Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent

TL;DR

This paper introduces a gradient-based method for multi-objective optimization using the uncrowded hypervolume indicator, showing it can outperform evolutionary algorithms in certain scenarios with available gradients.

Contribution

It demonstrates that the gradient of the uncrowded hypervolume can be computed, enabling direct gradient ascent for multi-objective optimization, which is a novel approach.

Findings

Gradient-based algorithms outperform EAs with exact gradients on small evaluation budgets.

EAs perform similarly or better than gradient methods with larger budgets.

The proposed method remains competitive even with approximate gradients.

Abstract

Evolutionary algorithms (EAs) are the preferred method for solving black-box multi-objective optimization problems, but when gradients of the objective functions are available, it is not straightforward to exploit these efficiently. By contrast, gradient-based optimization is well-established for single-objective optimization. A single-objective reformulation of the multi-objective problem could therefore offer a solution. Of particular interest to this end is the recently introduced uncrowded hypervolume (UHV) indicator, which takes into account dominated solutions. In this work, we show that the gradient of the UHV can often be computed, which allows for a direct application of gradient ascent algorithms. We compare this new approach with two EAs for UHV optimization as well as with one gradient-based algorithm for optimizing the well-established hypervolume. On several bi-objective benchmarks, we find that gradient-based algorithms outperform the tested EAs by obtaining a better hypervolume with fewer evaluations whenever exact gradients of the multiple objective functions are available and in case of small evaluation budgets. For larger budgets, however, EAs perform similarly or better. We further find that, when finite differences are used to approximate the gradients of the multiple objectives, our new gradient-based algorithm is still competitive with EAs in most considered benchmarks. Implementations are available at https://github.com/scmaree/uncrowded-hypervolume.