Crowdsourcing software project scheduling based on group learning particle swarm optimization algorithm

To solve the three coupled subproblems of the crowdsourcing software project scheduling including developer selection, task assignment and determination of the dedications, by introducing the reputation of the developers and considering the constraints such as task skills, working hours and team size, a mathematical model was constructed aiming to maximize the completion quality and minimize the project duration simultaneously. A group learning particle swarm optimization algorithm was proposed to solve the model, which adopted a three-segment hybrid encoding method and divided the population into three groups according to the fitness ranking. The number of particles in different groups changed adaptively with the evolutionary generation, and each group employed distinct update strategies according to the differences of fitness values. The proposed algorithm was compared with 10 representative algorithms on 12 instances with different scales. Experimental results showed that the proposed algorithm could obtain a scheduling solution with higher precision.