A Social-Aware Ant Colony Optimization Algorithm with Reproductive Division of Labor for MCS Task Allocation

SHEN Xiaoning; SHE Juan; WANG Zhilong; LI Jiayuan

doi:10.11999/JEIT260018

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SHEN Xiaoning, SHE Juan, WANG Zhilong, LI Jiayuan. A Social-Aware Ant Colony Optimization Algorithm with Reproductive Division of Labor for MCS Task Allocation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260018

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SHEN Xiaoning, SHE Juan, WANG Zhilong, LI Jiayuan. A Social-Aware Ant Colony Optimization Algorithm with Reproductive Division of Labor for MCS Task Allocation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260018

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A Social-Aware Ant Colony Optimization Algorithm with Reproductive Division of Labor for MCS Task Allocation

doi: 10.11999/JEIT260018 cstr: 32379.14.JEIT260018

SHEN Xiaoning^{1, 2, 3, 4},
SHE Juan^{1
,
,},
WANG Zhilong¹,
LI Jiayuan¹

1.
School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
2.
Jiangsu Provincial Key Laboratory of Big Data Analysis Technology, Nanjing 210044, China
3.
Jiangsu Engineering Research Center of Meteorological Energy Utilization and Control, Nanjing 210044, China
4.
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (61502239), The Natural Science Foundation of Jiangsu Province (BK20150924)

Received Date: 2026-01-06
Accepted Date: 2026-05-12
Rev Recd Date: 2026-05-05

Available Online: 2026-06-02

Abstract

Abstract

Objective With the rapid development of handheld and wearable smart devices, Mobile Crowd Sensing (MCS) has become an efficient data collection paradigm. Effective task allocation can improve system efficiency, requester and participant satisfaction, and platform sustainability. Existing models often neglect task skill requirements, do not use participants’ social networks as auxiliary execution resources in emergencies, and overlook the effect of collaboration efficiency on team-task quality. To address these issues, this paper proposes a Social-Aware MCS Task Allocation model (SAMCSTA) with two objectives: maximizing total platform revenue and total task sensing quality. Social networks are used to build a two-layer collaboration framework of platform participants and social-network friends, which expands available execution resources and improves allocation flexibility. For complex tasks, participant sensing capability is quantified, and collaboration efficiency is introduced to optimize team composition. Methods This paper proposes a Multi-objective Ant Colony Optimization based on Reproductive Division of Labor (MACORDL) algorithm. The main innovations are as follows. First, the ant colony is divided into four collaborative subpopulations: queen ants, male ants, scout ants, and worker ants. Local enhancement, memetic crossover, knowledge transfer, and other search strategies are designed for these subpopulations to form a hierarchical collaborative search framework. Second, a statistical-learning-based mating selection strategy is designed to support intelligent transfer of elite genes. Third, the short-term contribution of each subpopulation is predicted from historical performance, which enables dynamic and adaptive allocation of computational resources. Fourth, a cooperative update mechanism for node pheromones and participant pheromones is designed to establish a dual-layer search guidance system. Results and Discussions The evaluation uses 8 synthetic instances and 4 real-world instances. Performance is measured by HyperVolume Ratio (HVR) and Inverted Generational Distance (IGD). The Wilcoxon rank-sum test at a significance level of 0.05 is used for statistical comparison. The results show that MACORDL achieves the best HVR and IGD on most instances (Table 2, Table 3). On average, MACORDL improves HVR and IGD by 16.41% and 18.04%, respectively, compared with the second-best algorithm. Visual comparisons further show that the Pareto front obtained by MACORDL has better convergence, distribution uniformity, and breadth (Fig. 4). Although its fine-grained local search can still be improved for a few large-scale instances, MACORDL shows stable performance and good scalability across different problem scales. It helps the platform obtain task allocation schemes with higher revenue and better sensing quality. Conclusions This paper studies the task allocation problem in MCS systems by considering interactions among platform participants and between participants and their social-network friends. A social-aware MCS task allocation model is established, and MACORDL is proposed to solve it. Comparative experiments on 8 synthetic instances and 4 real-world instances with different scales show that MACORDL outperforms six representative algorithms on most instances. It obtains allocation schemes and paths that yield higher total platform revenue and better task sensing quality, indicating good scalability. MACORDL uses multiple strategies to balance local exploitation and global exploration. However, the current model assumes that all tasks are released at the initial stage and that complete information is available. Participant privacy protection is also not considered. Future work will focus on MCS task allocation models in dynamic and uncertain environments and on privacy-preserving distributed optimization.
- Mobile Crowd Sensing (MCS),
- Task allocation,
- Social collaboration,
- Ant colony optimization,
- Reproductive division of labor

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References(22)

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