Abstract
AFSA has been widely used as its super global search ability. However, AFSA still has the problem of falling into the local optimal value due to the randomness of the initial states of AFs, this paper introduces k-means clustering method into AFSA to ensure the randomness and the uniform distribution of the initial states of AFs by introducing distance factor. In the end this paper presents two types of testing functions are introduced to prove the improved method is better in convergence rate, accuracy and the effect of avoiding the local optimum.
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Acknowledgments
This article was completed under the auspices of the BY2015065-09 fund, in which the author would like to expresses his sincere gratitude.
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Li-hua, Z., Zhi-qian, D., Guo-long, S. (2018). An Improved Artificial Fish-Swarm Algorithm Using Cluster Analysis. In: Qiao, F., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2017. Advances in Intelligent Systems and Computing, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-319-65978-7_8
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DOI: https://doi.org/10.1007/978-3-319-65978-7_8
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