一个改进的多目标粒子群优化算法,用于设计铁路交通上层盖项目基础坑的设计
Jinyan Shao1,2, Yuan Lu3, Yi Sun4
1TOD Institute, Beijing Jiaotong University, Beijing, 100044, China.
Scientific reports
|March 27, 2025
概括
一个新的多目标粒子群优化 (MOIPSO) 算法通过平衡多个目标和提高搜索效率来增强复杂的问题解决能力. 它在基准测试和现实工程应用中表现出强的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 工程应用 工程应用
背景情况:
- 复杂的优化问题需要先进的算法来有效地找到解决方案.
- 现有的粒子群优化 (PSO) 方法可能需要对多目标场景进行改进.
- 接近帕雷托最佳解决方案集对于多目标优化至关重要.
研究的目的:
- 提出一个新的多目标粒子群集优化 (MOIPSO) 算法.
- 在复杂优化中增强解决方案集的多样性和通用性.
- 改进勘探和趋同到全球最佳解决方案.
主要方法:
- MOIPSO算法集成了快速非主导分类和拥挤距离机制.
- 它结合了基于三角函数的加速度因子和自适应的高斯突变.
- 绩效使用CEC2020多模式多目标基准函数进行评估.
主要成果:
- MOIPSO证明了有效接近帕雷托最佳解决方案集.
- 算法显示了解决方案空间的多样性和覆盖率的提高.
- 对比研究表明MOIPSO的竞争力与先进的metaheuristics相比.
结论:
- 拟议的MOIPSO算法对于复杂的多目标优化问题是有效的.
- MOIPSO在现实世界的工程设计中展示了实际的适用性和竞争力,例如铁路运输基础坑.
- 增强的算法提供了一个强大的方法来平衡多个,往往相互冲突的优化目标.
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