Complex permittivity inversion algorithm with adaptive learning strategy and parameter balancing mechanism

In this paper, we studied an adaptive learning particle swarm optimization (ALPSO) algorithm for complex permittivity inversion problem. Adaptive learning strategy and parameter balancing mechanism were used in the algorithm. The candidate particle generated in the adaptive learning strategy competed with the global optimal particle, which enabled the population to maintain strong global search capability and high search accuracy. A parameter balancing mechanism was proposed for complex permittivity inversion of low-loss materials. The inversion results of three samples showed that ALPSO not only had fast convergence speed but also was not easy to fall into the local optimum. The objective function improvement rate was 10.91 %, 1.21 % and 37.53 %, respectively. The reflectivity measurement results of the composite sample were in good agreement with the theoretical calculation results, which proved the correctness of the inversion results.