[1]文笑雨,罗国富,李浩,等.基于广义粒子群优化模型的工艺规划方法研究[J].郑州大学学报(工学版),2018,39(06):59-63.[doi:10.13705/j.issn.1671-6833.2018.06.009]
　Wen Xiaoyu,Luo Guofu,Li Hao,et al. Research on Process Planning Problems Based on General Particle Swarm Optimization Modelundefined [J].Journal of Zhengzhou University (Engineering Science),2018,39(06):59-63.[doi:10.13705/j.issn.1671-6833.2018.06.009]
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基于广义粒子群优化模型的工艺规划方法研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
39
期数:
2018年06期
页码:
59-63
栏目:
出版日期:
2018-10-24

文章信息/Info

Title:

Research on Process Planning Problems Based on General Particle Swarm Optimization Model

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作者:
文笑雨; 罗国富; 李浩; 肖艳秋; 乔东平; 李晓科
郑州轻工业学院河南省机械装备智能制造重点实验室
Author(s):
Wen Xiaoyu; Luo Guofu; Li Hao; Xiao Yanqiu; Qiao Dongping; Li Xiaoke
Zhengzhou Institute of Light Industry, Henan Provincial Key Laboratory of Mechanical Equipment Intelligent Manufacturing
关键词:
广义粒子群优化算法; 工艺规划; 变邻域搜索; 组合优化
Keywords:
Generalized particle swarm optimization algorithm; process planning; variable neighborhood search; Portfolio Optimization
DOI:
10.13705/j.issn.1671-6833.2018.06.009
文献标志码:
A
摘要:
在广义粒子群优化模型基础上，结合工艺规划问题的特性，设计了求解工艺规划问题的广义粒子群优化算法。该算法采用当前粒子与个体极值库、种群极值库进行交叉操作的方式，使粒子能够从个体极值和种群中获取更新信息，引入变邻域搜索算法作为粒子的局部搜索和随机搜索策略。实例测试结果显示，与其他算法相比，本文算法在求解工艺规划问题时具有更高的求解效率和更好的稳定性.。
Abstract:

An Improved General Particle Swarm Optimization (IGPSO) algorithm was proposed for process planning problem based on the GPSO model and the characteristics of procwss planning problem. Crossover operations were utilized to achieve the particles to obtain updated information from individual extreme library and population extreme library. Variable Neighborhood Search was intaoduced as a local search strategy for particles. A set of instances have been conducted to examine the proposed algorithm and the comparisons among other algorithms appeared in current literature were also presented. The experimental results showed the proposed algorithm had higher efficiency and better stability in solving process planning problems.

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更新日期/Last Update: 2018-10-26