[1]SUN Wenxin,MU Huaping.Particle swarm optimization based on self-adaptive population structure[J].CAAI Transactions on Intelligent Systems,2013,8(4):372-376.[doi:10.3969/j.issn.1673-4785.201211041]
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Particle swarm optimization based on self-adaptive population structure
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
8
Number of periods:
2013 4
Page number:
372-376
Column:
学术论文—机器学习
Public date:
2013-08-25
- Title:
- Particle swarm optimization based on self-adaptive population structure
- CLC:
- TP18
- DOI:
- 10.3969/j.issn.1673-4785.201211041
- Abstract:
- In particle swarm optimization, the organization mode of population structure directly determines the information sharing and exchange between the particles which produce great influence. Based on the principle of dynamics in the formation process of the complex network, a new particle swarm optimization algorithm was proposed, which is a self-adaptive population structure. At the initial stages of the algorithm, particles are more dispersed with small amount of random connections and search mode is single. With the generation of new connections, the search mode of particles was gradually evolved from Lbest model to Gbest model, which is beneficial to better optimization performance. The evolution of population structure is conducive to early “exploration” and late “exploitation” of the algorithm. The experimental results show that the convergence performance of the new algorithm has been greatly improved.
- References:
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Last Update:
2013-09-27