Framework of model selection criteria approximated genetic programming for optimization function for renewable energy systems
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- @Article{GARG:2020:swarm,
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author = "Akhil Garg and Shaosen Su and Fan Li and Liang Gao",
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title = "Framework of model selection criteria approximated
genetic programming for optimization function for
renewable energy systems",
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journal = "Swarm and Evolutionary Computation",
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volume = "59",
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year = "2020",
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pages = "100750",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Model
selection criteria, Objective function approximation,
Renewable energy systems",
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ISSN = "2210-6502",
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URL = "http://www.sciencedirect.com/science/article/pii/S221065022030403X",
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DOI = "doi:10.1016/j.swevo.2020.100750",
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abstract = "For the realization of complex renewable energy
systems (such as nano-fluids based direct absorption
solar collector), an evolutionary system identification
method such as genetic programming (GP) can be applied
to develop mathematical models/functional relationships
between the process parameters. The system complexity
is attributed to interaction among the design variables
influencing the outputs. There are also uncertainties
in the system due to random and unknown variations in
the design and response variables. GP suffers from the
higher complexity structure of its solutions and
non-optimal convergence, which leads to poor fitness
values. Therefore, to address these uncertainties and
problems, the framework based on the model selection
criteria approximated genetic programming (MSC-GP) is
proposed for the formulation of geometry design based
thermal efficiency and entropy generation optimization
function for direct absorption solar collector (DASC)
system. In this proposed method, the four mathematical
model selection criteria are used as an approximation
for objective functions in GP framework for the
evaluation of fitting degree and structure of the
model. The results based on statistical measures (best
fitness, mean fitness, standard deviation of fitness,
number of nodes) show that models obtained from the
mathematical selection criteria, Predicted Residual
error sum of squares (PRESS), have performed the best.
Based on Pareto front analysis of PRESS function, it is
found that the best objective values and the number of
nodes of models (complexity) follows more or less
gradually slow increasing trend which is a good
symbolic desirable sign of minimal increase of
complexity of model with a decrease in objective values
as the values of generation increases. The results of
the sensitivity analysis show that the main factor
affecting the efficiency of DASC is its geometry of the
structure. 3-D interaction analysis shows that
increasing the thickness, length and reducing the width
of the collector can make the system maintain its
higher thermal efficiency and a smaller entropy
generation, which is useful for the optimized operation
of DASC. Non-dominated sorting genetic algorithm-II
(NSGA-II) is applied in the acquisition of the optimal
geometric settings of DASC system based on the selected
models. The optimal settings achieved is 5 cm in
length, 5 cm in width, and 2 cm in thickness. Systems
when operated using these settings results in a
satisfactory performance with 77.8117percent in thermal
efficiency and 6.0004E+3 in entropy generation)",
- }
Genetic Programming entries for
Akhil Garg
Shaosen Su
Fan Li
Liang Gao
Citations