A comparative study of pre-screening strategies within a surrogate-assisted multi-objective algorithm framework for computationally expensive problems

The multi-offspring method has been recognized as an efficient approach to enhance the performance of multi-objective evolutionary algorithms. However, some pre-screening strategies should be used when a multi-offspring-assisted multi-objective evolutionary algorithm is used to solve computationally expensive problems. So far, there is no any reported comprehensive study that compares the effects of different pre-screening strategies on the performance of the multi-offspring-assisted multi-objective evolutionary algorithms. In this paper, four pre-screening strategies (convergence-based, maximin distance-based expected improvement matrix (EIM-based), diversity-based and random-based strategies) for the multi-offspring-assisted multi-objective evolutionary algorithm are compared. The convergence-based strategy gives more priority to non-dominated solutions, and it is vital for exploiting the current promising areas. The diversity-based strategy gives more priority to solutions with greater uncertainties, and it is important for exploring the sparse areas. The EIM-based strategy considers the exploration and exploitation simultaneously, and the random-based strategy gives no priority to any solution. A series of benchmark problems whose dimensions vary from 8 to 30 and a reactive power optimization problem are used to test the multi-offspring-assisted multi-objective evolutionary algorithm under the four pre-screening strategies. The experimental results show that the convergence-based strategy performs best on most of the simple problems, while the EIM-based strategy performs best on most of the complex problems. The diversity-based strategy can produce positive effects on some problems, while the random-based strategy cannot improve the performance of its basic algorithm.

Automated summary

The paper compares four pre-screening strategies for the multi-offspring-assisted multi-objective evolutionary algorithm, indicating that the convergence-based strategy performs best on simple problems, while EIM-based strategy excels on complex problems. The diversity-based strategy has positive effects on certain problems, whereas the random-based strategy does not enhance the algorithm performance.