Solving multi-task manufacturing cloud service allocation problems via bee colony optimizer with transfer learning

Industrial internet platform is regarded as an emerging infrastructure to increase the manufacturing efficiency via sharing resources located in multiple sites. Manufacturing cloud service allocation (MCSA) aims to assign available services to the interconnected subtasks of a complicated task such that some performance indices are optimized. Current studies on MCSA are single-task-oriented and fail to exploit the shared task-solving experiences to jointly optimize a group of tasks with enhanced solution quality and search speed. This work considers the joint optimization of multiple MCSA problems in a parallel fashion via cross-task transfer learning mechanism, and two novel transfer learning strategies are embedded into the framework of bee colony algorithm to make the best use of cross-task helpful knowledge when resolving multi-task MCSA. The first one is to design an individual-dependent transfer learning mechanism to govern the probability of whether a bee to perform intra-task self-evolution or cross-task knowledge transfer, which adaptively regulates the search behavior of each bee according to its state. The second one is to select the potential bees from foreign tasks for knowledge exchange with the aid of anomaly detection mechanism. The proposed optimizer is extensively examined on different scales of MCSA instances in multi-task scenario. Experimental results confirm the performance advantage of our proposal in comparison with other state-of-the-art peers.