Space Crowdsourcing Allocation Scheme Based On Improved Fishing Optimization Algorithm

This study proposes a mobile crowdsourcing task allocation method based on the Improved Fishing Optimization Algorithm (ICFOA-SCAM), aiming to address the issue of user preferences and waiting time being overlooked in existing crowdsourcing platforms, thereby improving user satisfaction. Optimizing task allocation has become the key to improving service quality. The ICFOA-SCAM method comprehensively considers the personalized preferences of users, the abilities of workers, and the spatiotemporal characteristics of tasks. By defining key concepts such as spatiotemporal units, user preference evaluation scores, waiting time evaluation scores, and user satisfaction evaluation scores, it enriches the definition of tasks and crowdsourcing authors, enabling the model to more comprehensively consider the complexity of spatial crowdsourcing task allocation. The experimental results show that the ICFOA-SCAM method outperforms traditional white whale optimization algorithm and grey wolf optimization algorithm in terms of user satisfaction and task allocation efficiency. ICFOA-SCAM improves the global search capability and convergence speed of the algorithm by introducing Singer chaotic mapping initialization and Lévy flight strategy. The contribution of this article lies in proposing a new crowdsourcing task allocation model, which not only improves the efficiency and quality of task allocation, but also enhances user satisfaction. Future research will explore how to leverage workers' social relationships to further enhance the effectiveness of task allocation, as well as improve the service quality and user satisfaction of crowdsourcing workers while protecting location and information privacy.