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A Novel Animal Migration Algorithm for Global Numerical Optimization
Computer Science and Information Systems, Tome 13 (2016) no. 1 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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Animal migration optimization (AMO) searches optimization solutions by migration process and updating process. In this paper, a novel migration process has been proposed to improve the exploration and exploitation ability of the animal migration optimization. Twenty-three typical benchmark test functions are applied to verify the effects of these improvements. The results show that the improved algorithm has faster convergence speed and higher convergence precision than the original animal migration optimization and other some intelligent optimization algorithms such as particle swarm optimization (PSO), cuckoo search (CS), firefly algorithm (FA), bat-inspired algorithm (BA) and artificial bee colony (ABC).
Keywords: animal migration optimization algorithms, exploration and exploitation, functions optimization 
@article{CSIS_2016_13_1_a13,
     author = {Qifang Luo and Mingzhi Ma and Yongquan Zhou},
     title = {A {Novel} {Animal} {Migration} {Algorithm} for {Global} {Numerical} {Optimization}},
     journal = {Computer Science and Information Systems},
     year = {2016},
     volume = {13},
     number = {1},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2016_13_1_a13/}
}
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AU  - Mingzhi Ma
AU  - Yongquan Zhou
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UR  - http://geodesic.mathdoc.fr/item/CSIS_2016_13_1_a13/
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%F CSIS_2016_13_1_a13
Qifang Luo; Mingzhi Ma; Yongquan Zhou. A Novel Animal Migration Algorithm for Global Numerical Optimization. Computer Science and Information Systems, Tome 13 (2016) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2016_13_1_a13/