Improved Artificial Bee Colony Algorithm Embedded with Differential Evolution Operator

Aiming at the shortcomings of the artificial bee colony (ABC) algorithm in solving complex optimization problems, such as insufficient exploitation ability, slow convergence speed, and being prone to falling into local optimal solutions, an improved ABC algorithm embedded with differential evolution operators is proposed. Firstly, a search strategy with strong exploration ability is introduced in the employed bee phase. Secondly, two differential evolution operators are embedded in the onlooker bee phase, and the two strategies are mixed in a certain hybrid ratio to balance the exploration and exploitation abilities of the algorithm. Finally, to verify the optimization performance of the proposed algorithm, a systematic comparison with four excellent ABC variants is conducted on 22 test functions. The experimental results show that the proposed algorithm is superior to or equivalent to other algorithms, demonstrating its competitiveness.