Tree Seed Binary Differential Evolution Algorithm Based Access Point Selection Strategies in Cell-Free Massive MIMO Systems

In cell-free massive MIMO systems， The connection approach where a large number of access points （APs） serve numerous users suffers from high power consumption and backhaul link overhead. This paper proposes the tree seed binary differential evolution algorithm based AP selection strategies to select the optimal serving AP sets. Firstly， an AP selection algorithm based on binary differential evolution （BDE） is exploited via the evolution of multiple individuals to resolve the global optimization of high-dimensional data. Secondly， a dual-mechanism search strategy based on the tree seed optimization is proposed to avoid trapping in the local optimal solution， in which search tendency （ST） is used to achieve the best balance between the global and local search. Finally， an adaptive decreasing criterion of crossover rate （CR） is defined to accelerate the convergence. Compared with the existing algorithms， simulation results demonstrate that the proposed algorithm can significantly improve the sum rate of system.