Solving Parameter Identification of Nonlinear Problems by Artificial Bee Colony Algorithm | Zendy

Siamak Talatahari | Zendy; H. Mohaggeg | Zendy; Kh. Najafi | Zendy; A. Manafzadeh | Zendy

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Solving Parameter Identification of Nonlinear Problems by Artificial Bee Colony Algorithm

Author(s) -

Siamak Talatahari,

H. Mohaggeg,

Kh. Najafi,

A. Manafzadeh

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/479197

Subject(s) - artificial bee colony algorithm , identification (biology) , robustness (evolution) , optimization algorithm , mathematical optimization , bees algorithm , swarm intelligence , nonlinear system , swarm behaviour , foraging , meta optimization , algorithm , computer science , optimization problem , metaheuristic , particle swarm optimization , mathematics , biology , botany , physics , quantum mechanics , ecology , biochemistry , gene

A new optimization method based on artificial bee colony (ABC) algorithm is presented for solving parameter identification problems. The ABC algorithm as a swarm intelligent optimization algorithm is inspired by honey bee foraging. In this paper, for the first time, the ABC method is developed to determine the optimum parameters of Bouc-Wen hysteretic systems. The proposed method exhibits efficiency, robustness, and insensitivity to noise-corrupted data. The results of the ABC are compared with those other optimization algorithms from the literature to show the efficiency of this technique for solving parameter identification problems

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