# Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts

arXiv: https://arxiv.org/abs/2205.14344 · HTML: https://mlsanity.com/paper/2205.14344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2022.

## Built on by (most cited first, 3 of 3)

- [Multi-Fidelity Methods for Optimization: A Survey](https://mlsanity.com/paper/2402.09638/) (2024)
- [Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2401.02160/) (2024)
- [A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints](https://mlsanity.com/paper/2402.05955/) (2024)

## Abstract (arXiv)

Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions. The current research is mainly developed for problems with a 'regular' triangle-like Pareto-optimal front (PF), whereas the performance can significantly deteriorate when the PF consists of disconnected segments. Furthermore, the offspring reproduction in the current data-driven EMO does not fully leverage the latent information of the surrogate model. Bearing these considerations in mind, this paper proposes a data-driven EMO algorithm based on multiple-gradient descent. By leveraging the regularity information provided by the up-to-date surrogate model, it is able to progressively probe a set of well distributed candidate solutions with a convergence guarantee. In addition, its infill criterion recommends a batch of promising candidate solutions to conduct expensive objective function evaluations. Experiments on 33 benchmark test problem instances with disconnected PFs fully demonstrate the effectiveness of our proposed method against four selected peer algorithms.