Task allocation method of spatial crowdsourcing based on user satisfaction utility

doi:10.11772/j.issn.1001-9081.2021081528

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (10): 3235-3243.DOI: 10.11772/j.issn.1001-9081.2021081528

• Frontier and comprehensive applications • Previous Articles

Task allocation method of spatial crowdsourcing based on user satisfaction utility

Peng PENG¹^,²^,³, Zhiwei NI¹^,³, Xuhui ZHU¹^,³

^1.School of Management，Hefei University of Technology，Hefei Anhui 230009，China
^2.North Minzu University，Yinchuan Ningxia 750021，China
^3.Key Laboratory of Process Optimization and Intelligent Decision?making，Ministry of Education （Hefei University of Technology），Hefei Anhui 230009，China

Received:2021-08-27 Revised:2021-11-24 Accepted:2021-11-25 Online:2022-01-07 Published:2022-10-10
Contact: Zhiwei NI
About author:PENG Peng, born in 1988， Ph. D. candidate， lecturer. His research interests include intelligent computing， spatial crowdsourcing.
NI Zhiwei, born in 1963， Ph. D. ， professor. His research interests include artificial intelligence， machine learning， big data.
ZHU Xuhui, born in 1991， Ph. D. ， lecturer. His research interests include deep learning， intelligent computing.
Supported by:
National Natural Science Foundation of China(71521001);Anhui Provincial Natural Science Foundation(1908085QG298)

基于用户满意效用的空间众包任务分配方法

彭鹏¹^,²^,³, 倪志伟¹^,³, 朱旭辉¹^,³

^1.合肥工业大学管理学院, 合肥 230009
^2.北方民族大学, 银川 750021
^3.过程优化与智能决策教育部重点实验室(合肥工业大学), 合肥 230009

通讯作者: 倪志伟
作者简介:第一联系人：彭鹏（1988—），男，安徽合肥人，讲师，博士研究生，CCF会员，主要研究方向：智能计算、空间众包
倪志伟（1963—），男，安徽合肥人，教授，博士，主要研究方向：人工智能、机器学习、大数据; zhwnelson@163.com
朱旭辉（1991—），男，安徽阜阳人，讲师，博士，主要研究方向：深度学习、智能计算。
基金资助:
国家自然科学基金资助项目(71521001);安徽省自然科学基金资助项目(1908085QG298)

Abstract

Abstract:

In view of the actual situations such as the preference and the delay waiting of spatial crowdsourcing users of ride-hailing in life， a task allocation method of spatial crowdsourcing based on user satisfaction utility called IGSO（Improved discrete Glowworm Swarm Optimization）-SSCTA（Spatial Crowdsourcing Task Allocation based on user Satisfaction utility） was proposed. Firstly， user satisfaction utility was defined， which was composed of user preference utility， delay waiting utility and task completion expectation. Secondly， SSCTA model was constructed based on user satisfaction utility. Thirdly， IGSO algorithm was proposed by discrete coding， the initialization of reverse learning collaboration， four improved mobile strategies， adaptive selection strategy and treatment of infeasible solutions. Finally， IGSO algorithm was used to solve the above model. Experimental results on different scale datasets show that compared with the three allocation strategies of time minimization， distance minimization and random allocation， the user satisfaction utility of the proposed method is improved by 9.64%， 11.77% and 15.70% respectively， and the proposed algorithm has better stability and convergence than the greedy algorithm and other improved glowworm algorithms.

Key words: user preference utility, user satisfaction utility, spatial crowdsourcing, task allocation, discrete glowworm swarm optimization algorithm

摘要：

针对生活中专车类空间众包用户存在偏好和延时等待的实际情况，提出一种基于用户满意效用的空间众包任务分配方法IGSO-SSCTA。首先，定义了由用户偏好效用、延时等待效用和任务完成期望组成的用户满意效用；其次，构建了基于用户满意效用的空间众包任务分配（SSCTA）模型；接着，通过离散编码、反向学习协同初始化、四种改进移动策略、自适应选择和不可行解处理，提出一种适用该模型的改进离散萤火虫群优化（IGSO）算法；最后，利用IGSO算法对前述模型进行求解。不同规模数据集上的实验结果表明，所提方法和考虑时间最小化分配、考虑路程最小化分配、随机分配三种策略相比，用户满意效用分别提高了提升了9.64%、11.77%、15.70%；所提算法与贪婪算法和其他改进萤火虫算法相比，也有更好的稳定性和收敛性。

