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Harris Hawks Optimization Algorithm Based on Elite Fractional Mutation for Data Clustering by Wenyan Guo; Peng Xu; Fang Dai; Zhuolin Hou is a Computer Science article available to read on EtoBox.

What is Harris Hawks Optimization Algorithm Based on Elite Fractional Mutation for Data Clustering about?

The density peak clustering (DPC) algorithm is an efficient clustering algorithm that can automatically find the class center and realize arbitrary shape data clustering. The design of the local density is the core of the DPC algorithm, and the value of the cutoff distance parameter involved in the calculation of the local density has a great impact on the performance of the algorithm. In this paper, based on analyzing the defects of the local density design of the DPC algorithm, we use the cosine similarity and exponential decay function to establish a new method of segmented local density calculation, and build an optimization model for the selection of cutoff distance parameters. A new density peak clustering algorithm (NDPC) is proposed. When solving the model, an improved Harris hawks optimization algorithm (FHHO)based on elite fractional derivative mutation is proposed. Simultaneously, the FHHO-NDPC algorithm combining FHHO and NDPC algorithm is put forward. The FHHO algorithm uses Grünwald-Letnikov (G-L) fractional derivative to correct the elite population which changes with the number of iterations and uses a more random exploration strategy to enhance the exploration perf

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Author

Wenyan Guo; Peng Xu; Fang Dai; Zhuolin Hou

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)