Dynamic Detection of Relevant Objectives and Adaptation to Preference Drifts in Interactive Evolutionary Multi-Objective Optimization

TL;DR

This paper presents a dynamic approach for detecting relevant objectives and adapting to shifting decision-maker preferences in interactive evolutionary multi-objective optimization, improving solution quality amid evolving preferences.

Contribution

It introduces methods to detect and adapt to changing DM preferences and safeguard relevant objectives, addressing a key limitation in existing interactive EMOAs.

Findings

Effectively manages evolving preferences in EMOAs

Enhances solution quality and relevance

Safeguards objectives from local optima

Abstract

Evolutionary Multi-Objective Optimization Algorithms (EMOAs) are widely employed to tackle problems with multiple conflicting objectives. Recent research indicates that not all objectives are equally important to the decision-maker (DM). In the context of interactive EMOAs, preference information elicited from the DM during the optimization process can be leveraged to identify and discard irrelevant objectives, a crucial step when objective evaluations are computationally expensive. However, much of the existing literature fails to account for the dynamic nature of DM preferences, which can evolve throughout the decision-making process and affect the relevance of objectives. This study addresses this limitation by simulating dynamic shifts in DM preferences within a ranking-based interactive algorithm. Additionally, we propose methods to discard outdated or conflicting preferences when such shifts occur. Building on prior research, we also introduce a mechanism to safeguard relevant objectives that may become trapped in local or global optima due to the diminished correlation with the DM-provided rankings. Our experimental results demonstrate that the proposed methods effectively manage evolving preferences and significantly enhance the quality and desirability of the solutions produced by the algorithm.