A knowledge-based fruit fly optimization algorithm for multi-skill resource-constrained project scheduling problem

In this paper, a knowledge-based fruit fly optimization algorithm (KBFOA) is proposed for the multi-skill resource-constrained project scheduling problem (MSRCPSP). In the KBFOA, the solution is represented by two lists, i.e. resource list and task list. The smell-based search is implemented through neighborhood based search operators designed for the MSRCPSP, and the vision-based search adopts a greedy strategy to update the fruit fly swarm. In addition, a knowledge-based search procedure is introduced to enhance the exploration, which utilizes the knowledge gained by the superior fruit fly during the evolution. Furthermore, the influence of parameter setting of the KBFOA is investigated based on the Taguchi method of design of experiments, and a suitable parameter setting is recommended. Finally, numerical simulation results based on some benchmark instances and comparison with the existing algorithm are provided, which demonstrate the effectiveness and efficiency of the proposed KBFOA in solving the MSRCPSP.