Artificial bee colony algorithm variants on constrained optimization
, Optimization problems are generally classified into two main groups: unconstrained and constrained. In the case of constrained optimization, special techniques are required to handle with constraints and to produce solutions in the feasible space. Intelligent optimization techniques that do not make assumptions on the problem characteristics are preferred to produce acceptable solutions to the constrained optimization problems. In this study, the performance analysis of artificial bee colony algorithm (ABC), one of the intelligent optimization techniques, is examined on constrained problems and the effect of some modifications on the performance of the algorithm is examined. Different variants of the algorithm were proposed and compared in terms of efficiency and stability. Depending on the results, when DE operators were integrated into ABC algorithm, an enhancement in the performance was gained in addition to preserving the stability of the basic ABC. The ABC algorithm is a simple optimization algorithm that can be efficiently used for constrained optimization without requiring a priori knowledge
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