{"ID":2848925,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24083","arxiv_id":"2510.24083","title":"A Novel Virus Diffusion Optimization (VDO) Algorithm for Global Optimization","abstract":"Meta-heuristic algorithms are widely used to tackle complex optimization problems, including nonlinear, multimodal, and high-dimensional tasks. However, many existing methods suffer from premature convergence, limited exploration, and performance degradation in large-scale search spaces. To overcome these limitations, this paper introduces a novel Virus Diffusion Optimizer (VDO), inspired by the life-cycle and propagation dynamics of herpes-type viruses. VDO integrates four biologically motivated strategies, including viral tropism exploration, viral replication step regulation, virion diffusion propagation, and latency reactivation mechanism, to achieve a balanced trade-off between global exploration and local exploitation. Experiments on standard benchmark problems, including CEC 2017 and CEC 2022, demonstrate that VDO consistently surpasses state-of-the-art metaheuristics in terms of convergence speed, solution quality, and scalability. These results highlight the effectiveness of viral-inspired strategies in optimization and position VDO as a promising tool for addressing large-scale, complex problems in engineering and computational intelligence.To ensure reproducibility and foster further research, the source code of VDO is made publicly available.","short_abstract":"Meta-heuristic algorithms are widely used to tackle complex optimization problems, including nonlinear, multimodal, and high-dimensional tasks. However, many existing methods suffer from premature convergence, limited exploration, and performance degradation in large-scale search spaces. To overcome these limitations,...","url_abs":"https://arxiv.org/abs/2510.24083","url_pdf":"https://arxiv.org/pdf/2510.24083v1","authors":"[\"Zhaoqi Sun\",\"Qingsong Wang\"]","published":"2025-10-28T05:51:58Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[\"Diffusion Model\",\"LoRA\"]","has_code":false}