Blood-sucking leech optimizer

•Developed a novel Blood-Sucking Leech Optimizer (BSLO) inspired by blood-sucking leeches.•Evaluated the performance of BSLO using numerical experiments and six engineering problems.•Compared BSLO with ten meta-heuristic algorithms.•Validated the strong robustness and optimization efficiency of BSLO...

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Vydané v:Advances in engineering software (1992) Ročník 195; s. 103696
Hlavní autori: Bai, Jianfu, Nguyen-Xuan, H., Atroshchenko, Elena, Kosec, Gregor, Wang, Lihua, Abdel Wahab, Magd
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.09.2024
Predmet:
ANN
Benchmark function
Blood-sucking leech optimizer (BSLO)
Engineering design problem
Melt electrospinning writing
Meta-heuristic
Blood-sucking leech optimizer (BSLO)
Engineering design problem
ANN
Benchmark function
Meta-heuristic
Melt electrospinning writing
ISSN:0965-9978
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Popis
Shrnutí:•Developed a novel Blood-Sucking Leech Optimizer (BSLO) inspired by blood-sucking leeches.•Evaluated the performance of BSLO using numerical experiments and six engineering problems.•Compared BSLO with ten meta-heuristic algorithms.•Validated the strong robustness and optimization efficiency of BSLO.•Demonstrated superiority of BSLO for diameter prediction of melt electrospinning writing fiber. In this paper, a new meta-heuristic optimization algorithm motivated by the foraging behaviour of blood-sucking leeches in rice fields is presented, named Blood-Sucking Leech Optimizer (BSLO). BSLO is modelled by five hunting strategies, which are the exploration of directional leeches, exploitation of directional leeches, switching mechanism of directional leeches, search strategy of directionless leeches, and re-tracking strategy. BSLO and ten comparative meta-heuristic optimization algorithms are used for optimizing twenty-three classical benchmark functions, CEC 2017, and CEC 2019. The strong robustness and optimization efficiency of BSLO are confirmed via four qualitative analyses, two statistical tests and convergence curves. Furthermore, the superiority of BSLO for real-world problems under constraints is demonstrated using five classical engineering problems. Finally, a BSLO-based Artificial Neural Network (ANN) predictive model for diameter prediction of melt electrospinning writing fibre is proposed, which further verifies BSLO's applicability for real-world problems. Therefore, BSLO is a potential optimizer for optimizing various problems. Source codes of BSLO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/163106-blood-sucking-leech-optimizer.
ISSN:0965-9978
DOI:10.1016/j.advengsoft.2024.103696