Design Optimization of a Speed Reducer Using Deterministic Techniques

Lin, Chia-Chen; Tsai, Jung‐Fa; Hu, Nian-Ze; Chang, Shu-Chuan

doi:10.1155/2013/419043

Cited by 26 publications

(6 citation statements)

References 31 publications

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“…In order to verify the problem-solving ability of ODBO, in this section, the ODBO algorithm will be applied to two engineering applications: speed reducer design [29] and compression spring design [30], and the results of the runs will be compared with those of the five other algorithms.…”

Section: Engineering Applicationsmentioning

confidence: 99%

An Improved Dung Beetle Optimization Algorithm for High-Dimension Optimization and Its Engineering Applications

Wang,

Kang,

Shen

et al. 2024

Symmetry

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One of the limitations of the dung beetle optimization (DBO) is its susceptibility to local optima and its relatively low search accuracy. Several strategies have been utilized to improve the diversity, search precision, and outcomes of the DBO. However, the equilibrium between exploration and exploitation has not been achieved optimally. This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory. In addition, in order to enhance the performance of the dung ball rolling phase, this paper combines the global search strategy of the osprey optimization algorithm with the position update strategy of the DBO. Additionally, we enhance the population’s diversity during the foraging phase of the DBO by incorporating vertical and horizontal crossover of individuals. This introduction of asymmetry in the crossover operation increases the exploration capability of the algorithm, allowing it to effectively escape local optima and facilitate global search.

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Section: Engineering Applicationsmentioning

confidence: 99%

An Improved Dung Beetle Optimization Algorithm for High-Dimension Optimization and Its Engineering Applications

Wang,

Kang,

Shen

et al. 2024

Symmetry

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“…The goal is to enable each component to rotate at the most effective speed. Several studies have attempted to tackle this problem, such as the work by Lin, et al [45] which builds upon previous research on speed reducer design optimization. In addition to convexification strategies and piecewise linearization techniques used in previous studies.…”

Section: Speed Reducer Gearboxmentioning

confidence: 99%

Design a speed reducer gearbox using chameleon swarm algorithm

Al-Shabi

Obaideen

Amin

et al. 2023

Unmanned Systems Technology XXV

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In this paper, Chameleon Swarm Algorithm, which is a new metaheuristic algorithm, is used to design a speed reducer gearbox. The gearbox is used in an autonomous vehicle, and it is supposed to take in consideration the total weight of the gear sets along with other contains that include the stresses; including the bending and surface stresses for the gears and the stresses of the shaft, and he deflections of the shafts, beside other contains. The algorithm must find the optimal solution while satisfying the eleven constraints, which makes it a multi-objective optimization problem. The algorithm minimizes the energy dissipation in the gears as the design is optimal. The algorithm will be tested in Monte Carlo simulation to show its stability and robustness in such an application. The computer resources are examined, and the results are compared to the particle swarm optimization. The proposed method shows better results and better performance compared to the PSO.

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“…In speed reducer design problem [19,24,21] shown in Fig. 7, the weight of speed reducer is to be minimized subject to constraints on bending stress of the gear teeth, surface stress, transverse deflections of the shafts, and stresses in the shafts.…”

Section: The Speed Reducer Designmentioning

confidence: 99%

A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems

Abdel-Baset¹,

Hezam²

2016

IJCA

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Flower pollination algorithm (FP) is a new nature-inspired algorithm, based on the characteristics of flowering plants.Combining with the features of flower pollination algorithm, an improved simulated annealing algorithm is proposed in this paper (FPSA). It can improve the speed of annealing. The initial state of simulated annealing and new solutions are generated by flower pollination. Therefore, it has the advantage of high quality and efficiency. The method combines the standard flower pollination algorithm (FP) with simulated annealing to enhance the search performance and speeds up the global convergence rate. Structural engineering optimization problems are presented to demonstrate the effectiveness and robustness of the proposed algorithm. The experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed algorithm is competitive to those achieved by the existing algorithms.

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Design Optimization of a Speed Reducer Using Deterministic Techniques

Cited by 26 publications

References 31 publications

An Improved Dung Beetle Optimization Algorithm for High-Dimension Optimization and Its Engineering Applications

An Improved Dung Beetle Optimization Algorithm for High-Dimension Optimization and Its Engineering Applications

Design a speed reducer gearbox using chameleon swarm algorithm

A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems

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