Evolutionary Multi-Objective Algorithm to effectively improve the performance of the classic tuning of fuzzy logic controllers for a heating, ventilating and Air Conditioning system

In this work, we present an advanced Multi-Objective Genetic Algorithm for obtaining more compact fuzzy logic controllers as the way to find the best combination of rules, thus improving the system performance in a problem to control a Heating, Ventilating, and Air Conditioning system. To this end, two objectives have been considered, maximizing the performance of the system (involving energy performance, stability and indoor comfort requirements) and minimizing the number of rules obtained (for finding the most cooperative/accurate rule subset).