Combining Simple and Adaptive Monte Carlo Methods for Approximating Hypervolume
Jingda Deng, Qingfu Zhang · IEEE Transactions on Evolutionary Computation · 2020
The computation of hypervolume is a key issue in multiobjective optimization, particularly, multiobjective evolutionary optimization. However, it is NP-hard to compute the exact hypervolume value. Monte Carlo methods have been widely used for approximating the hypervolume. Observing that the basic Monte Carlo method and the fully polynomial-time randomized approximation scheme (FPRAS) suit different solution sets, we propose a combination of these two methods and show that it performs very well on a number of solution sets.