A Multi-swarm Artificial Bee Colony Algorithm for Function Optimization
[1]Wang Shouna,Liu Hong,Gao Kaizhou.A Multi-swarm Artificial Bee Colony Algorithm for Function Optimization[J].Journal of Zhengzhou University (Engineering Science),2018,39(06):30-35.[doi:10.13705/j.issn.1671-6833.2018.06.019]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
39
Number of periods:
2018 06
Page number:
30-35
Column:
Public date:
2018-10-24
- Title:
- A Multi-swarm Artificial Bee Colony Algorithm for Function Optimization
- Keywords:
- Artificial bee colony algorithm; population segmentation; honey source location update; fitness function; function optimization
- CLC:
- -
- Abstract:
- A multi-swarm Artificial Bee Colony（MABC）algorithm based on the segmentation of population was proposed in this paper. It was applied to function optimization to overcome the drawbacks of slow convergence and low computational accuracy of conventional ABC algorithm. In this algorithm， K-means clustering algorithm based on Euclidean distance was introduced to divide the bee colony. In the subpopulation, a method was introduced to update the location of nectar based on global communication to accelerate the convergence of the algorithm;and the fitness function based on local communication was introduced to expand the diversity of the solution. The simulation results of six standard functions show that the MABC algorithm can attain significant improvement on convergence rate and solution accuracy， and show better performance in function optimization problems when compared with the ABC algorithm.
- References:
- -
- Similar References:
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Last Update:
2018-10-25