Multi-objective optimization of an evaporator control system using surrogate modeling

This paper discussed the idea of using surrogate modeling to optimize a multi-objective problem which in this case are PID controllers of an evaporator system. The main emphasis of this approach is to approximate a set of controller parameters from a few sample and search for Pareto front. The Radial Basis Function Neural Network (RBFNN) used was able to give a good approximation to the Preto front of controller parameters and perform four times faster compare by using brute-force search approach.