  A self-organizing particle swarm is presented. It works in dissipative state
by employing the small inertia weight, according to experimental analysis on a
simplified model, which with fast convergence. Then by recognizing and
replacing inactive particles according to the process deviation information of
device parameters, the fluctuation is introduced so as to driving the
irreversible evolution process with better fitness. The testing on benchmark
functions and an application example for device optimization with designed
fitness function indicates it improves the performance effectively.
