A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism
Aiming at the problems of low path success rate, easy precocious maturity, and easily falling into local extremums in the complex environment of path planning of mobile robots, this paper proposes a new particle swarm algorithm (RDS-PSO) based on restart strategy and adaptive dynamic adjustment mech...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/15/2339 |
| _version_ | 1797414278905462784 |
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| author | Lisang Liu Hui Xu Bin Wang Rongsheng Zhang Jionghui Chen |
| author_facet | Lisang Liu Hui Xu Bin Wang Rongsheng Zhang Jionghui Chen |
| author_sort | Lisang Liu |
| collection | DOAJ |
| description | Aiming at the problems of low path success rate, easy precocious maturity, and easily falling into local extremums in the complex environment of path planning of mobile robots, this paper proposes a new particle swarm algorithm (RDS-PSO) based on restart strategy and adaptive dynamic adjustment mechanism. When the population falls into local optimal or premature convergence, the restart strategy is activated to expand the search range by re-randomly initializing the group particles. An inverted S-type decreasing inertia weight and adaptive dynamic adjustment learning factor are proposed to balance the ability of local search and global search. Finally, the new RDS-PSO algorithm is combined with cubic spline interpolation to apply to the path planning and smoothing processing of mobile robots, and the coding mode based on the path node as a particle individual is constructed, and the penalty function is selected as the fitness function to solve the shortest collision-free path. The comparative results of simulation experiments show that the RDS-PSO algorithm proposed in this paper solves the problem of falling into local extremums and precocious puberty, significantly improves the optimization, speed, and effectiveness of the path, and the simulation experiments in different environments also show that the algorithm has good robustness and generalization. |
| first_indexed | 2024-03-09T05:30:45Z |
| format | Article |
| id | doaj.art-dc0a907c45ac485f8b1565d697bb15fe |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T05:30:45Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-dc0a907c45ac485f8b1565d697bb15fe2023-12-03T12:33:18ZengMDPI AGElectronics2079-92922022-07-011115233910.3390/electronics11152339A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic MechanismLisang Liu0Hui Xu1Bin Wang2Rongsheng Zhang3Jionghui Chen4School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaAiming at the problems of low path success rate, easy precocious maturity, and easily falling into local extremums in the complex environment of path planning of mobile robots, this paper proposes a new particle swarm algorithm (RDS-PSO) based on restart strategy and adaptive dynamic adjustment mechanism. When the population falls into local optimal or premature convergence, the restart strategy is activated to expand the search range by re-randomly initializing the group particles. An inverted S-type decreasing inertia weight and adaptive dynamic adjustment learning factor are proposed to balance the ability of local search and global search. Finally, the new RDS-PSO algorithm is combined with cubic spline interpolation to apply to the path planning and smoothing processing of mobile robots, and the coding mode based on the path node as a particle individual is constructed, and the penalty function is selected as the fitness function to solve the shortest collision-free path. The comparative results of simulation experiments show that the RDS-PSO algorithm proposed in this paper solves the problem of falling into local extremums and precocious puberty, significantly improves the optimization, speed, and effectiveness of the path, and the simulation experiments in different environments also show that the algorithm has good robustness and generalization.https://www.mdpi.com/2079-9292/11/15/2339restart strategyadaptive adjustmentparticle swarm optimizationspline interpolation |
| spellingShingle | Lisang Liu Hui Xu Bin Wang Rongsheng Zhang Jionghui Chen A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism Electronics restart strategy adaptive adjustment particle swarm optimization spline interpolation |
| title | A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism |
| title_full | A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism |
| title_fullStr | A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism |
| title_full_unstemmed | A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism |
| title_short | A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism |
| title_sort | study on particle swarm algorithm based on restart strategy and adaptive dynamic mechanism |
| topic | restart strategy adaptive adjustment particle swarm optimization spline interpolation |
| url | https://www.mdpi.com/2079-9292/11/15/2339 |
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