Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm

In this paper, the system-level synthesis problem (SLSP) is modeled as a multi-objective mode-identity resource-constrained project scheduling problem with makespan and resource investment criteria (MOMIRCPSP-MS-RI). Then, a hybrid Pareto-archived estimation of distribution algorithm (HPAEDA) is presented to solve the MOMIRCPSP-MS-RI. To be specific, the individual of the population is encoded as the activity-mode-priority-resource list (AMPRL), and a hybrid probability model is used to predict the most promising search area, and a Pareto archive is used to preserve the non-dominated solutions that have been explored, and another archive is used to preserve the solutions for updating the probability model. Moreover, specific sampling mechanism and updating mechanism for the probability model are both provided to track the most promising search area via the EDA-based evolutionary search. Finally, the modeling methodology and the HPAEDA are tested by an example of a video codec based on the H.261 image compression standard. Simulation results and comparisons demonstrate the effectiveness of the modeling methodology and the proposed algorithm.