关键词: 用户偏好效用, 用户满意效用, 空间众包, 任务分配, 离散萤火虫群优化算法

CLC Number:

TP301.6

Peng PENG, Zhiwei NI, Xuhui ZHU. Task allocation method of spatial crowdsourcing based on user satisfaction utility[J]. Journal of Computer Applications, 2022, 42(10): 3235-3243.

彭鹏, 倪志伟, 朱旭辉. 基于用户满意效用的空间众包任务分配方法[J]. 《计算机应用》唯一官方网站, 2022, 42(10): 3235-3243.

Figures/Tables 9

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指标编号	指标内容	指标占比/%	对应参数
A	遵守交通规则	86.6	pa
B	路况熟悉度	84.1	pb
C	驾驶平稳	75.9	pc
D	车辆品质	58.2	pd
E	车内卫生	55.1	pe
F	工作者文明礼仪	49.8	pf
G	是否有烟味	34.1	pg
H	工作者性别	15.2	ph

指标编号	指标内容	指标占比/%	对应参数
A	遵守交通规则	86.6	pa
B	路况熟悉度	84.1	pb
C	驾驶平稳	75.9	pc
D	车辆品质	58.2	pd
E	车内卫生	55.1	pe
F	工作者文明礼仪	49.8	pf
G	是否有烟味	34.1	pg
H	工作者性别	15.2	ph

移动策略	萤火虫i各维度数值
移动策略	第1维	第2维	第3维	第4维	第5维	第6维
萤火虫i	3	4	6	8	1	2
互交换	3	1	6	8	4	2
反演	3	1	8	6	4	2
左相邻交换	4	3	6	1	8	2
右相邻交换	3	6	4	8	2	1

移动策略	萤火虫i各维度数值
移动策略	第1维	第2维	第3维	第4维	第5维	第6维
萤火虫i	3	4	6	8	1	2
互交换	3	1	6	8	4	2
反演	3	1	8	6	4	2
左相邻交换	4	3	6	1	8	2
右相邻交换	3	6	4	8	2	1

算法	主要参数
IGSO	ρ=0.4，γ=0.6，β=0.08，r_s =15， n_t =6，R₁=0.4h=0.5，ρ_min=0.25
DGSO
MGSO^［24］	ρ=0.4，γ=0.6，β=0.08，r_s =15，n_t =6
FGSO^［25］	ρ=0.4，γ=0.6，β=0.08，r_s =15，n_t =6
IDFA^［26］	γ=0.08，β=6
CDFA^［27］	γ=0.08，β=6
GR^［6］	—

Task allocation method of spatial crowdsourcing based on user satisfaction utility

基于用户满意效用的空间众包任务分配方法

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Figures/Tables 9

References 32

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Metrics

参数	取值	数据来源
M₁	（10，15，20，25，30，35，40，45，50，55，60，65）	合成
M₂	（500，1 000，1 500，2 000，2 500，3 000）	合成
p_u	pa，pb，pc，pd，pe，pf，pg，ph	调查统计
q_w	［0，100］	随机生成
v_w	［0，5］	随机生成
l_u	［0，100］	真实数据映射
l_w	［0，100］	真实数据映射
p_u	［0，1］	随机生成
z_w	（0.5，1）	随机生成

