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arXiv · 2104.09736

Hypervolume-Optimal $μ$-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions

Ke Shang·Hisao Ishibuchi·Weiyu Chen·Yang Nan·Weiduo Liao
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Abstract

Hypervolume is widely used in the evolutionary multi-objective optimization (EMO) field to evaluate the quality of a solution set. For a solution set with $μ$ solutions on a Pareto front, a larger hypervolume means a better solution set. Investigating the distribution of the solution set with the largest hypervolume is an important topic in EMO, which is the so-called hypervolume optimal $μ$-distribution. Theoretical results have shown that the $μ$ solutions are uniformly distributed on a linear Pareto front in two dimensions. However, the $μ$ solutions are not always uniformly distributed on a single-line Pareto front in three dimensions. They are only uniform when the single-line Pareto front has one constant objective. In this paper, we further investigate the hypervolume optimal $μ$-distribution in three dimensions. We consider the line- and plane-based Pareto fronts. For the line-based Pareto fronts, we extend the single-line Pareto front to two-line and three-line Pareto fronts, where each line has one constant objective. For the plane-based Pareto fronts, the linear triangular and inverted triangular Pareto fronts are considered. First, we show that the $μ$ solutions are not always uniformly distributed on the line-based Pareto fronts. The uniformity depends on how the lines are combined. Then, we show that a uniform solution set on the plane-based Pareto front is not always optimal for hypervolume maximization. It is locally optimal with respect to a $(μ+1)$ selection scheme. Our results can help researchers in the community to better understand and utilize the hypervolume indicator.

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Ke Shang, Hisao Ishibuchi, Weiyu Chen, Yang Nan, Weiduo Liao. 2021-04-20. Hypervolume-Optimal $μ$-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions. https://doi.org/10.1109/tevc.2021.3093114

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