Artificial Bee Colony is a single objective optimization technique for unconstrained optimization problems. It has been reported that ABC has been improperly implemented in various works (specially with respect to functional limitations). This implementation is based on the Algorithm 1 provided in the following
On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation, Information Sciences 291, (2015) 115-127; https://doi.org/10.1016/j.ins.2014.08.040
(i) Unlike other computational intelligence techniques, the number of functional evaluations cannot be deterministically determined based on the number of food sources and the number of cycles.
(ii) The user defined parameters are (a) the number of food sources, (b) the number of maximum functional evaluations and (c) the parameter 'limit' which governs the removal of a solution from the solution pool. This has been widely used as (dimension of the problem * No. of food sources) but has been reported to have significant impact on the performance of the algorithm.
(iii) This implementation ensures monotonic convergence.
SKS Labs (2020). Single Objective Artificial Bee Colony Optimization (https://www.mathworks.com/matlabcentral/fileexchange/65794-single-objective-artificial-bee-colony-optimization), MATLAB Central File Exchange. Retrieved .
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