Complementary surrogate-assisted differential evolution algorithm for expensive multi-objective problems under a limited computational budget

Different surrogate-assisted strategies can greatly influence the optimization efficiency of surrogate-assisted multi-objective evolutionary algorithms. By hybridizing two complementary surrogate-assisted strategies, this study proposed an efficient surrogate-assisted differential evo-lution algorithm to optimize expensive multi-objective problems under a limited computational budget. The two proposed surrogate-assisted strategies balance global and local search for multi -objective optimization. Specifically, one strategy is an improved surrogate-based multi-objective local search method that is based on maximin angle-distance sequential sampling. Compared with the previous local search method that is based on Euclidian distance-based sampling, the improved local search method is more efficient because it can mitigate the scale difference of different objectives. The other surrogate-assisted strategy is prescreening based on a diversity -enhanced expected improvement matrix infill criterion. The proposed infill criterion aims to improve the diversity of approximate Pareto optimal solutions by considering distribution of candidate individuals in the objective space in the infill function. Within a limited computational burden, the performance of the proposed algorithm is demonstrated on a large set of multi -objective benchmark problems, as well as a real-world airfoil design problem. Experimental re-sults show that the proposed algorithm performs significantly better than some existing algo-rithms on most problems investigated in this study.

Automated summary

This study proposes an efficient surrogate-assisted differential evolution algorithm by hybridizing two complementary strategies to optimize expensive multi-objective problems with limited computational resources. One strategy is an improved local search method based on maximin angle-distance sequential sampling. The other strategy is prescreening based on a diversity-enhanced expected improvement matrix infill criterion.