首页 | 本学科首页   官方微博 | 高级检索  
     

基于DE和PSO的混合智能算法及其在模糊EOQ模型中的应用*
引用本文:曾宇容,王林,富庆亮.基于DE和PSO的混合智能算法及其在模糊EOQ模型中的应用*[J].计算机应用研究,2012,29(2):438-441.
作者姓名:曾宇容  王林  富庆亮
作者单位:1. 湖北经济学院信息管理学院,武汉,430205
2. 华中科技大学管理学院,武汉,430074
基金项目:国家自然科学基金资助项目(70801030);湖北省教育厅科研重点资助项目(D20112201);国家教育部人文社会科学研究青年基金项目(11YJC630275)
摘    要:设计了融合差分进化和PSO算法优点的混合智能优化算法DEPSO,通过在粒子迭代过程中,随机选择一定数量的粒子进行差分进化操作,增加粒子的多样性,使陷入局部极小的粒子逃出,以保证DEPSO的全局收敛性能,并采用典型测试函数验证了DEPSO的性能。针对模糊相关机会规划EOQ模型求解难题,设计了基于模糊模拟方法和DEPSO的智能求解算法来计算模糊事件的可信性,从而得到了使库存费用不超过预算水平的可信度最大的最优订货量,算例证实了此求解算法的有效性。

关 键 词:经济订货批量  相关机会规划  差分进化  粒子群优化  混合智能算法

Hybrid intelligent algorithm based on differential evolution and particle swarm optimization algorithm and its application in fuzzy EOQ model
ZENG Yu-rong,WANG Lin,FU Qing-liang.Hybrid intelligent algorithm based on differential evolution and particle swarm optimization algorithm and its application in fuzzy EOQ model[J].Application Research of Computers,2012,29(2):438-441.
Authors:ZENG Yu-rong  WANG Lin  FU Qing-liang
Affiliation:1.School of Information Management,Hubei University of Economics,Wuhan 430205,China;2.School of Management,Huazhong University of Science & Technology,Wuhan 430074,China)
Abstract:This paper designed a novel hybrid intelligent algorithm(DEPSO) by integrating advantages of DE and PSO algorithm. During iterative process in the PSO, a certain randomly selected particles would receive the differential evolution operator to increase the diversity of particles. Then, particles with the local minimum would escape to ensure the global convergence of the proposed hybrid algorithm. The performance of DEPSO algorithm was tested using typical test functions. Aiming at the fuzzy dependent-chance programming EOQ model, designed an intelligent algorithm to calculate the credibility of the fuzzy event using DEPSO algorithm and fuzzy simulation. Then, obtained the optimal order quantity for maximizing the credibility of an event such that the total cost did not exceed the budget constraint. At last, a numerical example shows the validity of the proposed algorithm.
Keywords:economic order quantity(EOQ)  dependent-chance programming  differential evolution(DE)  particle swarm optimization(PSO)  hybrid intelligent algorithm
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《计算机应用研究》浏览原始摘要信息
点击此处可从《计算机应用研究》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号