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Open AccessArticle

Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison

1
Artificial Intelligence Research Center, University of Veracruz, Sebastián Camacho 5, Col. Centro, Xalapa 91000, Veracruz, Mexico
2
Naturally Inspired Computation Research Group, Department of Computer Science, Maynooth University, Maynooth W23 F2K8, Kildare, Ireland
*
Author to whom correspondence should be addressed.
Math. Comput. Appl. 2020, 25(2), 32; https://doi.org/10.3390/mca25020032
Received: 21 April 2020 / Revised: 12 June 2020 / Accepted: 15 June 2020 / Published: 16 June 2020
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications)
This work presents the assessment of the well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and one of its variants to optimize a proposed electric power production system. Such variant implements a chaotic model to generate the initial population, aiming to get a better distributed Pareto front. The considered power system is composed of solar, wind and natural gas power sources, being the first two renewable energies. Three conflicting objectives are considered in the problem: (1) power production, (2) production costs and (3) CO2 emissions. The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is also adopted in the comparison so as to enrich the empirical evidence by contrasting the NSGA-II versions against a non-Pareto-based approach. Spacing and Hypervolume are the chosen metrics to compare the performance of the algorithms under study. The obtained results suggest that there is no significant improvement by using the variant of the NSGA-II over the original version. Nonetheless, meaningful performance differences have been found between MOEA/D and the other two algorithms. View Full-Text
Keywords: Multi-Objective Evolutionary Algorithm; power production; renewable energies Multi-Objective Evolutionary Algorithm; power production; renewable energies
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MDPI and ACS Style

Vargas-Hákim, G.-A.; Mezura-Montes, E.; Galván, E. Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison. Math. Comput. Appl. 2020, 25, 32. https://doi.org/10.3390/mca25020032

AMA Style

Vargas-Hákim G-A, Mezura-Montes E, Galván E. Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison. Mathematical and Computational Applications. 2020; 25(2):32. https://doi.org/10.3390/mca25020032

Chicago/Turabian Style

Vargas-Hákim, Gustavo-Adolfo; Mezura-Montes, Efrén; Galván, Edgar. 2020. "Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison" Math. Comput. Appl. 25, no. 2: 32. https://doi.org/10.3390/mca25020032

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