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A new decomposition-based evolutionary framework for many-objective optimization

机译：一个新的基于分解的多目标优化进化框架

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摘要

A new class of Multi-Objective Evolutionary Algorithms (MOEAs) has emerged recently that uses the concept of decomposition to overcome the challenges faced by the current state-of-the-art MOEAs in undertaking optimization problems with more than three objectives. This new class of MOEAs employs a set of reference points to decompose the objective space into multiple scalar problems and to generate the target reference vectors for the solutions to sustain their diversity at every stage of the evolutionary process. In this study, we propose a novel framework for this class of MOEAs with a restricted mating selection scheme, with the aim to further improve the quality of the solutions close to the target reference vectors. The proposed framework is evaluated and compared with the current popular reference vector-based MOEAs to demonstrate its effectiveness. Using the Inverted Generation Distance (IGD) as the quality indicator, the experimental results indicate the superiority of the proposed framework when it is coupled with the MOEAs in solving 3- to 10-continuous objective functions in many-objective optimization problems.
机译：最近出现了一类新的多目标进化算法（MOEA），它使用分解的概念来克服当前最先进的MOEA在解决具有三个以上目标的优化问题时所面临的挑战。这类新的MOEA使用一组参考点将目标空间分解为多个标量问题，并为解决方案生成目标参考向量，以在进化过程的每个阶段维持其多样性。在这项研究中，我们提出了一种针对此类MOEA的新型框架，该框架具有有限的交配选择方案，旨在进一步提高接近目标参考向量的解的质量。对提出的框架进行了评估，并与当前流行的基于参考向量的MOEA进行了比较，以证明其有效性。实验结果以倒数生成距离（IGD）为质量指标，表明该框架与MOEA结合在求解多目标优化问题中的3至10个连续目标函数时具有优越性。

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