Online Diversity Assessment in Evolutionary Multiobjective Optimization: A Geometrical Perspective


Many diversity metrics have been proposed for offline diversity measurement of the whole population in multiobjective optimization. Most of the existing methods require knowledge of the exact Pareto optimal front or the ideal vector. For this reason, there is no direct approach to use the diversity metrics in an online manner. In this paper we propose an online diversity metric that is inspired by the geometrical interpretation of convergence and diversity. In addition, the proposed method is able to measure the diversity loss caused by any individual in the population. This information is useful in the selection process as the algorithm can perform a diversity-preservation selection based on the measured diversity loss contributed by each individual. To demonstrate the effectiveness of the proposed metric in enhancing the diversification of the solution set, we implement the metric on the well-known multiobjective evolutionary algorithm with decomposition. The simulation results show the applicability and usability of the proposed online diversity measurement.