Appl Intell DOI 10.1007/s10489-010-0264-x A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems María José Gacto · Rafael Alcalá · Francisco Herrera © Springer Science+Business Media, LLC 2010 Abstract This paper focuses on the use of multi-objecti- ve evolutionary algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ven- tilating and air conditioning systems, energy performance, stability and indoor comfort requirements. This problem presents some specific restrictions that make it very par- ticular and complex because of the large time requirements needed to consider multiple criteria (which enlarge the so- lution search space) and the long computation time models required in each evaluation. In this work, a specific multi-objective evolutionary algo- rithm is proposed to obtain more compact fuzzy logic con- trollers as a way of finding the best combination of rules, thus improving the system performance to better solve the HVAC system control problem. This method combines lat- eral tuning of the linguistic variables with rule selection. To this end, two objectives have been considered, maximizing the performance of the system and minimizing the number of rules obtained. This algorithm is based on the well-known SPEA2 but uses different mechanisms for guiding the search towards the desired Pareto zone. Moreover, the method im- Supported by the Spanish Ministry of Education and Science under grant no. TIN2008-06681-C06-01. M.J. Gacto () Dept. Computer Sciences, University of Jaén, Jaén, Spain e-mail: mjgacto@ugr.es R. Alcalá · F. Herrera Dept. Computer Science and Artificial Intelligence, University of Granada, Granada, Spain R. Alcalá e-mail: alcala@decsai.ugr.es F. Herrera e-mail: herrera@decsai.ugr.es plements some advanced concepts such as incest prevention, that help to improve the exploration/exploitation trade-off and consequently its convergence ability. The proposed method is compared to the most represen- tative mono-objective steady-state genetic algorithms previ- ously applied to the HVAC system control problem, and to generational and steady-state versions of the most interest- ing multi-objective evolutionary algorithms (never applied to this problem) showing that the solutions obtained by this new approach dominate those obtained by these methods. The results obtained confirm the effectiveness of our ap- proach compared with the rest of the analyzed methods, ob- taining more accurate fuzzy logic controllers with simpler models. Keywords Heating, ventilating, and air conditioning systems · HVAC systems · Fuzzy logic controllers · Genetic tuning · Linguistic 2-tuples representation · Rule selection · Multi-objective evolutionary algorithms 1 Introduction In EU countries, primary energy consumption in buildings represents about 40% of total energy consumption, and de- pending on the countries, more than half of this energy is used for indoor climate conditions. From a technological point of view, it is estimated that the consideration of spe- cific technologies like building energy management systems (BEMSs) can save up to 20% in the energy consumption of the building sector. With this aim, BEMSs are generally ap- plied only to the control of active systems, i.e., Heating, Ven- tilating, and Air Conditioning (HVAC) systems. HVAC sys- tems consist of complex equipment usually implemented for