Exploring Speed-forward-looking Particle Swarm-taboo Search Optimization Algorithm by Introducing Path Angle

LI Min; YOU Dazhang; XU Wenjun

doi:10.13433/j.cnki.1003-8728.20200585

Volume 42 Issue 1

Jan. 2023

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2023 > 42(1): 119-124

LI Min, YOU Dazhang, XU Wenjun. Exploring Speed-forward-looking Particle Swarm-taboo Search Optimization Algorithm by Introducing Path Angle[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 119-124. doi: 10.13433/j.cnki.1003-8728.20200585

Citation:

LI Min, YOU Dazhang, XU Wenjun. Exploring Speed-forward-looking Particle Swarm-taboo Search Optimization Algorithm by Introducing Path Angle[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 119-124. doi: 10.13433/j.cnki.1003-8728.20200585

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Exploring Speed-forward-looking Particle Swarm-taboo Search Optimization Algorithm by Introducing Path Angle

doi: 10.13433/j.cnki.1003-8728.20200585

School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China

Received Date: 2021-03-08
Publish Date: 2023-01-25

Abstract

Abstract

This paper proposes a multi-track and speed-forward-looking algorithm that uses path angles to optimize the path in a CNC system that has continuous micro-line segments. Firstly, it calculates the connection speed based on the constraint relationship between front and back paths. Secondly, it uses the path angle to map the connection speed and the maximum jerk vector function. Finally, it introduces the obtained acceleration and deceleration parameters into the S-shaped acceleration and deceleration model to obtain the asymmetric S-shaped acceleration and deceleration data. The particle swarm-taboo search hybrid algorithm is used to optimize the adjustment coefficient of the introduced path angle. The weight of the fitness function synthesis index is calculated with the entropy weight method, and the global optimal solution is obtained. The results show that compared with the traditional S-type acceleration and deceleration model, the algorithm proposed in this paper significantly improves the processing efficiency, has higher flexibility and greatly improves the processing accuracy.
- particle swarm-taboo search hybrid algorithm,
- entropy weight method,
- S-shaped acceleration and deceleration,
- forward planning,
- path angle

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References(15)

References

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