Abstract
The performance of the genetic algorithm (GA) and particle swarm optimization (PSO) was compared to identify the best-suited algorithm for hyperthermia treatment planning (HTP) of breast cancer. Both algorithms were tested on four heterogeneous patient breast models derived from magnetic resonance (MR) images. Electromagnetic (EM) simulations indicate that PSO induces 5.7% less hotspot to target quotient (HTQ) compared to GA. However, coupled EM and thermal simulations of four patient models indicate that GA based HTP induces $\boldsymbol{1.25}^{\circ} \mathbf{C}-\boldsymbol{3.87}^{\circ}\mathbf{C}$ higher average temperature in cancer tissue with limited thermal hotspots in healthy tissue when compared to PSO algorithm. This was observed to be due to the low power level assigned to each channel by PSO compared to GA. Coupled simulations of heterogeneous patient models indicate GA is a better global optimization algorithm for HTP of breast cancer.
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