An Improved Bare Bones Particle Swarm Optimization Algorithm Based on Sequential Update Mechanism and a Modified Structure
The past three decades have witnessed the rapid development of nature-inspired algorithms. Among these, population-based optimization algorithms have gained significant popularity due to their effectiveness in solving a wide range of problems. Particle Swarm Optimization (PSO) stands out as a pionee...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10820314/ |
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| Summary: | The past three decades have witnessed the rapid development of nature-inspired algorithms. Among these, population-based optimization algorithms have gained significant popularity due to their effectiveness in solving a wide range of problems. Particle Swarm Optimization (PSO) stands out as a pioneering algorithm in this domain. Bare-Bones Particle Swarm Optimization (BBPSO) is a simplified variant of PSO that eliminates the velocity term and additional parameters. This study introduces a novel sequential update rule for BBPSO, along with a modification to the standard algorithm. The proposed methods were evaluated on a comprehensive benchmark suite, including 36 benchmark problems from the literature, 30 benchmark problems from CEC2021, consisting of 10 basic and 20 transformed variants and 5 engineering optimization problems. Comparative analysis with standard BBPSO and other simplified PSO variants demonstrated the effectiveness of our proposed approach. |
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| ISSN: | 2169-3536 |