A method for axis straightness error evaluation based on improved differential evolution algorithm

Luo, Jun; Liu, Zewei; Zhang, Ping; Liu, Xueming; Liu, Zheng

doi:10.1007/s00170-020-05732-w

Cited by 9 publications

(1 citation statement)

References 20 publications

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“…Zhang et al [31] applied an ant-colony algorithm (ACO) to straightness, but it easily fell into local optimal solutions, so Luo et al [32] applied an improved artificial bee-colony (ABC) algorithm to straightness error evaluation; however, there was still a lack of accuracy. Based on this, Luo [33] proposed to use an improved differential evolution algorithm (DE) for straightness evaluation. For roundness, Wen et al [34] proposed the use of a genetic algorithm (GA) for the evaluation of the minimum-zone circle, but the genetic algorithm requires the adjustment of numerous parameters.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Improved Harris Hawks Optimizer and Its Application to Form Deviation-Zone Evaluation

Liu

Sun

et al. 2023

Sensors

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Evaluation of the deviation zone based on discrete measured points is crucial for quality control in manufacturing and metrology. However, deviation-zone evaluation is a highly nonlinear problem that is difficult to solve using traditional numerical optimization methods. Swarm intelligence has many advantages in solving this problem: it produces gradient-free, high-quality solutions and is characterized by its ease of implementation. Therefore, this study applies an improved Harris hawks algorithm (HHO) to tackle the problem. The average fitness is applied to replace the random operator in the exploration phase to solve the problem of conflicting exploration strategies due to randomness. In addition, the salp swarm algorithm (SSA) with a nonlinear inertia weight is embedded into the HHO, such that the superior explorative ability of SSA can fill the gap in the exploration of HHO. Finally, the optimal solution is greedily selected between SSA-based individuals and HHO-based individuals. The effectiveness of the proposed improved HHO optimizer is checked through a comparison with other swarm intelligence methods in typical benchmark problems. Moreover, the experimental results of form deviation-zone evaluation on primitive geometries show that the improved method can accurately solve various form deviations, providing an effective general solution for primitive geometries in the manufacturing and metrology fields.

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Section: Introductionmentioning

confidence: 99%

An Efficient Improved Harris Hawks Optimizer and Its Application to Form Deviation-Zone Evaluation

Liu

Sun

et al. 2023

Sensors

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A Multi-strategy Enhanced Arithmetic Optimization Algorithm and Its Application in Path Planning of Mobile Robots

Deng,

He,

2024

Neural Process Lett

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A multi-strategy enhanced arithmetic optimization algorithm called MSEAOA is proposed to address the issues of low population diversity, imbalanced exploration and exploitation capabilities, and low accuracy of optimal solution in the Arithmetic Optimization Algorithm. Firstly, using the good point set strategy for population initialization to improve population diversity and thus accelerate convergence speed. Secondly, we integrate the exploration and exploition capabilities of differential self-learning strategy, best example learning strategy, and second-order differential perturbation strategy balancing algorithm. Finally, the introduction of somersault foraging strategy improves the accuracy of the optimal solution. We select 14 classical benchmark test functions and the CEC2019 function test set to test the optimization ability of MSEAOA, and apply MSEAOA to the path planning problem of mobile robots. MSEAOA is compared with other meta-heuristic optimization algorithms, and the experimental results are statistically analyzed by the Wilcoxon rank-sum test. The simulation experimental results show that MSEAOA performs the best among 14 benchmark functions, but for 10 CEC2019 functions, MSEAOA has the best optimization performance among 5 of them (50%). In the path optimization problem of mobile robots, the path obtained by MSEAOA is also the best among all algorithms, its path shortening rate exceeds 8.8% in 83% of environments. The results indicate that MSEAOA is a reliable algorithm suitable for function optimization and practical optimization problems.

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A Comprehensive Review on Computational Techniques for Form Error Evaluation

Pathak

Singh

2021

Arch Computat Methods Eng

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A method for axis straightness error evaluation based on improved differential evolution algorithm

Cited by 9 publications

References 20 publications

An Efficient Improved Harris Hawks Optimizer and Its Application to Form Deviation-Zone Evaluation

An Efficient Improved Harris Hawks Optimizer and Its Application to Form Deviation-Zone Evaluation

A Multi-strategy Enhanced Arithmetic Optimization Algorithm and Its Application in Path Planning of Mobile Robots

A Comprehensive Review on Computational Techniques for Form Error Evaluation

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