For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicator-based MOEA (IB-MOEA) has specific search preferences related to its baseline QI, producing Pareto front approximations with different properties. In consequence, an IB-MOEA based on a single QI has a limited scope of multiobjective optimization problems (MOPs) in which it is expected to have a good performance. This issue is emphasized when the associated Pareto front geometries are highly irregular. In order to overcome these issues, we propose here an island-based multiindicator algorithm (IMIA) that takes advantage of the search biases of multiple IB-MOEAs through a cooperative scheme. Our experimental results show that the cooperation of multiple IB-MOEAs allows IMIA to perform more robustly (considering several QIs) than the panmictic versions of its baseline IB-MOEAs as well as several state-of-the-art MOEAs. Additionally, IMIA shows a Pareto-front-shape invariance property, which makes it a remarkable optimizer when tackling MOPs with complex Pareto front geometries.

中文翻译：

---

基于指标的多目标进化算法的协作效果

近 20 年来，质量指标 (QI) 推动了多目标进化算法 (MOEA) 新选择机制的设计。每个基于指标的 MOEA (IB-MOEA) 都有与其基线 QI 相关的特定搜索偏好，从而产生具有不同属性的帕累托前沿近似值。因此，基于单个 QI 的 IB-MOEA 具有有限的多目标优化问题 (MOP) 范围，在这些问题中它有望具有良好的性能。当相关的帕累托前沿几何形状高度不规则时，这个问题就会被强调。为了克服这些问题，我们在这里提出了一种基于岛的多指标算法（IMIA），该算法通过合作方案利用了多个 IB-MOEA 的搜索偏差。我们的实验结果表明，多个 IB-MOEAs 的合作使 IMIA 比其基线 IB-MOEAs 的 panmictic 版本以及几个最先进的 MOEAs 更稳健地执行（考虑几个 QI）。此外，IMIA 显示了帕累托前沿形状不变性，这使其在处理具有复杂帕累托前沿几何形状的 MOP 时成为卓越的优化器。