Legendre Cooperative PSO Strategies for Trajectory Optimization
Particle swarm optimization (PSO) is a population-based stochastic optimization technique in a smooth search space. However, in a category of trajectory optimization problem with arbitrary final time and multiple control variables, the smoothness of variables cannot be satisfied since the linear int...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/5036791 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850174324867072000 |
|---|---|
| author | Lei Liu Yongji Wang Fuqiang Xie Jiashi Gao |
| author_facet | Lei Liu Yongji Wang Fuqiang Xie Jiashi Gao |
| author_sort | Lei Liu |
| collection | DOAJ |
| description | Particle swarm optimization (PSO) is a population-based stochastic optimization technique in a smooth search space. However, in a category of trajectory optimization problem with arbitrary final time and multiple control variables, the smoothness of variables cannot be satisfied since the linear interpolation is widely used. In the paper, a novel Legendre cooperative PSO (LCPSO) is proposed by introducing Legendre orthogonal polynomials instead of the linear interpolation. An additional control variable is introduced to transcribe the original optimal problem with arbitrary final time to the fixed one. Then, a practical fast one-dimensional interval search algorithm is designed to optimize the additional control variable. Furthermore, to improve the convergence and prevent explosion of the LCPSO, a theorem on how to determine the boundaries of the coefficient of polynomials is given and proven. Finally, in the numeral simulations, compared with the ordinary PSO and other typical intelligent optimization algorithms GA and DE, the proposed LCPSO has traits of lower dimension, faster speed of convergence, and higher accuracy, while providing smoother control variables. |
| format | Article |
| id | doaj-art-d65b9d4fc717491eab01b459265c0811 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-d65b9d4fc717491eab01b459265c08112025-08-20T02:19:40ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/50367915036791Legendre Cooperative PSO Strategies for Trajectory OptimizationLei Liu0Yongji Wang1Fuqiang Xie2Jiashi Gao3National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaNational Key Laboratory of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Electric Engineering, University of South China, Hengyang, Hunan, ChinaNational Key Laboratory of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaParticle swarm optimization (PSO) is a population-based stochastic optimization technique in a smooth search space. However, in a category of trajectory optimization problem with arbitrary final time and multiple control variables, the smoothness of variables cannot be satisfied since the linear interpolation is widely used. In the paper, a novel Legendre cooperative PSO (LCPSO) is proposed by introducing Legendre orthogonal polynomials instead of the linear interpolation. An additional control variable is introduced to transcribe the original optimal problem with arbitrary final time to the fixed one. Then, a practical fast one-dimensional interval search algorithm is designed to optimize the additional control variable. Furthermore, to improve the convergence and prevent explosion of the LCPSO, a theorem on how to determine the boundaries of the coefficient of polynomials is given and proven. Finally, in the numeral simulations, compared with the ordinary PSO and other typical intelligent optimization algorithms GA and DE, the proposed LCPSO has traits of lower dimension, faster speed of convergence, and higher accuracy, while providing smoother control variables.http://dx.doi.org/10.1155/2018/5036791 |
| spellingShingle | Lei Liu Yongji Wang Fuqiang Xie Jiashi Gao Legendre Cooperative PSO Strategies for Trajectory Optimization Complexity |
| title | Legendre Cooperative PSO Strategies for Trajectory Optimization |
| title_full | Legendre Cooperative PSO Strategies for Trajectory Optimization |
| title_fullStr | Legendre Cooperative PSO Strategies for Trajectory Optimization |
| title_full_unstemmed | Legendre Cooperative PSO Strategies for Trajectory Optimization |
| title_short | Legendre Cooperative PSO Strategies for Trajectory Optimization |
| title_sort | legendre cooperative pso strategies for trajectory optimization |
| url | http://dx.doi.org/10.1155/2018/5036791 |
| work_keys_str_mv | AT leiliu legendrecooperativepsostrategiesfortrajectoryoptimization AT yongjiwang legendrecooperativepsostrategiesfortrajectoryoptimization AT fuqiangxie legendrecooperativepsostrategiesfortrajectoryoptimization AT jiashigao legendrecooperativepsostrategiesfortrajectoryoptimization |