Artiflcial Bee Colony (ABC) is a swarm-based metaheuristic for continuous optimization. Recent work hybridized this algorithm with other metaheuristics in order to improve performance. The work in this paper, experimentally evaluates the use of difierent mutation operators with the ABC algorithm. The introduced operator is activated according to a determined probability called mutation rate (MR). The results on standard benchmark function suggest that the use of this operator improves performance in terms of convergence speed and quality of flnal obtained solution. It shows that Power and Polynomial mutations give best results. The fastest convergence was for the mutation rate value (MR=0.2).
['Iyad Abu Doush', 'Basima Hani Hasan', 'Mohammed Azmi Al-Betar', 'Eslam Al Maghayreh', 'Faisal Alkhateeb', 'Mohammad Hamdan']
Artificial Bee Colony with Different Mutation Schemes - A comparative study.