MULTI-OBJECTIVE OPTIMIZATION OF HYDRODYNAMIC BEARING BASED ON ADAPTIVE GOLDEN JACKAL ALGORITHM (MT)

XU Kai; ZHANG HuiFang

doi:10.16579/j.issn.1001.9669.2023.03.019

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MULTI-OBJECTIVE OPTIMIZATION OF HYDRODYNAMIC BEARING BASED ON ADAPTIVE GOLDEN JACKAL ALGORITHM (MT)

更新时间：2023-08-01

- MULTI-OBJECTIVE OPTIMIZATION OF HYDRODYNAMIC BEARING BASED ON ADAPTIVE GOLDEN JACKAL ALGORITHM (MT)
- Journal of Mechanical Strength Issue 3, Pages: 640-645(2023)
- 作者机构：
  
  1. 新乡职业技术学院数控技术学院
  2. 新乡职业技术学院教务处
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2023.03.019
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XU Kai, ZHANG HuiFang. MULTI-OBJECTIVE OPTIMIZATION OF HYDRODYNAMIC BEARING BASED ON ADAPTIVE GOLDEN JACKAL ALGORITHM (MT). [J]. Journal of Mechanical Strength (3):640-645(2023)
DOI：

XU Kai, ZHANG HuiFang. MULTI-OBJECTIVE OPTIMIZATION OF HYDRODYNAMIC BEARING BASED ON ADAPTIVE GOLDEN JACKAL ALGORITHM (MT). [J]. Journal of Mechanical Strength (3):640-645(2023) DOI： 10.16579/j.issn.1001.9669.2023.03.019.

摘要

为提高动压滑动轴承的承载能力，同时降低发热量和摩擦因数，建立了动压滑动轴承的多目标优化模型。针对传统优化算法收敛速度慢、易陷入局部最优解的不足，提出一种自适应策略改进的金豺优化算法，进而提高金豺算法的勘探和探索能力。利用含有约束条件的算例对自适应策略改进的金豺优化算法进行性能验证，结果表明自适应策略改进的金豺优化算法具有良好的收敛性能。将该算法用于求解动压滑动轴承的多目标优化问题，优化结果表明，优化后的结构相对初始结构性能有了较大提升，承载能力提高了12.257%,发热量和摩擦因数分别降低了15.610%和33.333%。

Abstract

In order to improve the load-carrying capacity of the hydrodynamic bearing, and reduce the calorific value and friction coefficient at the same time, a multi-objective optimization model of the hydrodynamic bearing was established. Aiming at the shortcomings of the traditional optimization algorithm that the convergence speed is slow and it is easy to fall into the local optimal solution, an adaptive strategy assisted the golden jackal optimization algorithm is proposed to improve the exploration and exploration ability of the golden jackal algorithm. The performance of the adaptive strategy assisted the golden jackal optimization algorithm is verified by an example with constraints. The results show that the adaptive strategy assisted the golden jackal optimization algorithm has good convergence performance. The algorithm is used to solve the multi-objective optimization problem of hydrodynamic bearings. The optimization results show that the optimized structure has a great improvement in performance compared with the initial structure, the load capacity is increased by 12.257%, and the calorific value and friction coefficient are reduced respectively by 15.610% and 33.333%.

关键词

金豺优化算法自适应策略动压滑动轴承多目标优化优化算法

Keywords

Golden jackal optimization algorithmAdaptive strategyHydrodynamic bearingMulti-objective optimizationOptimization algorithm

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