A Social-Aware Ant Colony Optimization 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 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 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 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: Supported by the National Natural Science Foundation of China (61502239), Supported by the Natural Science Foundation of Jiangsu Province, China (No.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 rise of handheld/wearable smart devices, Mobile Crowd Sensing (MCS) has become an efficient data collection paradigm. Effective task allocation improves system efficiency, requester/participant satisfaction, and platform sustainability. Existing models overlook task skill requirements, fail to leverage participants' social networks for emergencies, and ignore collaboration efficiency in team tasks. To address this, we propose a Social-Aware MCS Task Allocation Model (SAMCSTA) with dual objectives: maximizing platform total revenue and overall task perceived quality. The model incorporates social networks to build a two-tier collaboration framework, expanding resources and enhancing flexibility. For complex tasks, it quantifies individual capabilities and introduces a collaboration efficiency mechanism to optimize team composition. Methods This paper proposes a Multi-objective Ant Colony Optimization Based on Reproductive Division of Labor (MACORDL). The core innovations of the algorithm include: (1) Constructing four subpopulations—queen ants, male ants, scout ants, and worker ants—each equipped with distinct strategies such as local enhancement, memetic crossover, and knowledge transfer, forming a hierarchical collaborative search framework; (2) A mating selection strategy based on statistical learning is designed to enable the intelligent transfer of elite genes; (3) The short-term contribution of each subpopulation is predicted based on its historical performance, allowing for dynamic and adaptive allocation of computational resources; (4) Designing a cooperative update mechanism for node pheromones and participant pheromones, establishing a dual-layer search guidance system. Results and Discussions The evaluation uses 8 synthetic and 4 real-world instances, with performance measured by Hypervolume Ratio (HVR) and Inverted Generational Distance (IGD). The Wilcoxon rank-sum test (significance 0.05) is employed for statistical comparison. Results show that MACORDL achieves the best HVR and IGD on most instances (Table 2, Table 3). On average, it outperforms the second-best algorithm by 16.41% in HVR and 18.04% in IGD. Visual comparisons confirm that the Pareto front by MACORDL is superior in convergence, distribution uniformity, and breadth (Fig 4). Though slight improvement remains in fine-grained search on a few large-scale cases, MACORDL demonstrates stable performance and scalability across different scales, enabling the platform to obtain task allocation solutions with higher revenue and better perceived quality. Conclusions This paper addresses the task allocation problem in MCS systems, taking into account both interactions among platform participants and those between participants and their social connections. A social-aware MCS task allocation model is established. The MACORDL algorithm is proposed to solve it. Comparative experiments on 12 real and synthetic instances of varying scales show that MACORDL significantly outperforms six representative algorithms on most instances, obtaining allocation schemes and paths that yield higher revenue and better perceived quality, demonstrating good scalability. MACORDL incorporates multiple strategies to balance local exploitation and global exploration. However, limitations include assumption that all tasks are released at the start with full information, and the lack of participant privacy protection. Future work will focus on dynamic/uncertain MCS task allocation models and privacy-preserving distributed optimization.
- Mobile Crowd Sensing,
- Task Allocation,
- Social Collaboration,
- Ant Colony Optimization,
- Reproductive Division of Labor

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References(22)

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