Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.

中文翻译：

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进化大规模多目标优化：调查

多目标进化算法（MOEA）在解决各种优化问题方面表现出了良好的性能，但在处理包含大量决策变量的问题时，它们的性能可能会急剧下降。近年来，人们致力于解决大规模多目标优化问题带来的挑战。本文对用于解决大规模多目标优化问题的最新 MOEA 进行了全面调查。我们首先将这些 MOEA 分类为基于决策变量分组、基于决策空间缩减和基于新搜索策略的 MOEA，并讨论它们的优缺点。然后，我们回顾了性能评估的基准问题以及 MOEA 在大规模多目标优化中的一些重要和新兴应用。最后，我们讨论了进化大规模多目标优化的一些剩余挑战和未来的研究方向。