Perpendicular Bisector Optimization Algorithm (PBOA): A Novel Geometric-Mathematics-Inspired Metaheuristic Algorithm for Controller Parameter Optimization
I tiakina i:
| I whakaputaina i: | Symmetry vol. 17, no. 9 (2025), p. 1410-1463 |
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| Kaituhi matua: | |
| Ētahi atu kaituhi: | , |
| I whakaputaina: |
MDPI AG
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
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| 022 | |a 2073-8994 | ||
| 024 | 7 | |a 10.3390/sym17091410 |2 doi | |
| 035 | |a 3254649155 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231635 |2 nlm | ||
| 100 | 1 | |a Wu, Dafei | |
| 245 | 1 | |a Perpendicular Bisector Optimization Algorithm (PBOA): A Novel Geometric-Mathematics-Inspired Metaheuristic Algorithm for Controller Parameter Optimization | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a To address the inadequate balance between exploration and exploitation of existing algorithms in complex solution spaces, this paper proposes a novel mathematical metaheuristic optimization algorithm—the Perpendicular Bisector Optimization Algorithm (PBOA). Inspired by the geometric symmetry of perpendicular bisectors (the endpoints of a line segment are symmetric about them), the algorithm designs differentiated convergence strategies. In the exploration phase, a slow convergence strategy is adopted (deliberately steering particles away from the optimal region defined by the perpendicular bisector) to expand the search space; in the exploitation phase, fast convergence refines the search process and improves accuracy. It selects 4 particles to construct line segments and perpendicular bisectors with the current particle, enhancing global exploration capability. The experimental results on 27 benchmark functions, compared with 15 state-of-the-art algorithms, show that the PBOA outperforms others in accuracy, stability, and efficiency. When applied to 5 engineering design problems, its fitness values are significantly lower. For H-type motion platforms, the PBOA-optimized platform not only achieves high unidirectional motion accuracy, but also the average synchronization error of the two Y-direction motion mechanisms reaches ±2.6 × 10−5 mm, with stable anti-interference performance. | |
| 653 | |a Accuracy | ||
| 653 | |a Search process | ||
| 653 | |a Design optimization | ||
| 653 | |a Principles | ||
| 653 | |a Convergence | ||
| 653 | |a Exploitation | ||
| 653 | |a Design engineering | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Hydrologic cycle | ||
| 653 | |a Mutation | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Decision making | ||
| 653 | |a Optimization | ||
| 653 | |a Solution space | ||
| 653 | |a Synchronism | ||
| 653 | |a Algorithms | ||
| 653 | |a Foraging behavior | ||
| 653 | |a Segments | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Geometry | ||
| 653 | |a Symmetry | ||
| 653 | |a Heuristic methods | ||
| 700 | 1 | |a Chen, Wei | |
| 700 | 1 | |a Zhang, Ying | |
| 773 | 0 | |t Symmetry |g vol. 17, no. 9 (2025), p. 1410-1463 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254649155/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254649155/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254649155/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |