A novel discrete particle swarm optimization algorithm for estimating dielectric constants of tissue

Global optimization algorithms basically create a set of solutions, classify them, and then search for the best answer, iteratively. In this paper, a new discrete particle swarm optimization algorithm is proposed to estimate the permittivity arrangements of lossy multilayer structures, which represent body tissue models. Microwave imaging (AMI) is the modality in which the proposed algorithm is used for reconstructing the image. The main objective of this article is to depict the flexibility of PSO-based methods in handling complex problems expeditiously and successfully. Our new algorithm improves the estimation time by 85% as compared to our previous proposed one. Here, the impact of various parameters, namely, the AMI frequency, the immersion medium, the number of agents, the smoothing coefficient, and the maximum velocity, on the estimation performance are studied in terms of the maximum estimation error. It is demonstrated that by choosing the parameters correctly, one can achieve estimation results with a maximum error less that 10% in only 0.1 minute.