基于改进的粒子群优化和灰色决策的路头板参数的多目标优化
Qiang Li1,2, Mengdi Gao1, Zhilin Ma1
1School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, China.
这项研究引入了一种改进的粒子群优化 (PSO) 算法,以优化路头机板参数. 新方法通过降低阻力和增加负载能力来提高道效率.
科学领域:
- 工程 工程师 工程师 工程师
- 优化算法 优化算法
- 采矿技术 采矿技术 采矿技术
背景情况:
- 道设备的开发,特别是道路头部,落后,阻碍了高效的煤矿生产.
- 路头板参数显著影响性能,需要优化以提高可靠性和设计.
- 传统的多目标优化方法有局限性,包括依赖先前的知识和易受初始化问题的可能性.
研究的目的:
- 开发和验证一个改进的粒子群优化 (PSO) 算法,用于多目标路头机板参数优化.
- 通过优化板参数以减少阻力和增加负载能力来提高路头车的性能.
- 为工程应用提供实用和高效的多目标优化方法.
主要方法:
- 开发了一种改进的粒子群优化 (PSO) 算法,使用最小欧几里德距离来评估极端值.
- 该算法促进了多目标并行优化,生成了一个非劣质的解决方案集.
- 应用灰色决策理论从非劣质集合中选择最佳解决方案.
主要成果:
- 优化的道路头部板参数是宽度 (l) 为3.144m,倾斜角 (β) 为16.88°.
- 优化导致板质量减少了14.3%.
- 推进阻力下降了6.62%,而负载能力增加了3.68%.
结论:
- 建议的多目标优化方法,结合改进的公共服务任务和灰色决策,是可行的和有效的.
- 优化了板参数,通过降低阻力和增加负载能力,显著提高了路头车的性能.
- 这种方法在实际工程场景中为多目标优化提供了一种方便的方法.
更多相关视频
11:53The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
Published on: October 14, 2017
10:36Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
Published on: November 3, 2023
相关概念视频
Design Example: Alignment of a Road Line Using GIS
Decision Making: P-value Method
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Design Example: Aggregate Gradation
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
Response Surface Methodology
The process of RSM involves several key steps:
Distributed Loads: Problem Solving
