Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis

Yu, H; Li, W; Chen, C; Liang, J; Gui, W; Wang, M; Chen, H

Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis

Yu, H Li, W Chen, C Liang, J

Gui, W Wang, M Chen, H

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Engineering with Computers, 2020, pp. 1-29
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Yu, H
dc.contributor.author	Li, W
dc.contributor.author	Chen, C
dc.contributor.author	Liang, J https://orcid.org/0000-0001-7179-5208
dc.contributor.author	Gui, W
dc.contributor.author	Wang, M
dc.contributor.author	Chen, H
dc.date.accessioned	2021-06-09T06:33:25Z
dc.date.available	2021-06-09T06:33:25Z
dc.date.issued	2020-01-01
dc.identifier.citation	Engineering with Computers, 2020, pp. 1-29
dc.identifier.issn	0177-0667
dc.identifier.issn	1435-5663
dc.identifier.uri	http://hdl.handle.net/10453/149473
dc.description.abstract	The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Engineering with Computers
dc.relation.isbasedon	10.1007/s00366-020-01174-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics
dc.subject.classification	Design Practice & Management
dc.title	Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis
dc.type	Journal Article
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0802 Computation Theory and Mathematics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-06-09T06:33:23Z
pubs.publication-status	Published

Abstract:

The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.

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http://hdl.handle.net/10453/149473