Scholarlink

<p>To address the limitations of traditional suspension bridge main cable shaping methods, which cannot automatically perform shape-finding during the optimization process, this paper proposes a bisection-parabolic (BP) method. This method is integrated with traditional finite element (FE) modeling to increase the efficiency of FE modeling during the optimization process. A multi-objective optimization model for suspension bridges is presented, which uses layout and dimension parameters as design variables while aiming to reduce cumulative material costs and improve the safety factor, under the premise of meeting design requirements. The Lasso function is used for elastic net regression analysis to evaluate the impact of design variables on the objective functions. This study proposes a multi-objective particle swarm optimization algorithm based on a dual-population coevolution strategy (DPMOPSO) to solve the optimization problem. The algorithm divides the population into two parts, using the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization algorithm (MOPSO) for solving, with improvements to enhance the algorithm’s performance. The test results demonstrate that DPMOPSO is an effective algorithm for escaping local optima and achieving better multi-objective optimization.</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

Multiobjective optimization of suspension bridges via coupled modeling and dual population multiobjective particle swarm optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

查看全文
  • Peiling Yang,
  • Jianhua Deng,
  • Anli Wang

摘要

To address the limitations of traditional suspension bridge main cable shaping methods, which cannot automatically perform shape-finding during the optimization process, this paper proposes a bisection-parabolic (BP) method. This method is integrated with traditional finite element (FE) modeling to increase the efficiency of FE modeling during the optimization process. A multi-objective optimization model for suspension bridges is presented, which uses layout and dimension parameters as design variables while aiming to reduce cumulative material costs and improve the safety factor, under the premise of meeting design requirements. The Lasso function is used for elastic net regression analysis to evaluate the impact of design variables on the objective functions. This study proposes a multi-objective particle swarm optimization algorithm based on a dual-population coevolution strategy (DPMOPSO) to solve the optimization problem. The algorithm divides the population into two parts, using the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization algorithm (MOPSO) for solving, with improvements to enhance the algorithm’s performance. The test results demonstrate that DPMOPSO is an effective algorithm for escaping local optima and achieving better multi-objective optimization.