An Improved NSGA-II for Service Provider Composition in Knowledge-Intensive Crowdsourcing

Abstract

As crowdsourcing attracts more and more attention, knowledge-intensive crowdsourcing (KI-C) is becoming a domain with great potential. Consumers submit complicated and personalized tasks in KI-C, expecting the tasks to be completed according to their requirements. This paper studied the service provider composition (SPC) in order to accomplish complicated tasks proposed by consumers. A mathematical optimization model based on SPC is formulated, aiming to maximize the platform operator income and customer satisfaction. For this model, a hybrid algorithm combining the non-dominated sorting genetic algorithm (NSGA-II) and simulated annealing algorithm (SA) is proposed for solving it. The practicability and validity of the model and algorithm are tested by an example.