Design Optimization of a Spur Gear Train Using TLBO and ETLBO Algorithms

Rao, R. Venkata

doi:10.1007/978-3-319-22732-0_6

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Meta-heuristic algorithms are a class of optimization algorithms that are inspired by physical phenomena or biological behaviors in nature, such as MFO [11], SOS [12], GA [13], PSO [14], FA [15], BA [16], GWO [17], etc. They are widely used in machine learning [18]- [21], data mining [22], [23], engineering design [24]- [26], industrial control [27], [28], and power systems [29] due to superior optimization performance. The swarm intelligence optimization algorithm is a typical representative in the meta-heuristic algorithm, and these algorithms are simple, flexible, easy to implement but not easy to fall into local optimum.…”

Section: Introductionmentioning

confidence: 99%

A New DBSCAN Parameters Determination Method Based on Improved MVO

Lai

Zhou

et al. 2019

IEEE Access

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Density-based spatial clustering of applications with noise (DBSCAN) is a typical kind of algorithm based on density clustering in unsupervised learning. It can cluster data of arbitrary shape and also identify noise samples in the dataset. However, an unavoidable defect of the DBSCAN algorithm exists since the clustering performance is quite sensitive to the parameter settings of MinPts and Eps, and there is no theory to guide the setting of its parameters. Therefore, a new method is proposed to optimize the DBSCAN parameters in this paper. Multi-verse optimizer algorithm, a special variable updating method with excellent optimization performance, is selected and improved for optimizing the parameters of DBSCAN, which not only can quickly find out the highest clustering accuracy of DBSCAN, but also find the interval of Eps corresponding to the highest accuracy. In order to search the range of Eps more quickly and efficiently, we design a new mechanism for the variable update of MVO. The experimental results show that the improved MVO is used to optimize DBSCAN, which not only can quickly find out its highest clustering accuracy but also can search the parameters of MinPts and Eps corresponding to the highest clustering accuracy efficiently. INDEX TERMS Improved MVO, DBSCAN, parameter optimization, unsupervised learning.

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Section: Introductionmentioning

confidence: 99%

A New DBSCAN Parameters Determination Method Based on Improved MVO

Lai

Zhou

et al. 2019

IEEE Access

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Multi-objective optimization design of spur gear based on NSGA-II and decision making

Qi-zhi

2019

Advances in Mechanical Engineering

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Optimization design of spur gear is a complicated work because the performance characteristics depend on different types of decision variables and objectives. Traditional single-objective optimization design of the spur gear always results in poor outcomes relative to other objectives due to objectives' competition with each other. Therefore, this study works on the spur gear design based on the multi-objective optimization model of elitist non-dominated sorting genetic algorithm (NSGA-II). In the model, gear module, teeth number, and transmission ratio are decision variables, while center distance, bearing capacity coefficient, and meshing efficiency are objectives. Final optimal solutions are picked out from Pareto frontier calculated from NSGA-II using the decision makers of Shannon Entropy, linear programming technique for multidimensional analysis of preference (LINMAP), and technique of order preference by similarity to an ideal solution (TOPSIS). Meanwhile, a deviation index is used to evaluate the reasonable status of the optimal solutions. From triple-objective and dual-objective optimization results, it is found that the optimal solution selected from LINMAP decision maker shows a relatively small deviation index. It indicates that LINMAP decision maker may yield better optimal solution. This study could provide some beneficial information for spur design.

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Design Optimization of a Spur Gear Train Using TLBO and ETLBO Algorithms

Cited by 2 publications

References 19 publications

A New DBSCAN Parameters Determination Method Based on Improved MVO

A New DBSCAN Parameters Determination Method Based on Improved MVO

Multi-objective optimization design of spur gear based on NSGA-II and decision making

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