A two-stage evolutionary algorithm for noisy bi-objective optimization

For noisy bi-objective optimization problems, the algorithm is affected by noise differently in different optimization stages. Generally speaking, only when the impact of the noise is non-negligible, the noise may lead to errors in the quality evaluation of the solutions, and then affect the performance of the algorithm. Therefore, we propose an adaptive switch strategy to make the algorithm switch adaptively among different noise treatments depending on the noise impact to solve noisy bi-objective optimization problems. In addition, a data selection strategy and a model performance estimation method are proposed to enhance the modeling-based denoising method. Furthermore, an output solution selection is used to select more reliable non-dominated solutions as the final output at the late stage of optimization. The performance of the proposed algorithm is verified on the DTLZ and WFG problems. From the experimental results, it can be found that the proposed algorithm is very competitive with other existing algorithms designed for noisy bi-objective optimization problems.