Using Cooperation Approaches at Different Levels of Artificial Bee Colony Method
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Description
In this work, a Multi-Level Artificial Bee Colony
(called MLABC) for optimizing numerical test functions is presented.
In MLABC, two species are used. The first species employs n
colonies where each of them optimizes the complete solution vector.
The cooperation between these colonies is carried out by exchanging
information through a leader colony, which contains a set of elite
bees. The second species uses a cooperative approach in which the
complete solution vector is divided to k sub-vectors, and each of
these sub-vectors is optimized by a colony. The cooperation between
these colonies is carried out by compiling sub-vectors into the
complete solution vector. Finally, the cooperation between two
species is obtained by exchanging information. The proposed
algorithm is tested on a set of well-known test functions. The results
show that MLABC algorithm provides efficiency and robustness to
solve numerical functions.
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References
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