A multi-objective worker selection scheme in crowdsourced platforms using NSGA-II

Abstract

Crowdsourcing has led to a paradigm shift in how commercial houses execute projects by lowering the production cost. A crucial aspect of crowdsourcing is selecting the best set of workers to perform a task. The environment envisaged in this work is an independent pool of workers, each equipped with a pre-defined set of skills. We assume that these skills do not follow any priority order over each other. Given a task with a set of required skills, we aim to select a team of workers who can collectively fulfil the task’s requirements, while maximizing the collective expertise and minimizing the team cost. We propose a nondominated sorting genetic algorithm II (NSGA II) based algorithm to find the best set of workers that can perform the task. The basic operators of the evolutionary computation approach are tuned in accordance with our problem objectives. We perform a detailed analysis to show that the solution is well distributed along the Pareto optimal, converges exponentially apropos the number of generations and is cost-efficient. The proposed approach is compared with an optimal strategy, a greedy-based method, and another evolutionary-based algorithm to establish its effectiveness. Extensive simulation results using real data set and a synthetic data set were presented to validate our claim.