Study on Dynamic Equilibrium Factor based on Gauss-Cauchy Distribution in Artificial Bee Colony Algorithm

Based on the limitations of the traditional artificial bee colony algorithm in employed bee, onlooker bee and scout bee phase search strategy. The article proposes a dynamic balance factor strategy that uses the combination of Gauss-Cauchy distribution to further optimize the shortcomings of the original algorithm in global search and local search. The dynamic balance factor combines the characteristics of Gaussian distribution and Cauchy distribution, enriches the population diversity in the stage of scout bee and employed bee, and improves the accuracy of local search in the stage of onlooker bee. In the later stage of the algorithm, adaptive influence coefficients are introduced based on the characteristics of mathematical functions to change the algorithm’s convergence speed, so as to realize the optimization of the algorithm’s search breadth and accuracy, as well as the convergence speed. The results of simulation experiments show that the artificial bee colony algorithm base on Normal-Cauchy distribution (ABC-NCD) based on Gauss-Cauchy distribution is superior to other similar algorithms in terms of global search breadth and local convergence accuracy.