A sensitivity analysis method for driving the Artificial Bee Colony algorithm's search process

• The Artificial Bee Colony algorithm is improved with a sensitivity analysis method.
• Morris’ OAT method detects high influential dimensions.
• Morris’ OAT method drives the neighborhood search of the ABC algorithm.
• ABC-Morris outperforms the ABC algorithm on classical optimization functions.

In this paper, we improve D. Karaboga's Artificial Bee Colony (ABC) optimization algorithm, by using the sensitivity analysis method described by Morris. Many improvements of the ABC algorithm have been made, with effective results. In this paper, we propose a new approach of random selection in neighborhood search. As the algorithm is running, we apply a sensitivity analysis method, Morris’ OAT (One-At-Time) method, to orientate the random choice selection of a dimension to shift. Morris’ method detects which dimensions have a high influence on the objective function result and promotes the search following these dimensions. The result of this analysis drives the ABC algorithm towards significant dimensions of the search space to improve the discovery of the global optimum. We also demonstrate that this method is fruitful for more recent improvements of ABC algorithm, such as GABC, MeABC and qABC.

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