The multi-objective task assignment scheme for software crowdsourcing platforms involving new workers

Software crowdsourcing has become a cornerstone of the Internet economy because of its unique capacity for selecting optimal workers to complete specific tasks. However, new workers face limited task opportunities compared to experienced workers, which negatively impacts their motivation and decreases overall activity on crowdsourcing platforms. This reduced activity can harm platform reputation. To encourage the active participation of new workers, this study introduces a novel method to identify and match worker-task preferences. Our approach categorizes preferred tasks based on golden tasks, historical data, and worker interests. We then present the Multi-Objective Worker-Task Recommendation (MOWTR) algorithm, built upon the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The MOWTR algorithm allocates tasks by considering worker preferences, salaries, and capabilities, aiming to optimize collective team performance while minimizing team costs, especially for new workers. New crossover and two-stage mutation operators are incorporated to increase algorithm efficiency. Experimental evaluations on four real and synthetic datasets demonstrate that MOWTR outperforms four advanced baseline methods, confirming its effectiveness.