Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions

TL;DR

This paper introduces an interactive framework for multi-objective optimization that learns a decision maker's preferences over time to focus the search on preferred regions of the Pareto front, improving solution relevance.

Contribution

It develops a novel interactive approach that progressively learns value functions from user feedback to guide decomposition-based EMO algorithms toward preferred solutions.

Findings

Effective in identifying preferred solutions within the decision maker’s ROI.

Improves convergence to user-preferred regions in high-dimensional objectives.

Demonstrates robustness across benchmark problems with up to ten objectives.

Abstract

Decomposition has become an increasingly popular technique for evolutionary multi-objective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approximate a whole Pareto-optimal front (PF). However, in practice, the decision maker (DM) might only be interested in her/his region of interest (ROI), i.e., a part of the PF. Solutions outside that might be useless or even noisy to the decision-making procedure. Furthermore, there is no guarantee to find the preferred solutions when tackling many-objective problems. This paper develops an interactive framework for the decomposition-based EMO algorithm to lead a DM to the preferred solutions of her/his choice. It consists of three modules, i.e., consultation, preference elicitation and optimization. Specifically, after every several generations, the DM is asked to score a few candidate solutions in a consultation session. Thereafter, an approximated value function, which models the DM's preference information, is progressively learned from the DM's behavior. In the preference elicitation session, the preference information learned in the consultation module is translated into the form that can be used in a decomposition-based EMO algorithm, i.e., a set of reference points that are biased toward to the ROI. The optimization module, which can be any decomposition-based EMO algorithm in principle, utilizes the biased reference points to direct its search process. Extensive experiments on benchmark problems with three to ten objectives fully demonstrate the effectiveness of our proposed method for finding the DM's preferred solutions.