A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy
In this study, a particle swarm optimization (PSO) algorithm with a negative gradient perturbation and binary tree depth-first strategy (GB-PSO) is proposed. The negative gradient term accelerates particle optimization in the direction of decreasing the objective function value. To calculate the ste...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/6599899 |
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| _version_ | 1832561590302408704 |
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| author | Xionghua Huang Tiaojun Zeng MinSong Li |
| author_facet | Xionghua Huang Tiaojun Zeng MinSong Li |
| author_sort | Xionghua Huang |
| collection | DOAJ |
| description | In this study, a particle swarm optimization (PSO) algorithm with a negative gradient perturbation and binary tree depth-first strategy (GB-PSO) is proposed. The negative gradient term accelerates particle optimization in the direction of decreasing the objective function value. To calculate the step size of this gradient term more easily, a method based on the ratio was proposed. In addition, a new PSO strategy is also proposed. Each iteration of PSO yields not only the current optimal solution of the group, but also the solution based on the 2-norm maximum. Under the current iteration solution of PSO, these two solutions are the children nodes. In the sense of the binary tree concept, the three solutions constitute the father-son relationship, and the solution generated throughout the entire search process constitutes the binary tree. PSO uses a traceable depth-first strategy to determine the optimal solution. Compared with the linear search strategy adopted by several algorithms, it can fully utilize the useful information obtained during the iterative process, construct a variety of particle swarm search paths, and prevent premature and enhance global optimization. The experimental results show that the algorithm outperforms some state-of-the-art PSO algorithms in terms of search performance. |
| format | Article |
| id | doaj-art-c106c619228e45908b14722b432b47ed |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-c106c619228e45908b14722b432b47ed2025-02-03T01:24:38ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/6599899A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search StrategyXionghua Huang0Tiaojun Zeng1MinSong Li2School of Information Sciences and EngineeringSchool of Information Sciences and EngineeringSchool of Information Sciences and EngineeringIn this study, a particle swarm optimization (PSO) algorithm with a negative gradient perturbation and binary tree depth-first strategy (GB-PSO) is proposed. The negative gradient term accelerates particle optimization in the direction of decreasing the objective function value. To calculate the step size of this gradient term more easily, a method based on the ratio was proposed. In addition, a new PSO strategy is also proposed. Each iteration of PSO yields not only the current optimal solution of the group, but also the solution based on the 2-norm maximum. Under the current iteration solution of PSO, these two solutions are the children nodes. In the sense of the binary tree concept, the three solutions constitute the father-son relationship, and the solution generated throughout the entire search process constitutes the binary tree. PSO uses a traceable depth-first strategy to determine the optimal solution. Compared with the linear search strategy adopted by several algorithms, it can fully utilize the useful information obtained during the iterative process, construct a variety of particle swarm search paths, and prevent premature and enhance global optimization. The experimental results show that the algorithm outperforms some state-of-the-art PSO algorithms in terms of search performance.http://dx.doi.org/10.1155/2022/6599899 |
| spellingShingle | Xionghua Huang Tiaojun Zeng MinSong Li A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy Journal of Mathematics |
| title | A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy |
| title_full | A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy |
| title_fullStr | A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy |
| title_full_unstemmed | A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy |
| title_short | A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy |
| title_sort | particle swarm optimization algorithm with gradient perturbation and binary tree depth first search strategy |
| url | http://dx.doi.org/10.1155/2022/6599899 |
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