Structural bias (SB) refers to systematic preferences of an optimisation algorithm for particular regions of the search space that arise independently of the objective function. While SB has been studied extensively in single-objective optimisation, its role in multi-objective optimisation remains largely unexplored. This is problematic, as dominance relations, diversity preservation and Pareto-based selection mechanisms may introduce or amplify structural effects. In this paper, we extend the concept of structural bias to the multi-objective setting and propose a methodology to study it in isolation from fitness-driven guidance. We introduce a suite of synthetic multi-objective test problems with analytically controlled Pareto fronts and deliberately uninformative objective values. These problems are designed to decouple algorithmic behaviour from problem structure, allowing bias induced purely by algorithmic operators and design choices to be observed. The test suite covers a range of Pareto front shapes, densities and noise levels, enabling systematic analysis of different manifestations of structural bias. We discuss methodological challenges specific to the multi-objective case and outline how existing SB detection approaches can be adapted. This work provides a first step towards behaviour-based benchmarking of multi-objective optimisers, complementing performance-based evaluation and informing more robust algorithm design.

翻译：结构偏差（SB）指优化算法独立于目标函数而产生的对搜索空间特定区域的系统性偏好。尽管结构偏差在单目标优化中已得到广泛研究，其在多目标优化中的作用仍基本未被探索。这是有问题的，因为支配关系、多样性保持和基于帕累托的选择机制可能引入或放大结构效应。本文中，我们将结构偏差的概念扩展到多目标场景，并提出一种将其与适应度驱动指导相隔离的研究方法。我们引入了一套合成的多目标测试问题，这些问题具有解析可控的帕累托前沿和刻意设计的无信息目标值。这些问题的设计旨在将算法行为与问题结构解耦，从而能够观察到纯粹由算法算子和设计选择引起的偏差。该测试套件涵盖了一系列帕累托前沿形状、密度和噪声水平，使得能够系统分析结构偏差的不同表现形式。我们讨论了多目标情况下特有的方法论挑战，并概述了如何调整现有的SB检测方法。这项工作为基于行为的多目标优化器基准测试迈出了第一步，补充了基于性能的评估，并为更稳健的算法设计提供了信息。