This paper extends and improves upon a previously published pandemic control model based on the Susceptible–Infected–Removed (SIR) framework with a feedback vaccination law, which established sufficient conditions for achieving herd immunity by minimizing a cost function that combines both vaccination effort and intervention time. In the original approach, optimization was performed using standard routines, which proved computationally demanding and potentially limited in high-dimensional or nonlinear scenarios. Here, we introduce an advanced Particle Swarm Optimization (PSO) strategy based on a domain-as-particle paradigm, in which the parameter space is partitioned into non-overlapping subdomains, each acting as an independent PSO particle. This structured exploration enhances both convergence speed and robustness. The hybrid framework, integrating dynamic simulation with the domain-as-particle PSO, achieves herd immunity more rapidly and efficiently compared to the original method, thereby offering improved guidance for public health policy design.