Optimized drug regimen and chemotherapy scheduling for cancer treatment using swarm intelligence

Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm

“…The multi-objective algorithms compared in this study include classic algorithms that have been widely used in optimizing chemotherapy doses in recent years, such as the Nondominated Sorting Genetic Algorithm II (NSGA-II) [4,13,22], the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO) [13,16,38], as well as the improved Two Archive algorithm (Two_Arch2) [33], the Constrained Two-Archive Evolutionary Algorithm II (C-TAEA-II) [39] for constrained multi-objective optimization, and the original Multi-Objective Squirrel Search Algorithm (MOSSA) [37]. The latter three are included to illustrate the performance improvements of the TA-MOSSA.…”

“…In order to determine the chemotherapy dosage while scheduling the self-recovery period for the patients, Dhieb et al [16] chose a mathematical model that can simulate the tumor cells and the immune system cells, and they developed a chemotherapy schedule for the patients by taking into account the duration of the treatment, the resting time, and the dosage of the drugs. Additionally, the amount of the chemotherapy dosage was calculated by the linear optimal controller, while the time scheduling was conducted by the particle swarm optimization algorithm (PSO), in which the tumor growth model is in a Logistic form.…”

“…Qods et al [22] used the Genetic Algorithm (GA) to solve the optimal control problem of cell cycle-specific chemotherapy dosing, again using the total number of tumor cells at the end of the regimen as the optimization objective, to find the optimal drug accumulation and drug dosage. Dhieb et al [16] chose the Particle Swarm Optimization (PSO) algorithm to minimize the duration of the chemotherapy course and the tumor size as the optimization objectives, and the two objectives were aggregated in a weighted manner. The fractional order internal mode (IMC FOPI) controller closed-loop multidrug scheduling model proposed by Pachauri et al [14] consists of a random walk Moth-flame Optimization Algorithm (MFOA) to optimize the design parameters, with the optimization objective set as the total number of drug-sensitive and drug-resistant tumor cells at the end of the regimen.…”