Abstract
Firefly algorithm is a simple and efficient meta-heuristic optimization algorithm which has outstanding performance on many optimization problems. However, in the standard FA, the fireflies will be attracted by all the other bright fireflies, and there is a lot of attraction that does not affect, but will increase the computational time of the algorithm. In addition, all the best firefly information in the search process has not been recorded, which may lead the algorithm to be inefficient. To over these problems, this paper proposed en elite-k attraction firefly algorithm (EkFA), which can not only reduce the no effective attractions between the fireflies but also can make full use of the best firefly’s information to guide other nearby fireflies to movement. Thirteen well-known benchmark functions are used to verify the performance of our proposed method. The experimental results show that the accuracy and efficiency of the proposed algorithm are significantly better than those of other FA variants.
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Acknowledgments
The authors would like to thank anonymous reviewers for their detailed and constructive comments that help us to increase the quality of this work. This work was supported by the National Natural Science Foundation of Jiangxi Province (No. 20151BAB207023) and Science and Technology project of Jiangxi Provincial Department of Education (No. GJJ150448).
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Wang, J. (2018). Firefly Algorithm with Elite Attraction. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_16
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DOI: https://doi.org/10.1007/978-981-13-1648-7_16
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