Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1703510 | Applied Mathematical Modelling | 2014 | 9 Pages |
Abstract
Krill herd (KH) is a novel search heuristic method. To improve its performance, a biogeography-based krill herd (BBKH) algorithm is presented for solving complex optimization tasks. The improvement involves introducing a new krill migration (KM) operator when the krill updating to deal with optimization problems more efficiently. The KM operator emphasizes the exploitation and lets the krill cluster around the best solutions at the later run phase of the search. The effects of these enhancements are tested by various well-defined benchmark functions. Based on the experimental results, this novel BBKH approach performs better than the basic KH and other optimization algorithms.
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Authors
Gai-Ge Wang, Amir H. Gandomi, Amir H. Alavi,