Scholarlink

<p>To address the limitations of existing research on vehicle handling and stability optimization, this paper proposes a suspension parameter optimization method based on a novel multi-objective optimization algorithm. First, a full-vehicle multi-degree-of-freedom (MDOF) dynamic model is constructed in accordance with suspension K characteristics, and the variation laws of key parameters are characterized via handling and stability simulation tests. Subsequently, the optimization objectives and a comprehensive evaluation function for vehicle handling and stability are defined. The proposed novel Multi-Objective Grey Wolf Optimizer (MOGWO) is then adopted to solve for the Pareto optimal solution set under predefined constraints, from which the optimal suspension parameter combination is determined. For benchmarking and comparative analysis, the widely accepted Non-dominated Sorting Genetic Algorithm II (NSGA-II) is also implemented in this work. Comparisons between the pre- and post-optimization results demonstrate that the proposed MOGWO-based method achieves higher evaluation scores for vehicle handling and stability metrics, and outperforms the NSGA-II algorithm in terms of optimization efficiency and solution quality.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimization of Vehicle Suspension Parameters Based on the Multi-Objective Grey Wolf Optimizer

  • 期刊论文

  • 发表日期：

    2026年4月17日

查看全文
  • Jun Zhang,
  • Zijin Cao,
  • Kun Zhang,
  • Jiawen Chen

摘要

To address the limitations of existing research on vehicle handling and stability optimization, this paper proposes a suspension parameter optimization method based on a novel multi-objective optimization algorithm. First, a full-vehicle multi-degree-of-freedom (MDOF) dynamic model is constructed in accordance with suspension K characteristics, and the variation laws of key parameters are characterized via handling and stability simulation tests. Subsequently, the optimization objectives and a comprehensive evaluation function for vehicle handling and stability are defined. The proposed novel Multi-Objective Grey Wolf Optimizer (MOGWO) is then adopted to solve for the Pareto optimal solution set under predefined constraints, from which the optimal suspension parameter combination is determined. For benchmarking and comparative analysis, the widely accepted Non-dominated Sorting Genetic Algorithm II (NSGA-II) is also implemented in this work. Comparisons between the pre- and post-optimization results demonstrate that the proposed MOGWO-based method achieves higher evaluation scores for vehicle handling and stability metrics, and outperforms the NSGA-II algorithm in terms of optimization efficiency and solution quality.