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Combining Simple and Adaptive Monte Carlo Methods for Approximating Hypervolume
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DOI：10.1109/TEVC.2020.2969965.png)
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摘要
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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.
Keyword:
Monte Carlo methods
Approximation algorithms
Approximation error
Optimization
Computational efficiency
Urban areas
Error probability
Approximation algorithms
hypervolume
multiobjective optimization
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Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
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