Self-Adaptive Induced Mutation Algorithm for Reconfigurable Antenna Systems

Reconfigurable antennas offer great advantages over traditional antennas in terms of physical size and bandwidth. A practical reconfigurable antenna, usually equipped with multiple switches, has a high real-time requirement for the optimization algorithm. In this letter, we therefore propose a self-adaptive induced mutation algorithm (SIMA) that has a fast convergence. SIMA first initializes a population using good point set and determines the “key switches” by analyzing the distribution of the switch states of lower standing-wave ratio in the evolution. The configurations of worse states are then induced to set up their “key switches.” Experiments on the optimization of a 39-switch reconfigurable antenna system at 50, 200, and 350 MHz demonstrate that the convergence rate of SIMA is at least 2.15 times that of the genetic algorithm.