基于改进人工蜂群算法的云制造服务组合优化方法

doi:10.11959/j.issn.1000-436x.2023024

通信学报 ›› 2023, Vol. 44 ›› Issue (1): 200-210.doi: 10.11959/j.issn.1000-436x.2023024

基于改进人工蜂群算法的云制造服务组合优化方法

胡强¹, 田雨晴¹, 綦浩泉¹, 吴鹏¹, 刘庆雪²

¹ 青岛科技大学信息科学技术学院，山东青岛 266061
² 昆明学院机电工程学院，云南昆明 650214

修回日期:2022-12-17 出版日期:2023-01-25 发布日期:2023-01-01
作者简介:胡强（1980- ），男，山东邹城人，博士，青岛科技大学副教授、硕士生导师，主要研究方向为服务计算、人工智能
田雨晴（1998- ），女，陕西咸阳人，青岛科技大学硕士生，主要研究方向为服务计算、智能优化算法
綦浩泉（1998- ），男，山东高密人，青岛科技大学硕士生，主要研究方向为云计算、自然语言处理
吴鹏（1967- ），男，山东菏泽人，青岛科技大学高级工程师，主要研究方向为大数据分析、人工智能
刘庆雪（1980- ），男，山东邹城人，博士，昆明学院副教授，主要研究方向为群智能优化算法
基金资助:
国家自然科学基金资助项目(61973180);山东省自然科学基金资助项目(ZR2019MF033);山东省自然科学基金资助项目(ZR2021MF092);山东省重点研发计划基金资助项目(2021RKY02037);云南省教育厅科学研究基金资助项目(2022J0635)

Optimization method for cloud manufacturing service composition based on the improved artificial bee colony algorithm

Qiang HU¹, Yuqing TIAN¹, Haoquan QI¹, Peng WU¹, Qingxue LIU²

¹ School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
² School of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China

Revised:2022-12-17 Online:2023-01-25 Published:2023-01-01
Supported by:
The National Natural Science Foundation of China(61973180);The Natural Science Foundation of Shan-dong Province(ZR2019MF033);The Natural Science Foundation of Shan-dong Province(ZR2021MF092);The Key Research and Development Program of Shandong Province(2021RKY02037);Foundation of Yunnan Provincial Education Department(2022J0635)

摘要/Abstract

摘要：

为提高云制造服务组合的流程寻优质量、效率和稳定性，提出一种基于改进人工蜂群算法的云制造服务组合优化方法。首先，建立了云制造服务组合场景下的3种服务协同质量计算方法；然后，构建了一种融合服务协同质量的云制造服务组合优化模型；最后，设计了一种具有多搜索策略岛屿模型的人工蜂群算法，实现最优云制造服务组合流程的求解。实验结果表明，所提算法在组合流程的寻优质量、效率和稳定性方面均优于当前流行的人工蜂群改进算法和其他群智能算法。

关键词: 云制造, 服务组合, 流程优化, 人工蜂群

Abstract:

To improve the optimization quality, efficiency and stability of cloud manufacturing service composition, a optimization method for cloud manufacturing service composition based on improved artificial bee colony algorithm was proposed.Firstly, three methods of service collaboration quality calculation under cloud manufacturing service composition scenario were put forward.Then, the optimization model with service collaboration quality was constructed.Finally, an artificial bee colony algorithm with multi-search strategy island model was designed to solve the optimal cloud manufacturing service composition.The experimental results show that the proposed algorithm is superior to the current popular improved artificial bee colony algorithms and other swarm intelligence algorithms in terms of optimization quality, efficiency and stability.

Key words: cloud manufacturing, service composition, process optimization, artificial bee colony

中图分类号:

TN92

胡强, 田雨晴, 綦浩泉, 吴鹏, 刘庆雪. 基于改进人工蜂群算法的云制造服务组合优化方法[J]. 通信学报, 2023, 44(1): 200-210.

Qiang HU, Yuqing TIAN, Haoquan QI, Peng WU, Qingxue LIU. Optimization method for cloud manufacturing service composition based on the improved artificial bee colony algorithm[J]. Journal on Communications, 2023, 44(1): 200-210.

图/表 18

图1

图2

图3

表1

不同流程结构的服务质量计算方法"

名称	顺序结构	并行结构	选择结构	循环结构
MT	$\sum_{i = 1}^{n} MT (s_{i})$	max(MT(s_i))	max(MT(s_i))	$\sum_{i = 1}^{n} MT (s_{i})$
SC	$\sum_{i = 1}^{n} SC (s_{i})$	$\sum_{i = 1}^{n} SC (s_{i})$	max(SC(s_i))	$\sum_{i = 1}^{n} SC (s_{i})$
SP	$\frac{1}{n} \sum_{i = 1}^{n} SP (s_{i})$	$\frac{1}{n} \sum_{i = 1}^{n} SP (s_{i})$	min(SP(s_i))	$\frac{1}{n} \sum_{i = 1}^{n} SP (s_{i})$
SR	$\prod_{i = 1}^{n} SR (s_{i})$	min(SR(s_i))	min(SR(s_i))	$\prod_{i = 1}^{n} SR (s_{i})$

表1

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

表2

表3

表4

表5

参考文献 23

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算法	最差值	最优值	平均值	标准差
MGABC	2.71	3.07	2.94	0.102
MABCM	2.83	3.1	2.905	0.079
SFABC	2.74	3.12	2.869	0.105
IABC	2.71	3.12	2.918	0.148
MSSIABC	2.83	3.12	2.985	0.075
IGSA	2.73	3	2.857	0.099
SCRIHHO	2.7	3	2.825	0.113
GA-HH	2.65	3	2.825	0.093
A-NSGA-III	2.69	3.03	2.896	0.114

算法	最差值	最优值	平均值	标准差
MGABC	2.87	3.07	2.952	0.074
MABCM	2.9	3.12	2.99	0.076
SFABC	2.73	3.02	2.898	0.088
IABC	2.73	3.04	2.91	0.12
MSSIABC	2.98	3.14	3.039	0.07
IGSA	2.7	3.03	2.862	0.111
SCRIHHO	2.72	3.05	2.855	0.098
GA-HH	2.68	3.03	2.845	0.113
A-NSGA-III	2.75	3.09	2.933	0.116

算法	最差值	最优值	平均值	标准差
MGABC	2.87	3.09	2.995	0.073
MABCM	2.92	3.12	3.011	0.803
SFABC	2.8	3.12	2.918	0.099
IABC	2.73	3.03	2.918	0.132
MSSIABC	3	3.14	3.09	0.067
IGSA	2.74	2.99	2.81	0.077
SCRIHHO	2.76	3.01	2.856	0.070
GA-HH	2.85	3.01	2.918	0.059
A-NSGA-III	2.84	3.08	2.927	0.079

算法	最差值	最优值	平均值	标准差
MGABC	2.71	3.14	2.859	0.14
MABCM	2.9	3.09	2.967	0.073
SFABC	2.83	3.02	2.896	0.069
IABC	2.8	3.12	2.97	0.130
MSSIABC	2.97	3.15	3.07	0.064
IGSA	2.75	3.04	2.85	0.096
SCRIHHO	2.66	3.01	2.838	0.094
GA-HH	2.81	3.01	2.922	0.660
A-NSGA-III	2.85	3.08	2.945	0.073

基于改进人工蜂群算法的云制造服务组合优化方法

Optimization method for cloud manufacturing service composition based on the improved artificial bee colony algorithm

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