数据集	P（S）值			P（T）值			P（L）值			P（R）值
数据集	MAX	MIN	AVG	MAX	MIN	AVG	MAX	MIN	AVG	MAX	MIN	AVG
Sdate₁	902.14	812.35	868.24	812.59	735.18	770.04	782.02	741.20	754.03	732.17	696.66	713.14
Sdate₂	1 353.21	1 302.56	1 322.14	1 251.31	1 116.24	1 182.95	1 188.64	1 078.26	1 122.13	1 135.61	1 023.54	1 085.22
Sdate₃	1 906.23	1 989.15	1 762.72	1 681.04	1 538.16	1 588.67	1 609.94	1 533.12	1 555.40	1 610.53	1 408.86	1 507.61
Sdate₄	2 180.23	2 111.58	2 129.45	1 954.93	1 881.47	1 911.24	1 952.31	1 851.23	1 892.34	1 923.68	1 726.47	1 846.12
Sdate₅	2 758.63	2 502.71	2 608.90	2 437.21	2 310.25	2 387.15	2 485.25	2 304.26	2 345.78	2 300.69	2 114.31	2 225.79
Sdate₆	2 993.36	2 907.22	2 962.14	2 779.15	2 615.43	2 704.36	2 721.91	2 606.31	2 651.35	2 675.32	2 546.91	2 612.75
Sdate₇	3 602.15	3 303.21	3 395.63	3 200.15	3 020.21	3 120.15	3 125.37	2 987.13	3 061.24	2 957.28	2 846.20	2 910.58
Sdate₈	3 762.25	3 685.12	3 719.53	3 541.23	3 341.28	3 446.21	3 443.26	3 321.49	3 398.51	3 294.23	3 170.61	3 241.37
Sdate₉	4 301.62	4 020.25	4 152.89	4 005.27	3 750.12	3 850.12	3 900.21	3 740.13	3 801.54	3 819.05	3 653.38	3 743.00
Sdate₁₀	4 670.65	4 501.32	4 598.91	4 301.49	4 142.39	4 219.82	4 211.97	4 085.27	4 139.63	4 102.28	3 941.29	4 012.83
Sdate₁₁	5 256.32	4 901.23	5 035.67	4 799.21	4 529.32	4 627.22	4 685.21	4 502.33	4 577.97	4 544.84	4 173.41	4 459.92
Sdate₁₂	5 521.37	5 364.23	5 407.89	5 113.65	4 942.36	5 027.19	5 015.34	4 912.31	4 971.57	4 927.98	4 742.39	4 745.16

数据集	IGSO	DGSO	MGSO	FGSO	IDFA	CDFA	GR	数据集	IGSO	DGSO	MGSO	FGSO	IDFA	CDFA	GR
Sdate₁	13.01	12.99	15.81	15.91	15.82	16.31	16.19	Sdate₇	14.43	15.32	15.92	15.99	15.92	16.43	22.45
Sdate₂	13.25	13.12	15.93	16.01	16.11	16.62	16.72	Sdate₈	14.87	15.71	16.18	16.23	16.13	16.58	24.62
Sdate₃	13.47	13.24	15.88	15.94	15.88	16.36	17.53	Sdate₉	14.93	15.91	16.25	16.29	15.92	16.50	26.95
Sdate₄	14.01	14.03	15.84	15.89	15.84	16.32	18.96	Sdate₁₀	15.03	16.02	16.42	16.44	16.22	16.69	28.56
Sdate₅	14.12	14.51	15.89	15.93	15.97	16.37	19.17	Sdate₁₁	15.12	16.08	16.37	16.40	16.21	16.65	30.84
Sdate₆	14.27	14.65	15.95	16.01	16.04	16.44	20.96	Sdate₁₂	15.29	16.23	16.44	16.47	16.46	16.73	32.52

数据规模	用户满意效用值/10⁴
数据规模	IGSO	DGSO	MGSO	FGSO	IDFA	CDFA	GR
500	3.883 6	3.788 0	3.839	3.806	3.833 6	3.844 0	3.781 0
1 000	7.624 9	7.558 0	7.582	7.562	7.588 2	7.589 0	7.550 0
1 500	11.396 8	11.274 5	11.320	11.340	11.343 6	11.339 0	11.256 5
2 000	15.128 4	15.010 9	15.080	15.050	15.078 2	15.034 0	14.995 9
2 500	18.895 3	18.742 5	18.830	18.780	18.842 6	18.787 2	18.722 5
3 000	22.599 3	22.461 6	22.540	22.490	22.523 0	22.525 6	22.431 6

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