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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2314-4785 |