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中南大学学报(自然科学版)

Journal of Central South University

第50卷    第11期    总第303期    2019年11月

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文章编号:1672-7207(2019)11-2906-09
基于改进粒子群算法的机车二系弹簧载荷分配优化
韩锟1,杨广为1,黄泽帆1,鲍天哲2

(1. 中南大学 交通运输工程学院,湖南 长沙,410075;
2. 利兹大学 电子电气学院,英国 利兹,LS2 9JT
)

摘 要: 针对机车二系弹簧载荷优化调整这一复杂的多变量优化问题,为进一步提高现有求解方法的优化效果和计算效率,将烟花算法融入粒子群算法,提出一种具有分层递阶结构的改进粒子群算法,算法为3层架构,其中,底层是基础层,为加入维变异算子的粒子群算法,是改进算法的基本框架;中间层是融合层,为引入烟花算法爆炸机制的粒子更新层,主要用于扩大算法搜索范围,提高全局搜索能力;顶层是扰动层,引入扰动因子,避免算法因陷入局部搜索而进行的大量无为冗余迭代,加快全局收敛速度。用典型测试函数对改进算法性能进行测试,并将其应用于机车二系弹簧载荷分配优化调整仿真实验。研究结果表明:改进算法与传统遗传算法、烟花算法和粒子群算法相比,全局搜索能力更强,鲁棒性更好,求解精度更高。

 

关键词: 机车二系弹簧;载荷分配优化;改进粒子群算法;分层递阶结构;改进烟花算法

Optimization of locomotive secondary spring loads distribution based on improved particle swarm optimization algorithm
HAN Kun1, YANG Guangwei1, HUANG Zefan1, BAO Tianzhe2

1. School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China;
2. School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK

Abstract:The optimization of spring loads distribution can be summarized as a complicated nonlinear multidimensional optimization problem. In order to further improve the optimization effect and calculation efficiency of the existing solution methods, an improved particle swarm optimization algorithm(IPSO) with hierarchical structure was proposed by integrating the fireworks algorithm(FWA) into a particle swarm optimization algorithm(PSO). The IPSO is a three-layer architecture. The underlying layer was the basic framework of the IPSO and a classical PSO that adopted a dimensional mutation operator. The middle layer was a fusion layer used to expand the search range of IPSO. In essence, it was a particle update layer that introduced the explosion mechanism in FWA. The top layer was the disturbance layer, with a disturbance factor introduced to avoid redundant iteration due to local search and speed up the global convergence. The IPSO was tested on typical test function and applied to the simulation experiment of the distribution optimization and adjustment of the locomotive secondary spring loads. The results show that compared with traditional genetic algorithm(GA), FWA and PSO, the IPSO has stronger global search ability, better robustness and higher accuracy.

 

Key words: locomotive secondary springs; load distribution optimization; improved particle swarm algorithm; hierarchical structure; improved fireworks algorithm

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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