Evolutionary Large-Scale Multi-Objective Optimization: A Survey
By Ye Tian, Langchun Si, Xing-yi Zhang, Ran Cheng, Cheng He, Kay Chen Tan, Yaochu Jin. ACM Computing Surveys, 2021
426 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/7c7816661591c1be14625f72da8b7a794667423d
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In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- A Survey on Evolutionary Constrained Multiobjective Optimization (2023, background): “To address these problems, the mechanisms [194] customized for large-scale problems can be embedded into the existing CMOEAs framework.”