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• W2896002634 abstract "Although many evolutionary algorithms (EAs) have successfully solved different optimization problems, no single EA has consistently been the best for all these problems. During the last decade, to alleviate this limitation, many proposals which utilize multiple EAs in a single algorithmic framework, called multi-methods or multi-operators, have been introduced. However, there is still room to enhance their performance. In this paper, an improved variant of a united multi-operator algorithm is introduced with few improvements that are capable of providing a balance between diversification and intensification properties during the optimization. The proposed algorithm is tested on the CEC2017 unconstrained benchmark problems, with the results revealing that the proposed algorithm is capable of producing high quality solutions compared with those of state-of-the-art algorithms." @default.
• W2896002634 created "2018-10-26" @default.
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• W2896002634 date "2018-07-01" @default.
• W2896002634 modified "2023-09-25" @default.
• W2896002634 title "Improved United Multi-Operator Algorithm for Solving Optimization Problems" @default.
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• W2896002634 doi "https://doi.org/10.1109/cec.2018.8477759" @default.
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