A Guidance method for robustness surrogate assisted multi-objective evolutionary algorithms

In the real world, multi-objective problems(MOPs) are relatively common in optimization in the areasof design, planning, decision support... In fact, problemsinclude two or many objectives, there is a class of problemscalled expensive problems that are problems with complexmathematical models, large computational costs,... Theycan not be solved by normal techniques, they are usually tobe solved with techniques such as simulation, decomposing,problem transformation. In particular, using a surrogatemodel with Kriging, neuron networks techniques in combination with an evolutionary algorithm is a subtle choice,with many positive results, being studied and applied inpractice. However, the use of a surrogate model withKriging, neuron networks combining selection strategy,sampling... can reduce the robustness of the algorithmsduring the search. This paper analyzes the issues affectingthe robustness of the multi-objective evolutionary algorithms (MOEAs) using surrogate models and suggests theuse of a guidance technique to increase the robustness ofthe algorithm, through analysis, experiment and results arecompetitive and effective to improve the quality of MOEAsusing a surrogate model to solve expensive problems.