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. 2022;38(Suppl 5):4387-4413.
doi: 10.1007/s00366-021-01442-3. Epub 2021 Aug 3.

Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

Ahmad Abbasi  1 , Behnam Firouzi  1 , Polat Sendur  1 , Ali Asghar Heidari  2 , Huiling Chen  3 , Rajiv Tiwari  4
Affiliations

Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

Ahmad Abbasi et al. Eng Comput. 2022.

Abstract

Bearing is one of the most fundamental components of rotary machinery, and its fatigue life is a crucial factor in designing. The design optimization of tapered roller bearing (TRB) is a complex design problem because various arrays of designing parameters and functional requirements should be fulfilled. Since there are many design variables and nonlinear constraints, presenting an optimal design of TRBs poses some challenges for metaheuristic algorithms. The Harris hawks optimization (HHO) algorithm is a robust nature-inspired method with unique exploitation and exploration phases due to its time-varying structure. However, this metaheuristic algorithm may still converge to local optima for more challenging problems such as the design of TRBs. Therefore, this study aims to improve the accuracy and efficiency of the shortcomings of this algorithm. The performance of the proposed algorithm is first evaluated for the TRB optimization problem. The TRB optimization design has nine design variables and 26 constraints because of geometrical dimensions and strength conditions. The productivity of the proposed method is compared with diverse metaheuristic algorithms in the literature. The results demonstrate the significant development of dynamic load capacity in comparison to the standard value. Furthermore, the enhanced version of the HHO algorithm presented in this study is benchmarked with various well-known engineering problems. For supplementary materials regarding algorithms in this research, readers can refer to https://aliasgharheidari.com.

Keywords: Constrained optimization; Fatigue life; Harris hawks optimization; Optimization; Swarm-intelligence algorithms; Tapered roller bearing.

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Figures

Fig. 1
Fig. 1
Structure of TRB
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Fig. 2
Internal dimensions of tapered roller bearing
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Schematic view of roller’s force
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Energy factor
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The flowchart of the HHO algorithm
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Fig. 6
Gaussian function for different values of standard deviation
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Fig. 7
The flowchart of the EHHO algorithm
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Fig. 8
The convergence curve for different bearing numbers a 30204, b 30205, c 32205, and d 322/28
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Fig. 9
Comparison of design variables: standard catalog, GA, and EHHO
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Fig. 10
Convergence curve for ten algorithms. a 32207, b 30307 c 32208
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Fig. 11
Cantilever beam design
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Fig. 12
Speed reducer design problem
See this image and copyright information in PMC

References

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