A Novel Hybrid Algorithm Based on Baldwinian Learning and PSO

In the paper, a novel hybrid algorithm based on Baldwinian learning and PSO (BLPSO) is proposed to increase the diversity of the particles and to prevent premature convergence of PSO. Firstly, BLPSO adopts the Baldwinian operator to simulate the learning mechanism among the particles and employs the...

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Bibliographic Details
Published in2010 International Conference on Computational Aspects of Social Networks pp. 299 - 302
Main Authors Wanliang Wang, Lili Chen, Jing Jie, Haiyan Wang, Xinli Xu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2010
Subjects
Acceleration
Algorithm design and analysis
Artificial neural networks
Baldwinian learning
Convergence
Hybrid algorithm
Machine learning algorithms
Optimization
Particle swarm optimization
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Summary:In the paper, a novel hybrid algorithm based on Baldwinian learning and PSO (BLPSO) is proposed to increase the diversity of the particles and to prevent premature convergence of PSO. Firstly, BLPSO adopts the Baldwinian operator to simulate the learning mechanism among the particles and employs the information of the swarm to alter the search space adaptively. Secondly, a mutation operation is introduced to make the particles leap the local optimum and enhance the chance to find out the global optimum. Finally, the proposed BLPSO is used to solve some complex optimization problems, the experiment results illustrate the efficiency of the proposed method.
ISBN:9781424487851
1424487854
DOI:10.1109/CASoN.2010.73