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This paper proposes to hybridize the migration operator of biogeography-based optimization (BBO) approach with artificial bee colony (ABC) algorithm.
Based on these observations, we propose to hybridize ABC with migration operator of the BBO algorithm to combine their strengths, called ABCBBO.
In this paper, we first derive the forward modeling and migration operator of simultaneous-source data, and then present the theory of regularized least-squares reverse time migration (RLSRTM).
To solve the design of radar waveforms such as polyphase codes design, this paper proposes to combine the migration operator of biogeography-based optimization (BBO) approach with the employed bee phase of artificial bee colony (ABC).
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The first one is a novel migration operator to mitigate the costs incurred on a cloud-based environment whereas the second operator is the pattern reduction operator [ 29, 30] to filter out the worst search directions.
The migration operator was the movement of individuals if population size was set to be a vector of length greater than 1.
As a result, we also expect that a larger aperture of the migration operator can be selected for the LLNL method.
This algorithm combines the migration operator with the employed bee phase of ABC.
Two efficient operators are employed in this paper: (1) the novel migration operator is aimed to avoid wasting of the computation power and waiting for the other virtual machines on a cloud computing environment, and (2) the pattern reduction operator is aimed to enhance the search performance.
Migration operator is able to promote information exchange of solutions in bee colony.
To remedy the defect, on one hand, an efficient operator named polyphyletic migration operator is proposed to enhance the search ability of POLBBO.
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