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A novel multi-population cultural algorithm adopting knowledge migration

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Abstract

In existing multi-population cultural algorithms, information is exchanged among sub-populations by individuals. However, migrated individuals cannot reflect enough evolutionary information, which limits the evolution performance. In order to enhance the migration efficiency, a novel multi-population cultural algorithm adopting knowledge migration is proposed. Implicit knowledge extracted from the evolution process of each sub-population directly reflects the information about dominant search space. By migrating knowledge among sub-populations at the constant intervals, the algorithm realizes more effective interaction with less communication cost. Taken benchmark functions with high-dimension as the examples, simulation results indicate that the algorithm can effectively improve the speed of convergence and overcome premature convergence.

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Acknowledgment

This work was supported by National Natural Science Foundation of China under Grant 60805025 and Qinglan Project of Jiangsu.

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Authors and Affiliations

  1. College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, 221008, Jiangsu, China

    Yi-nan Guo, Jian Cheng, Yuan-yuan Cao & Yong Lin

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  1. Yi-nan Guo
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  2. Jian Cheng
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  3. Yuan-yuan Cao
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  4. Yong Lin
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Correspondence to Yi-nan Guo.

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Guo, Yn., Cheng, J., Cao, Yy. et al. A novel multi-population cultural algorithm adopting knowledge migration. Soft Comput 15, 897–905 (2011). https://doi.org/10.1007/s00500-010-0556-4

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