Laplace Mutated Particle Swarm Optimization (LMPSO)
Particle Swarm Optimization (PSO) algorithm has shown good performance in many optimization problems. However, it can be stuck into local minima. To prevent the problem of early convergence into a local minimum, various researchers have proposed some variants of PSO. In this research different varia...
| Published in: | Life Science Journal |
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| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
Zhengzhou University
2014
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902961307&partnerID=40&md5=829481b9502bd3b5b310e323d06363b7 |
| Summary: | Particle Swarm Optimization (PSO) algorithm has shown good performance in many optimization problems. However, it can be stuck into local minima. To prevent the problem of early convergence into a local minimum, various researchers have proposed some variants of PSO. In this research different variants of PSO are reviewed that have been proposed by different researchers for function optimization problem and one new variant of PSO is proposed using Laplace distribution named as LMPSO. The performance of LMPSO is compared with existing variants of PSO proposed for function optimization. The analysis in this research shows the effect of different mutation operator on Particle Swarm Optimization (PSO). To validate the LMPSO, experiments are performed on 22 benchmark functions. The result shows that the LMPSO achieved better performance as compared to previous PSO varients. |
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| ISSN: | 10978135 |