A Self-tuned Bat Algorithm for Optimization in Radiation Therapy Treatment Planning

The performance of any optimization algorithm largely depends on the setting of its algorithm-dependent parameters. Swarm intelligence algorithms are popular methods in optimization since they have been proved very efficient. One drawback of those methods though, is that the appropriate setting of the algorithm-dependent parameters has a significant impact on the algorithm's performance. The "parameter tuning" of an algorithm in such a way to be able to find the optimal solution by using the minimum number of iterations, quite often is a difficult and time consuming task depending on the optimization problem. Essentially this is a hyper-optimization problem, that is, the optimization of the optimization algorithm. In this paper, a novel self-tuned metaheuristic algorithm is presented for optimization in radiation therapy treatment planning. The proposed Self-Tuned Bat Algorithm (STBA) finds itself the optimal set of algorithm-dependent parameters and therefore minimizes the number of iterations required for the optimization to reach sub-optimal solution. The applicability of the proposed algorithm is demonstrated in the optimization of a prostate case using intensity modulation radiation therapy (IMRT).