Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
The partitioned step particle swarm optimization (PSPSO) introduces a two-fold searching mechanism that increases the search capability of Particle Swarm Optimization. The first layer involves the <i>γ</i> and <i>λ</i>, values which are introduced to describe the current cond...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/6/2670 |
| Summary: | The partitioned step particle swarm optimization (PSPSO) introduces a two-fold searching mechanism that increases the search capability of Particle Swarm Optimization. The first layer involves the <i>γ</i> and <i>λ</i>, values which are introduced to describe the current condition of characteristics of the searched solution that diversifies the particles when it is converging too much on some optima. The second layer involves the partitioning of particles that tries to prevent premature convergence. With the two search mechanisms, the PSPSO presents a simpler way of making the particles communicate with each other without too much compromise of the computational time. The proposed algorithm was compared with different variants of particle swarm optimization (PSO) using benchmark functions as well as the IEEE 10-unit unit commitment problem. Results proved the effectiveness of PSPSO with different functions and proved its competitive advantage in comparison with published PSO variants. |
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| ISSN: | 2076-3417 |