A Novel Virus Diffusion Optimization (VDO) Algorithm for Global Optimization

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 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.