Multiobjective evolutionary algorithms (MOEAs) have witnessed prosperity in solving many-objective optimization problems (MaOPs) over the past three decades. Unfortunately, no one single MOEA equipped with given parameter settings, mating-variation operator, and environmental selection mechanism is suitable for obtaining a set of solutions with excellent convergence and diversity for various types of MaOPs. The reality is that different MOEAs show great differences in handling certain types of MaOPs. Aiming at these characteristics, this paper proposes a flexible ensemble framework, namely, ASES, which is highly scalable for embedding any number of MOEAs to promote their advantages. To alleviate the undesirable phenomenon that some promising solutions are discarded during the evolution process, a big archive that number of contained solutions be far larger than population size is integrated into this ensemble framework to record large-scale nondominated solutions, and also an efficient maintenance strategy is developed to update the archive. Furthermore, the knowledge coming from updating archive is exploited to guide the evolutionary process for different MOEAs, allocating limited computational resources for efficient algorithms. A large number of numerical experimental studies demonstrated superior performance of the proposed ASES. Among 52 test instances, the ASES performs better than all the six baseline algorithms on at least half of the test instances with respect to both metrics hypervolume and inverted generational distance.

中文翻译：

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多目标进化算法的大型存档辅助集成

在过去的三十年中，多目标进化算法（MOEA）见证了解决多目标优化问题（MaOP）的繁荣。不幸的是，没有任何一个配备给定参数设置，匹配变量算子和环境选择机制的MOEA适于获得针对各种类型MaOP的具有出色收敛性和多样性的一组解决方案。现实情况是，不同的MOEA在处理某些类型的MaOP方面显示出很大的差异。针对这些特征，本文提出了一种灵活的集成框架，即ASES，该框架具有很高的可扩展性，可以嵌入任何数量的MOEA以提升其优势。为了缓解不良现象，即在进化过程中会丢弃一些有希望的解决方案，该集成框架中集成了包含解决方案的数量远大于总体数量的大型档案，以记录大型非控制解决方案，并且还开发了有效的维护策略来更新档案。此外，来自更新档案的知识被用来指导不同MOEA的进化过程，为有效算法分配有限的计算资源。大量的数值实验研究证明了拟议的ASES的优越性能。在52个测试实例中，就指标超量和反向世代距离而言，在至少一半的测试实例中，ASES的性能优于所有六个基线算法。