An Adaptive ABC Variant For Numerical Optimization

It is known that the ABC algorithm has a slow convergence rate, this could be attributed to the fact that new solutions are generated by updating only one optimization parameter. Therefore, updating more optimization parameters could enhance the optimization performances. However, increasing this number arbitrary could decrease the optimization performance, as updating only one optimization parameter could be more efficient for some cases. To this end, we are proposing a new ABC variant that can adaptively control the number of optimization parameters to update during the run.The performance of the proposed approach has been evaluated against five other ABC variants using 16 numerical benchmark functions of varying characteristics. The obtained results have demonstrated the efficiency of the proposed algorithm.