Artificial bee colony algorithm with multi-strategy adaptation
Zhaolu Guo, Li Hongjin, Wensheng Zhang · International Journal of Bio-Inspired Computation · 2024
To improve the convergence performance of artificial bee colony (ABC) algorithm for tackling some complex optimisation issues, a new ABC with multi-strategy adaptation (MSABC) is presented. A multi-strategy adaptation mechanism is implemented to boost the search performance in MSABC. In this mechanism, an evolution rate index is proposed to adaptively select strategies with different characteristics at various evolutionary stages. Meanwhile, for balancing exploration and exploitation, a novel search strategy oriented by the elite individual is applied in this mechanism. On the CEC2014 test set, MSABC is compared to other existing algorithms to assess its performance. As the results demonstrate, MSABC can obtain good convergence performance.