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Cluster Computing

, Volume 22, Supplement 6, pp 14125–14134 | Cite as

Improved swarm intelligence algorithm for protein folding prediction

  • Shigang WangEmail author
Article

Abstract

Swarm intelligence algorithm is to simulate the behaviour of groups of organisms to solve the target problem heuristic search algorithm. This paper briefly describes four swarm intelligence algorithms, focus on analysis of the ant colony algorithm and artificial fish algorithm and how to merge the two algorithms. Based on ACO and AFSA, a new fusion algorithm is proposed. The fusion algorithm has the robustness and parallelism of the ant colony algorithm, and has the advantages of strong global search ability and fast search speed of artificial fish swarm algorithm. The convergence of the improved fusion algorithm is proved by analysis, and the algorithm is applied to the field of protein folding prediction. It can be found that the new fuse algorithm, which in the case of find out the minimum energy value, the time required for the algorithm is less than that of the basic algorithm. It not only ensures the quality of the optimal value, but also greatly saves the calculation time. It has great practical value.

Keywords

Swarm intelligence algorithm ACO AFSA Protein folding 

Notes

Acknowledgements

This work was supported by the Natural Science Foundation of Guangxi Province, China (Grant No. 2016GXNSFAA380211) and Doctoral Foundation of Guangxi University of Science and Technology (Grant No. XKB17Z08).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringGuangxi University of Science and TechnologyLiuzhouChina

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