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| Clustering Based Pseudo-Parallel Genetic Algorithms |
| LI Jun-Hua1, 2, LI Ming1, YUAN Li-Hua1, 2 |
1.Key Laboratory of Nondestructive Test of Ministry of Education, Nanchang Hangkong University,Nanchang 330063
2.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing 210016 |
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Abstract The traditional genetic algorithm (GA) for multi-modal function optimization is studied and the characteristics of Niche GA and multi-population GA are analyzed. A clustering based pseudo-parallel genetic algorithm is proposed. Cluster analysis is carried out on all the individuals. Local search algorithm is used to search the optimum in all clusters. A new subpopulation is created by the unclassified individuals and the representations of all clusters. To get better global search capacity, niche technology is applied in the subpopulation. The convergence of the algorithm is proved theoretically. Moreover, a new method is designed for automatically calculating clustering threshold. Finally, the presented algorithm is compared with EGA、DCGA and MPGA. Results show that the new algorithm is well in searching global optimum and maintaining population diversity.
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Received: 24 March 2008
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2009, Vol. 22 
