Adaptive dynamic ϵ-simulated annealing algorithm for tumor immunotherapy

Unlike traditional metaheuristics such as PSO or GA, which are less suited for constraint-rich, dynamic ODE-based systems, ADϵSA offers structural advantages in trajectory feasibility and adaptive convergence. This study highlights the potential of biologically informed optimization algorithms in personalized oncology and provides a computational basis for future closed-loop, patient-specific treatment strategies.