A Convolutional Neural Network-Based Surrogate Model for Multi-objective Optimization Evolutionary Algorithm Based on Decomposition

Abstract

Evolutionary algorithms (EAs) show good performance in solving multi-objective optimization problems (MOPs). An EA needs to perform a substantial number of fitness evaluations. For the MOP with high complexity, the fitness evaluation functions are computationally expensive, making the evolutionary algorithms time-consuming. Surrogate-assisted evolutionary algorithms (SAEAs) that apply surrogate models instead of fitness exact evaluation functions have successfully reduced the computational complexity of fitness evaluations. However, because training a surrogate model requires a certain amount of calculation, a large amount of calculation is required by the SAEA to train multiple surrogate models. Furthermore, most existing surrogate models may not achieve desired evaluation accuracy when processing medium-dimensional and high-dimensional MOPs. This paper proposes a novel surrogate model. The surrogate model can be applied in multi-objective optimization evolutionary algorithm based on decomposition (MOEA/D), which is a classic decomposition-based multi-objective optimization algorithm. The surrogate model is designed based on the convolutional neural network structure, and it is called the multi-objective parallel fitness evaluation network (MPFEN). An MPFEN model contains multiple sub-networks which can be applied as the surrogate models. By training the MPFEN model, we can obtain all surrogate models required by a MOEA/D simultaneously without training each required surrogate model separately. Therefore, the amount of calculation of training surrogate models in a MOEA/D is reduced. The evaluation accuracy of the MPFEN model is tested by experiments. The experimental results show that the evaluation accuracy of MPFEN model is higher than that of other classical surrogate models in most cases. By applying the MPFEN model, the solution quality of SAEA is also improved.