An inner-point modification of PSO for constrained optimization
In the last two decades, PSO (Particle Swarm Optimization) gained a lot of attention
among the different derivative-free algorithms for global optimization. The simplicity of the
implementation, compact memory usage and parallel structure represent some key features,
largely appreciated. On the other hand, the absence of local information about the objective
function slow down the algorithm when one or more constraints are violated, even if a
penalty approach is applied.






