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Fig. 6 Production cost of the
gradient-genetic algorithm
and the fuzzy logic methods
x
10
4
12
Gradient -genetic algorithm method
Fuzzy logic method
10
8
6
4
2
0
0.8
1
1.2
1.4
1.6
x 10
5
0.4
0.6
Time (sec)
Table 4 Comparison
between the optimization
methods
Fuzzy
logic
Gradient-genetic
algorithm
Production cost ($)
29,210
27,750
Execution time (s)
7.34
12.57
It is clear that through the comparison of production costs using the fuzzy logic
by that one obtained using the genetic algorithm method, Table
4
, the fuzzy logic
approach was reliable and enabled to get a gain of 1 % of the total cost. However,
the strategy based on the use of gradient-genetic algorithm method was the most
effective and presented high performances not only in the production cost but also
in the ability of convergence to the global optimum.
We note the gradient-genetic algorithm method did not presented an ef
cient
resolution time, since it requires enough time to reach the optimal solution
depending essentially on the choice of the initial population. Indeed, based on the
above table, it is noted that the strategy based on the use of fuzzy logic method is
more ef
cient than the hybrid method in terms of execution time and ef
ciency of
convergence.
Table
5
shows the organization of the on/off states of the production units of the
various optimization strategies. Thanks to the hybrid optimization method, we were
able to organize the On/Off statements of the various production units through an
estimation of the amount of load required by the electrical grid, taking into account
the allowable constraints; optimal scheduling can pro
t of the production cost.
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