In recent years, impressive capabilities have been demonstrated by deep learning-based vulnerability detection models in detecting vulnerabilities. Previous research has widely explored adversarial attacks using variable renaming to introduce disturbances in source code and evade detection. However, the effectiveness of introducing multiple disturbances through various transformation techniques in source code has not been adequately investigated. In this study, multiple synonymous transformation operators are applied to introduce disturbances in source code. A combination optimization strategy based on genetic algorithms is proposed, enabling the selection of source code transformation operators with the highest fitness to guide the generation of adversarial code segments capable of evading vulnerability detection. The proposed method is implemented in a framework named non-vulnerability generator (NonVulGen) and evaluated against deep learning-based vulnerability detection models. When applied to recently developed deep learning models, an average attack success rate of 91.38% is achieved against the CodeBERT-based model and 93.65% against the GraphCodeBERT-based model, representing improvements of 28.94% and 15.52% over state-of-the-art baselines, respectively. To assess the generalization ability of the proposed attack method, common models including Devign, ReGVD, and LineVul are targeted, achieving average success rates of 98.88%, 97.85%, and 92.57%, respectively. Experimental results indicate that adversarial code segments generated by NonVulGenx cannot be effectively distinguished by deep learning-based vulnerability detection models. Furthermore, significant reductions in attack success rates are observed after retraining the models with adversarial samples generated based on the training data, with a decrease of 96.83% for CodeBERT, 97.12% for GraphCodeBERT, 98.79% for Devign, 98.57% for ReGVD, and 97.94% for LineVul. These findings reveal the critical challenge of adversarial attacks in deep learning-based vulnerability detection models and highlight the necessity for model reinforcement before deployment.