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• W2018571946 abstract "The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M, we propose two improved solution search equations, namely “ABC/best/1” and “ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing “ABC/rand/1” and “ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms." @default.
• W2018571946 created "2016-06-24" @default.
• W2018571946 creator A5045137489 @default.
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• W2018571946 date "2011-09-01" @default.
• W2018571946 modified "2023-10-16" @default.
• W2018571946 title "Improved artificial bee colony algorithm for global optimization" @default.
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• W2018571946 doi "https://doi.org/10.1016/j.ipl.2011.06.002" @default.
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