A Novel Virus Diffusion Optimization (VDO) Algorithm for Global Optimization
Zhaoqi Sun, Qingsong Wang

TL;DR
This paper introduces a Virus Diffusion Optimizer (VDO), a biologically inspired metaheuristic that enhances global exploration and local exploitation, outperforming existing algorithms on benchmark problems.
Contribution
The paper presents a novel VDO algorithm inspired by virus life-cycle dynamics, integrating four strategies to improve optimization performance and scalability.
Findings
VDO outperforms state-of-the-art metaheuristics on benchmark problems.
VDO demonstrates superior convergence speed and solution quality.
VDO shows strong scalability in large-scale optimization tasks.
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…
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