Volume 16 Issue 4
Aug.  2021
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JIANG P Y, LIU J, ZHOU Q, et al. Advances in meta-heuristic methods for large-scale black-box optimization problems[J]. Chinese Journal of Ship Research, 2021, 16(4): 1–18 doi: 10.19693/j.issn.1673-3185.02248
Citation: JIANG P Y, LIU J, ZHOU Q, et al. Advances in meta-heuristic methods for large-scale black-box optimization problems[J]. Chinese Journal of Ship Research, 2021, 16(4): 1–18 doi: 10.19693/j.issn.1673-3185.02248

Advances in meta-heuristic methods for large-scale black-box optimization problems

doi: 10.19693/j.issn.1673-3185.02248
  • Received Date: 2020-12-31
  • Rev Recd Date: 2021-03-29
  • Available Online: 2021-07-16
  • Publish Date: 2021-08-10
  • The optimal design of complex engineering equipment usually faces high-complexity, high-dimensional optimization problems – the so-called "large-scale black-box optimization problems (LBOPs)" – which are characterized by unavailable mathematical expressions of objective functions and/or constraint functions, and high dimensionality of design variables. The LBOPs have attracted the interest of scholars in various fields in recent years, and meta-heuristic algorithms are considered effective methods for solving these problems. This paper comprehensively summarizes recent research progress in meta-heuristic algorithms for solving LBOPs, including meta-heuristic algorithms with and without decomposition strategies, and meta-heuristic algorithms for handling computationally expensive large-scale optimization problems. Finally, possible future research directions of meta-heuristic methods for solving LBOPs are proposed.
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