A hybrid ABC for expensive optimizations: CEC 2016 competition benchmark
Enrico Ampellio, Luca Vassio · 2016
An evolution of the Artificial Bee Colony (ABC) optimization algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented for leading to the best improvement with a low number of analyses. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of ABC structure and original hybridizations with interpolation strategies. The aforementioned techniques are tested on the expensive benchmark of the Special Session on RealParameter Single Objective Optimization at CEC 2016. In this specific case, the hybridization with a quadratic trust region approach assumes a major importance. Moreover, the AsBeC results are compared to the algorithms tested on the same benchmark at CEC 2015, showing remarkable competitiveness and robustness.