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- W4313495902 abstract "Covariance matrix adaptive evolution strategy (CMAES) is an excellent local search strategy that does not rely on gradient information. It is widely used in real-valued optimization because it fast converges to the local optimum. In addition, manta ray foraging optimization (MRFO) is an evolutionary algorithm inspired by the manta rays’ three particular foraging strategies which can carry out a good solution for global optimization problems. In this paper, we use CMAES to strengthen the local search ability of MRFO and propose CMAES-MRFO to generate better solutions. The CEC’2017 benchmark functions are employed to test the hybrid algorithm. Experimental results show that the proposed CMAES-MRFO significantly outperforms its original algorithms and some other newly-coming ones." @default.
- W4313495902 created "2023-01-06" @default.
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- W4313495902 date "2022-11-29" @default.
- W4313495902 modified "2023-10-17" @default.
- W4313495902 title "A Covariance Matrix Adaptation Evolution Strategy-based Manta Ray Foraging optimization" @default.
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- W4313495902 doi "https://doi.org/10.1109/scisisis55246.2022.10001965" @default.
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