Designing parallelism in surrogate-assisted multiobjective optimization based on decomposition

On the one hand, surrogate-assisted evolutionary algorithms are established as a method of choice for expensive black-box optimization problems. On the other hand, the growth in computing facilities has seen a massive increase in potential computational power, granted the users accommodate their approaches with the offered parallelism. While a number of studies acknowledge the impact of parallelism for single-objective expensive optimization assisted by surrogates, extending such techniques to the multi-objective setting has not yet been properly investigated, especially within the state-of-the-art decomposition framework. We first highlight the different degrees of parallelism in existing surrogate-assisted multi-objective evolutionary algorithms based on decomposition (S-MOEA/D). We then provide a comprehensive analysis of the key steps towards a successful parallel S-MOEA/D approach. Through an extensive benchmarking effort relying on the well-established bbob-biobj test functions, we analyze the performance of the different algorithm designs with respect to the problem dimensionality and difficulty, the amount of parallel cores available, and the supervised learning models considered. In particular, we show the difference in algorithm scalability based on the selected surrogate-assisted approaches, the performance impact of distributing the model training task and the efficacy of the designed parallel-surrogate methods.