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Recent advances in multi-objective grey wolf optimizer, its versions and applications.
- Source :
- Neural Computing & Applications; Nov2022, Vol. 34 Issue 22, p19723-19749, 27p
- Publication Year :
- 2022
-
Abstract
- In this work, a comprehensive review of the multi-objective grey wolf optimizer (MOGWO) is provided. In multi-objective optimization (MO), more than one objective function must be considered at the same time. To deal with such problems, a priori or a posteriori MOGWO variants have been proposed in the literature. In the a priori model, the multi-objective functions are aggregated into a single objective function by a number of weights. In the posterior model, the multi-objective formulation is maintained and MOGWO is employed to estimate the Pareto optimal solutions representing the best trade-offs between the objectives. Due to the successful performance of MOGWO, it has been widely utilized for MO. This review covers the research growth of MOGWO in terms of a number of researches, topics, top researchers, etc. Furthermore, several versions of MOGWO have been introduced and reviewed with applications in diverse fields. This work also provides a critical analysis to show the shortcomings and limitations of using the basic version of MOGWO followed by several future directions. This review paper will be a base paper for any researcher interested to implement MOGWO in its work. [ABSTRACT FROM AUTHOR]
- Subjects :
- WOLVES
PARETO optimum
CRITICAL analysis
LITERATURE reviews
Subjects
Details
- Language :
- English
- ISSN :
- 09410643
- Volume :
- 34
- Issue :
- 22
- Database :
- Complementary Index
- Journal :
- Neural Computing & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 159792944
- Full Text :
- https://doi.org/10.1007/s00521-022-07704-5