多模态多目标优化方法的研究现状比较
Multimodal Multi-objective Optimization: Comparative Study of the State-of-the-Art
摘要：
多模态多目标问题（MMOPs）通常出现在现实世界中的问题，在决策空间中的遥远的解决方案对应于非常相似的目标值。为了获得MMOPs的所有解，已经提出了许多多模态多目标进化算法（MMEAs）。目前，很少有研究涵盖了大多数最近提出的代表性的MMEAs，并进行了比较。在本研究中，我们首先回顾了近二十年来的相关工作。然后，我们选择了12个国家的最先进的算法，利用不同的多样性保持技术，并比较他们的性能在现有的测试套件。实验结果表明，不同的技术对不同类型的MMOPs的优势和劣势，从而提供指导，如何选择/设计MMEAs在特定的场景。
英文摘要：
Multimodal multi-objective problems (MMOPs) commonly arise in real-world problems where distant solutions in decision space correspond to very similar objective values. To obtain all solutions for MMOPs, many multimodal multi-objective evolutionary algorithms (MMEAs) have been proposed. For now, few studies have encompassed most of the recently proposed representative MMEAs and made a comparative comparison. In this study, we first review the related works during the last two decades. Then, we choose 12 state-of-the-art algorithms that utilize different diversity-maintaining techniques and compared their performance on existing test suites. Experimental results indicate the strengths and weaknesses of different techniques on different types of MMOPs, thus providing guidance on how to select/design MMEAs in specific scenarios.
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