Abstract

In the basic Artificial Bee Colony (ABC) algorithm, if the fitness value associated with a food source is not improved for a certain number of specified trials then the corresponding bee becomes a scout to which a random value is assigned for finding the new food source. Basically, it is a mechanism of pulling out the candidate solution which may be entrapped in some local optimizer due to which its value is not improving. In the present study, we propose two new mechanisms for the movements of scout bees. In the first method, the scout bee follows a non-linear interpolated path while in the second one, scout bee follows Gaussian movement. Numerical results and statistical analysis of benchmark problems indicate that the proposed modifications enhance the performance of ABC.

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