基于蒙特卡洛仿真和并行粒子群优化算法的携行备件优化

doi:10.3969/j.issn.1000-1093.2016.01.019

兵工学报 ›› 2016, Vol. 37 ›› Issue (1): 122-130.doi: 10.3969/j.issn.1000-1093.2016.01.019

基于蒙特卡洛仿真和并行粒子群优化算法的携行备件优化

张永强^1,2, 徐宗昌¹, 孙寒冰¹，胡春阳¹

(1.装甲兵工程学院技术保障工程系北京 100072； 2.海军航空兵学院辽宁葫芦岛 125000)

收稿日期:2015-03-20 修回日期:2015-03-20 上线日期:2016-03-23
通讯作者: 张永强 E-mail:wying40852@163.com
作者简介:张永强（1983—），男，助理工程师，博士研究生

Optimization of Carried Spare Parts Based on Monte Carlo Simulation and Parallel Particle Swarm Optimization Algorithm

ZHANG Yong-qiang^1,2, XU Zong-chang¹, SUN Han-bing¹, HU Chun-yang¹

(1.Department of Technical Support Engineering, Academy of Armored Force Engineering, Beijing 100072, China;2Naval Air Force Institute, Huludao 125000, Liaoning, China)

Received:2015-03-20 Revised:2015-03-20 Online:2016-03-23
Contact: ZHANG Yong-qiang E-mail:wying40852@163.com

摘要/Abstract

摘要： 为提高携行备件方案优化模型的准确性和求解的精确度，以遂行远海训练任务的舰艇编队为研究背景，针对优化模型的建立和求解提出了一系列改进措施。在传统优化模型的基础上，分析了虚警和串件拼修对备件的影响，建立了基于携行能力、备件成本、装备可用度、同型号装备群完好率等多约束条件的携行备件优化模型；利用粒子群优化（PSO）算法确定备件的优化配置，利用蒙特卡洛仿真法计算配置方案的保障效能；引入云格计算技术实现PSO算法的并行求解，从硬件性能上提高算法的全局寻优能力；将普通粒子转化为量子粒子实现解的多样化，减小了算法陷入局部最优的危险。案例分析证实了改进措施的可行性和有效性。

关键词: 兵器科学与技术, 携行备件, 蒙特卡洛仿真, 粒子群优化算法, 方案优化

Abstract: In order to enhance the veracity of model and the accuracy of solution for carried spare parts scheme, several improvements are raised under the background of naval fleet. An improved model is established, which is based on multi-constraints including carrying capacity, spare parts cost, availability of equipment, and serviceability rate of the same type equipment. Particle swarm optimization(PSO) is used to optimize the spare parts scheme, and Monte Carlo simulation is used to calculate its performance. PSO is concurrently computed on the designed gloud platform, which could improve global optimization ability by hardware parallel computation, and the ordinary particles in PSO are transformed to the quantum particles in special conditions, which could avoid PSO trapping in local optimum. The example shows that these improvements are feasible and effective.

Key words: ordnance science and technology, carried spare parts, Monte Carlo simulation, particle swarm optimization algorithm, scheme optimization

中图分类号:

E917
N945.17

张永强, 徐宗昌, 孙寒冰，胡春阳. 基于蒙特卡洛仿真和并行粒子群优化算法的携行备件优化[J]. 兵工学报, 2016, 37(1): 122-130.

ZHANG Yong-qiang, XU Zong-chang, SUN Han-bing, HU Chun-yang. Optimization of Carried Spare Parts Based on Monte Carlo Simulation and Parallel Particle Swarm Optimization Algorithm[J]. Acta Armamentarii, 2016, 37(1): 122-130.

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基于蒙特卡洛仿真和并行粒子群优化算法的携行备件优化

Optimization of Carried Spare Parts Based on Monte Carlo Simulation and Parallel Particle Swarm Optimization Algorithm

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