virus. MO mimics the virus’s rapid spread by employing virus-to-cell infection, where the virus persistently seeks out vulnerable cells to penetrate—representing global exploration of the search space. Once inside, cell-to-cell transmission enables fast local propagation, modeling the refinement of high-potential solutions through accelerated replication. To conserve resources, MO continuously deletes the least effective virion copies, maintaining a compact and memory-efficient population. This biologically grounded design not only accelerates convergence but also aligns MO with TinyML principles, making it ideally suited for low-power, resource-constrained IoT environments. MO is benchmarked against 21 recent algorithms across 90 functions from CEC-2017, CEC-2019, and CEC-2020, and validated on three engineering design problems. Results show MO achieves up to 13% lower energy consumption and 34% shorter execution time compared to state-of-the-art competitors, while maintaining robust accuracy. A theoretical analysis reveals MO’s time complexity is O ( m n + R T n ) , confirming its scalability. Statistical validation via Friedman and Fisher tests further supports MO’s performance gains. • Introduces Monkeypox Optimization (MO), a TinyML-compatible bio-inspired algorithm. • Mimics virus replication to efficiently balance exploration and exploitation. • Outperforms 21 algorithms on CEC-2017, CEC-2019, and CEC-2020 benchmark functions. • Reduces energy use by 13% and reduces the execution time by 34% in TinyML tasks. • Validated on real-world engineering problems, ensuring robust practical applications. • Proves the potential of MO in solving complex optimization tasks in constrained environments, showcasing its applicability for future IoT systems and embedded devices.