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Large-scale evolutionary optimization: A review and comparative study☆
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DOI：10.1016/j.swevo.2023.101466.png)
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摘要
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Large-scale global optimization (LSGO) problems have widely appeared in various real -world applications. However, their inherent complexity, coupled with the curse of dimensionality, makes them challenging to solve. Continuous efforts have been devoted to designing computational intelligence-based approaches to solve them. This paper offers a comprehensive review of the latest developments in the field, focusing on the advances in both single -objective and multi -objective large-scale evolutionary optimization algorithms over the past five years. We systematically categorize these algorithms, discuss their distinct features, and highlight benchmark test suites essential for performance evaluation. After that, comparative studies are conducted using numerical solutions to evaluate the performance of state -of -the -art LSGO for both single -objective and multiobjective problems. Finally, we discuss the real -world applications of LSGO, some challenges, and possible future research directions.
Keyword:
Large-scale optimization
Evolutionary optimization
Multi-objective optimization
Sparse optimization
High-dimensional problems
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