A Hybrid evolutionary algorithm based on Artificial Bee Colony algorithm and Differential Evolution

In order to make up for the lack of local development capabilities of the classic artificial bee colony algorithm (ABC), an improved artificial bee colony search strategy is proposed: a new solution search equation is introduced, and a combined search strategy is formed with the original solution search equation in the classic artificial bee colony algorithm. To further improve the convergence speed and accuracy of the algorithm, an improved differential evolution strategy is proposed: a probability parameter is introduced to adjust the selection of the crossover probability of the differential evolution algorithm(DE); finally, in order to alleviate the harm of premature convergence caused by increasing the convergence speed, introduce An improvement mechanism; based on these three major improvements, a hybrid evolutionary algorithm based on ABC and DE is proposed. Then, based on a simulation experiment composed of benchmark test functions, the entire algorithm was verified. The results compared with the classic ABC and DE show that the improvement has obvious effects.