{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:59:49Z","timestamp":1767920389176,"version":"3.49.0"},"reference-count":12,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T00:00:00Z","timestamp":1613779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function\u2019s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory\u2019s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.<\/jats:p>","DOI":"10.3390\/sym13020344","type":"journal-article","created":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T21:15:01Z","timestamp":1613942101000},"page":"344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8054-6510","authenticated-orcid":false,"given":"Alejandro Humberto","family":"Garc\u00eda Ruiz","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Tamaulipas, Tampico 89339, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salvador","family":"Ibarra Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Tamaulipas, Tampico 89339, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Antonio","family":"Cast\u00e1n Rocha","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Tamaulipas, Tampico 89339, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jes\u00fas David","family":"Ter\u00e1n Villanueva","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Tamaulipas, Tampico 89339, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julio","family":"Laria Menchaca","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Tamaulipas, Tampico 89339, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mayra Guadalupe","family":"Trevi\u00f1o Berrones","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Tamaulipas, Tampico 89339, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2496-0009","authenticated-orcid":false,"given":"Mirna Patricia","family":"Ponce Flores","sequence":"additional","affiliation":[{"name":"Graduate Program Division, Tecnol\u00f3gico Nacional de M\u00e9xico, Madero 89440, Mexico"},{"name":"Graduate Program Division, Instituto Tecnol\u00f3gico de Ciudad Madero, Cd., Madero 89440, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3265-8531","authenticated-orcid":false,"given":"Aurelio Alejandro","family":"Santiago Pineda","sequence":"additional","affiliation":[{"name":"Information Technology Engineering, Polytechnic University of Altamira, Altamira 89602, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11916","DOI":"10.3390\/en81011916","article-title":"A Solution Based on Bluetooth Low Energy for Smart Home Energy Management","volume":"8","author":"Collotta","year":"2015","journal-title":"Energies"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Attoue, N., Shahrour, I., and Younes, R. (2018). Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting. Energies, 11.","DOI":"10.20944\/preprints201801.0051.v1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"123673","DOI":"10.1109\/ACCESS.2020.2994119","article-title":"Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Trean\u0163\u0103, S. (2021). On a Class of Constrained Interval-Valued Optimization Problems Governed by Mechanical Work Cost Functionals. J. Optim. Theory Appl., 1\u201312.","DOI":"10.1007\/s10957-021-01815-0"},{"key":"ref_5","unstructured":"Trean\u0163\u0103, S. (2020). Efficiency in uncertain variational control problems. Neural Comput. 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