A Novel Fruit Fly Optimization Algorithm with Evolution Strategy for Magnetotelluric Data Inversion
Bin Yin,
Jie Yang,
Yue Li and
Niansheng Tang
Journal of Mathematics, 2023, vol. 2023, 1-13
Abstract:
As a novel metaheuristic algorithm, fruit fly optimization algorithm (FOA) can effectively deal with the inversion problem of one-dimensional magnetotelluric data. However, FOA still has the disadvantage of premature convergence and falling into local extreme value. Therefore, based on standard FOA, we improve the FOA algorithm by introducing evolutionary strategies. Firstly, crossover and mutation strategies are introduced to improve the updating process of FOA population individuals. Secondly, by improving the variation scale factor, the global search and local search capabilities of the algorithm are balanced, and these improvements can accelerate the algorithm convergence. The improved algorithm is compared with other algorithms. After the benchmark function test, the improved algorithm has better optimization ability. Finally, the MT theoretical model and field data are used to test that the evolutionary strategy can effectively improve the convergence speed of the algorithm, and the inversion accuracy of the new algorithm is greatly improved.
Date: 2023
References: Add references at CitEc
Citations:
Downloads: (external link)
http://downloads.hindawi.com/journals/jmath/2023/8810401.pdf (application/pdf)
http://downloads.hindawi.com/journals/jmath/2023/8810401.xml (application/xml)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:8810401
DOI: 10.1155/2023/8810401
Access Statistics for this article
More articles in Journal of Mathematics from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().