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Establishing Radiotherapy on a Hybridization of Genetic Algorithms, and Particle Swarm Optimization
  • Keshav Kumar K
Keshav Kumar K
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 In large-scale, non-convex problems, adaptive stochastic global optimization methodologies are frequently used. But improving these methodsâ\euro™ search effectiveness and re- peatability frequently calls for well-customized methodologies. Intensity-modulated radiation treatment (IMRT) planning faces the critical but difficult problem of automatically selecting the beam angle. Despite numerous efforts, the clinical IMRT practice continues to be not particularly satisfying for the reason of the excessive processing of the inverse circumstance. The objective problem that we would discuss in this research is the 4-Dimensional Radiation Therapy (4DRT) Inverse Planning Problem. Previous research in this domain makes use of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This article aims in proposing a hybrid model that combines both the Genetic Algorithm as well as the Particle Swarm Optimization and chooses the best resulting parameters. The proposition makes use of the concept of threading that allows both the GA, and the PSO to run in parallel at the same time.Â