An improved performance metric for multiobjective evolutionary algorithms with user preferences

This paper proposes an improved performance metric for multiobjective evolutionary algorithms with user preferences. This metric uses the idea of decomposition to transform the preference information into m+1 points on a constructed preference-based hyperplane, then calculates the Euclidean distances and the angles between the obtained solutions by algorithms and those obtained m+1 points, respectively. By means of these distances and angles, the proposed metric can evaluate effectively both the convergence and diversity of the obtained solution set, with consideration of the preference information. This makes easier and allows meaningful comparisons between different multiobjective evolutionary algorithms using preference information.