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Título
Initialization of a Multi-objective Evolutionary Algorithms Knowledge Acquisition System for Renewable Energy Power Plants
Autor
Facultad/Centro
Área de conocimiento
Cita Bibliográfica
Jarie: Journal of Applied Research on Industrial Engineering, 2018, vol. 5, n.3
Editorial
Ayandegan Institute of Higher Education, Iran
Fecha
2018-10-11
Resumen
The design of Renewable Energy Power Plants (REPPs) is crucial not only for the
investments' performance and attractiveness measures, but also for the maximization of
resource (source) usage (e.g. sun, water, and wind) and the minimization of raw
materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te)
consumption. Hence, several appropriate and satisfactory Multi-objective Problems
(MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only
be managed by very well organized knowledge acquisition on all REPPs' design
equations and models. The proposed MOPs need to be solved with one or more multiobjective algorithm, such as Multi-objective Evolutionary Algorithms (MOEAs). In this
respect, the first aim of this research study is to start gathering knowledge on the REPPs'
MOPs. The second aim of this study is to gather detailed information about all MOEAs
and available free software tools for their development. The main contribution of this
research is the initialization of a proposed multi-objective evolutionary algorithm
knowledge acquisition system for renewable energy power plants (MOEAs-KAS-FREPPs) (research and development loopwise process: develop, train, validate, improve,
test, improve, operate, and improve). As a simple representative example of this
knowledge acquisition system research with two selective and elective proposed
standard objectives (as test objectives) and eight selective and elective proposed
standard constraints (as test constraints) are generated and applied as a standardized
MOP for a virtual small hydropower plant design and investment. The maximization of
energy generation (MWh) and the minimization of initial investment cost (million €)
are achieved by the Multi-objective Genetic Algorithm (MOGA), the Niched Sharing
Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the
NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all
proposed standardized MOEAs on two desktop computer configurations (Windows 10
Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet
connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB
RAM with internet connection). The algorithm run-times (computation time) of the
current applications vary between 20.64 and 59.98 seconds.
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