DEHM: An Improved Differential Evolution Algorithm Using Hierarchical Multistrategy in a Cybertwin 6G Network
Zhou Zhou, Jemal Abawajy, Mohammad Shojafar, Morshed Chowdhury
Abstract
Differential evolution (DE) algorithm can be used in edge/cloud cyberspace to find an optimal solution due to its effectiveness and robustness. With the rapid increase of the mobile traffic data and resources in a cybertwin-driven 6G network, the DE algorithm faces some problems such as premature convergence and search stagnation. To deal with the problems mentioned above, in this article, an improved DE algorithm based on hierarchical multistrategy in a cybertwin-driven 6G network (denoted by DEHM) is proposed. Based on the fitness value of the population, DEHM classifies the population into three sub-population. Regarding each sub-population, DEHM adopts different mutation strategies to achieve a tradeoff between convergence speed and population diversity. In addition, a new selection strategy is presented to ensure that the potential individual with good genes is not lost. Experimental results suggest that the DEHM algorithm surpasses other benchmark algorithms in the field of convergence speed and accuracy. The proposed DEHM is expected to be leveraged in edge/cloud cyberspace, aiming at reducing energy costs and improving resource utilization.