FSDE-Forced Strategy Differential Evolution used for data clustering | Zendy

Meera Ramadas | Zendy; Ajith Abraham | Zendy; Sushil Kumar | Zendy

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FSDE-Forced Strategy Differential Evolution used for data clustering

Author(s) -

Meera Ramadas,

Ajith Abraham,

Sushil Kumar

Publication year - 2016

Publication title -

journal of king saud university - computer and information sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.617

H-Index - 33

eISSN - 2213-1248

pISSN - 1319-1578

DOI - 10.1016/j.jksuci.2016.12.005

Subject(s) - differential evolution , cluster analysis , mutation , crossover , benchmark (surveying) , computer science , constant (computer programming) , differential (mechanical device) , mathematical optimization , data mining , algorithm , mathematics , artificial intelligence , physics , biology , biochemistry , geodesy , gene , programming language , geography , thermodynamics

This chapter introduces another approach of Differential Evolution algorithm named as FSDE—Forced Strategy Differential Evolution. FSDE uses two control parameters: a constant parameter and a varying parameter. By using two control parameters, the efficiency of FSDE improves greatly. This variant is then applied on clustering of data.

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