[1]曲鹏举,何 雪.花粉授粉机制在改进粒子群算法研究[J].机械与电子,2024,42(02):15-21.
　QU Pengju,HE Xue.Research on Pollen Pollination Mechanism Employed in Improved Particle Swarm Optimization[J].Machinery & Electronics,2024,42(02):15-21.
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花粉授粉机制在改进粒子群算法研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年02期
页码:
15-21
栏目:
设计与研究
出版日期:
2024-02-27

文章信息/Info

Title:
Research on Pollen Pollination Mechanism Employed in Improved Particle Swarm Optimization
文章编号:
1001-2257 ( 2024 ) 02-0015-07
作者:
曲鹏举; 何 雪
贵州理工学院工程训练中心,贵州 贵阳 550025
Author(s):
QU Pengju ;  HE Xue
( Engineering Training Centre , Guizhou Institute of Technology , Guiyang 550025 , China )
关键词:
粒子群算法; 满意度评价值; Logistic 混沌映射; 花粉授粉阈值; 惯性权重幂函数
Keywords:
particle swarm optimization ;  satisfaction evaluation value ;  Logistic chaos mapping ;  pollination threshold ;  inertial weight power function
分类号:
TH165 ; TP391
文献标志码:
A
摘要:
针对柔性作业中多目标优化问题,首先构建多目标任务满意度数学模型,该模型以最小加工时间、最低制造成本和最短运输时间为目标,去量纲操作后利用几何平均法求解综合满意度评价值。然后,提出一种改进的粒子群算法( LFPSO ),该算法为平衡算法全局和局部搜索能力,惯性权重采用幂函数自适应调节,为改变粒子群前期的搜索性能,在惯性权重中加入了 Logistic 混沌映射丰富粒子多样性,为平衡全局搜索能力与局部搜索能力,引入花粉授粉机制作为全局搜索阈值。最后,将 LFPSO 算法与其他算法进行仿真对比,结果验证了 LFPSO 算法具有良好的性能及解决柔性作业多目标优化问题的有效性。
Abstract:
Aiming at the multi-objective optimization problem in flexible operations , a multi-objective task satisfaction mathematical model is first constructed.The model takes the minimum machining time , the lowest manufacturing cost and the shortest transportation time as its objectives , and uses the geometric average method to solve the comprehensive satisfaction evaluation value after dedimensional operation.Secondly , an improved Logistic chaotic map and flower pollination mechanism employed in particle swarm optimization ( LFPSO ) is proposed.The inertia weight of the algorithm adopts power function adaptive adjustment to balance global search ability and local search ability.In order to increase the search performance of particle swarm in the early stage , Logistic chaos mapping is added to the inertia weight to enrich particle diversity.In order to balance global search ability and local search ability , pollen pollination mechanism is introduced as the global search threshold.Finally , the LFPSO algorithm is simulated and compared with the other three algorithms , and the results verify that the LFPSO algorithm has good performance and the effectiveness of solving the multi-objective optimization problem of flexible operation.

参考文献/References:

[ 1 ] 朱光宇,王浩杰 . 考虑运输时间的紧前约束下柔性作业车间调度[ J ] . 华中科技大学学报(自然科学版), 2022 ,50 ( 6 ): 139-148.
[ 2 ] AQEL G A , LI X Y , GAO L.A modi ed iterated greedy algorithm for flexible job shop scheduling problem [ J ] . Chinese journal of mechanical engineering , 2019 , 32( 2 ): 157-167.
[ 3 ] 唐红涛,李悦,王磊 . 模糊分布式柔性作业车间调度问题的求解算法[ J ] . 华中科技大学学报(自然科学版),2022 , 50 ( 6 ): 81-88.
[ 4 ] 李瑞,龚文引 . 改进的基于分解的多目标进化算法求解双目标模糊柔性作业车间调度问题[ J ] . 控制理论与应用,2022 , 39 ( 1 ): 31-40.
[ 5 ] 孟磊磊,张彪,任亚平,等 . 求解分布式柔性作业车间调度的混合蛙跳算法[ J ] . 机械工程学报, 2021 , 57 ( 17 ):263-272.
[ 6 ] MENG L L , ZHANG C Y , REN Y P , et al.Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem [ J ] .Computers and industrial engineering , 2020 , 142 : 106347.
[ 7 ] 曲鹏举 . 改进粒子群算法在柔性作业加工时间问题研究[ J ] . 机械与电子, 2023 , 41 ( 1 ): 3-6 , 12.
[ 8 ] 包贤哲,丁稳房,宋阿妮 . 混沌压缩非线性粒子群算法求解车间调度问题[ J ] . 现代制造工程, 2020 ( 9 ): 15-22 , 52.
[ 9 ] 卞京红,贺兴时,杨新社 . 基于萤火虫算法的自适应花授粉优化算法[ J ] . 计算机工程与应用, 2016 , 52 ( 21 ):162-167 , 217.
[ 10 ] 李克文,梁永琪,李绍辉 . 基于混合策略改进的花朵授粉算法[ J ] . 计算机应用研究, 2022 , 39 ( 2 ): 361-366.
[ 11 ] KENNEDY J , EBERHART R.Particle swarm optimization [ C ] ∥Proceedings of ICNN ’95 International Conference on Neural Networks , 1995 : 1942-1948.
[ 12 ] ZHOU Y Q , WANG R , LUO Q F.Elite opposition based flower pollination algorithm [ J ] .Nerocomputing , 2016 , 188 : 294-310.
[ 13 ] 黎书文,张成龙,周知进 . 基于改进粒子群算法的离散制造车间柔性调度优化[ J ] . 组合机床与自动化加工技术,2018 ( 11 ): 150-152.
[ 14 ] 侯晓莉,刘永,江来臻,等 . 多目标 FJSP 的一维编码粒子群优化求解方法[ J ] . 计算机工程与应用, 2015 , 51 ( 13 ): 47-51 , 71.

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备注/Memo

备注/Memo:
收稿日期: 2023-07-29
基金项目:贵州省教育厅青年科技人才成长项目(黔教合 KY 字[ 2018 ] 243 );贵州省教育厅青年科技人才成长项目(黔教技[ 2022 ] 274 号)
作者简介:曲鹏举 ( 1988- ),男,河南林州人,硕士,讲师,研究方向为先进制造模式与制造信息系统。
更新日期/Last Update: 2024-03-21