Multi-objective design optimization for a two-stage transmission system under heavy load condition

Fu, Guo‐Zhong; Huang, Hong‐Zhong; Li, Yanfeng; Zheng, Bo; Jin, Tongdan

doi:10.1016/j.mechmachtheory.2017.12.024

Cited by 13 publications

(4 citation statements)

References 25 publications

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“…e optimization cannot be selected by simply comparing the values. e optimized solution of MOPs is a set of mutually compromised solutions, i.e., Pareto optimal solution set (PS) [1][2][3][4][5]. e performance metrics of the PS are diversity and convergence.…”

Section: Introductionmentioning

confidence: 99%

A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy

et al. 2021

Mathematical Problems in Engineering

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Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is the seminal framework of multiobjective evolutionary algorithms (MOEAs). To alleviate the nonuniformly distributed solutions generated by a fixed set of evenly distributed weight vectors in the presence of nonconvex and disconnected problems, an adaptive vector generation mechanism is proposed. A coevolution strategy and a vector generator are synergistically cooperated to remedy the weight vectors. Optimal weight vectors are generated to replace the useless weight vectors to assure that optimal solutions are distributed evenly. Experiment results indicate that this mechanism is efficient in improving the diversity of MOEA/D.

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Section: Introductionmentioning

confidence: 99%

A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy

et al. 2021

Mathematical Problems in Engineering

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“…29–31 Considering the versatility of calculation models and analysis methods, several approaches 32–35 were proposed to improve the dynamic behaviour, such as spline optimization. Multi-objective optimization (MOO) methods 36–39 were applied to optimize some transmission mechanisms, such as cam mechanisms and gear mechanisms. Some methods based on intelligent algorithms, such as NSGA-II and SPEA2, 40 , 41 are also effective in solving the MOO problem.…”

Section: Introductionmentioning

confidence: 99%

Minimum contact stress modification for profile curve design defects in the beam-spring-cone docking mechanism

Lan

Fan

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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A minimum contact stress modification method for profile curve design defects in a beam-spring-cone docking mechanism (BSCDM) based on genetic algorithm is presented in this paper, the profile curve and contact position of BSCDM are optimized. Under low-speed conditions, an improved elastic contact model of semi-space elastic bodies is established to modify and optimize the elliptic profile envelope curve based on Hertz contact theory and two kinds of complete elliptic integral, the improved contact model is used to solve elastic contact problems with the geometric characteristics of the ellipse surface, the optimal profile curve of the docking joint and the optimal docking contact point position are obtained. The results of numerical simulation and the experiment demonstrate the feasibility and validity of above models and methods.

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“…The crow search algorithm (CSA) as a swarm intelligence optimization algorithm is derived from a series of intelligent behaviors of crows (the smartest birds in nature). Different from the traditional PSO algorithm, the CSA algorithm introduces an anti‐tracking mechanism, which significantly reduces the probability of falling into local optimum when dealing with multipeak problems . The proposed CSA has been applied to optimization problems in many different fields, such as the image segmentation and diagnosis of Parkinson's disease, which have achieved satisfactory optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Finite element model updating using crow search algorithm with Levy flight

et al. 2020

Numerical Meth Engineering

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Summary The accuracy of the finite element model is essential for fatigue analysis and vibration of a flexible body. This paper presents a finite element model updating (FEMU) algorithm based on crows search algorithm incorporated with Levy flight (LFCSA). The proposed algorithm is tested on the updating of two different cases: a simple structure (beam) and a complex structure (gearbox housing). To verify the performance of LFCSA, two optimization algorithm, which is the standard CSA and the particle swarm optimization algorithm with Levy flight (LFPSO), are applied to FEMU of the simple beam. The result of the comparison shows that the LFCSA has sufficient convergence speed and global search ability in the implementation of FEMU. In the case of gearbox housing, the error of frequencies and mode shapes are analyzed to indicate that the LFCSA gives a satisfactory result in a complex structure.

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Multi-objective design optimization for a two-stage transmission system under heavy load condition

Cited by 13 publications

References 25 publications

A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy

A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy

Minimum contact stress modification for profile curve design defects in the beam-spring-cone docking mechanism

Finite element model updating using crow search algorithm with Levy flight

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