![](bg6.png)
Table 3: Average of minimum values obtained over 25 trials with RO function (D = 30, population size 50, CR = 0.5).
Strategy
Scaling factor F
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
DE-PSO-APGA1 - - - - - - - - - -
DE-PSO-APGA2 0 0 0 0 0 0 0 - - -
DE-PSO-APGA3 2.96e-30 5.06e-30 2.9e-197 2.9e-197 2.8e-197 0 0 - - -
DE-PSO-APGA4 0 0 0 0 0 - - - - -
DE-PSO-APGA5 - - - - - - - - - -
DE-PSO-APGA6 5.31e-30 1.6e-118 - - - - - - - -
DE-PSO-APGA7 - - - - - - - - - -
DE-PSO-APGA8 - - - - - - - - - -
Table 4: Results by DE-PSO-APGA with population size
100 (F = 0.1, CR = 0.5). Mean indicates average of mini-
mum values obtained, ”Std.” stands for standard deviation.
Func Dim. Max Gen. Mean (Std.)
RA
30 500 0.00e+00 (0.00e+00)
100 1500 0.00e+00 (0.00e+00)
RI
30 500 0.00e+00 (0.00e+00)
100 1500 0.00e+00 (0.00e+00)
GR
30 500 0.00e+00 (0.00e+00)
100 1500 0.00e+00 (0.00e+00)
AC
30 500 4.44e-16 (0.00e+00)
100 1500 4.44e-16 (0.00e+00)
RO
30 500 0.00e+00 (0.00e+00)
100 1500 0.00e+00 (0.00e+00)
Table 5: Comparison results of PSO, DE, DE-PSO and DE-
PSO-APGA (D = 30, population size 30, max generation
3000).
Func PSO DE DE-PSO
DE-PSO
-APGA
RA
37.82 2.531 1.614 0
(7.456) (5.19) (3.885) (0)
RI
- - - 0
- - - (0)
GR
0.018 0 0 0
(0.023) (0) (0) (0)
AC
1.0e-08 7.3e-15 3.7e-15 4.4e-16
(1.9e-08) (7.7e-16) (0) (0.0e+0)
RO
81.27 31.14 24.2 0
(41.22) (17.12) (12.31) (0)
ficient performance on many benchmark problems.
5 CONCLUSIONS
To overcome the computational complexity, a new
strategy using DE for Adaptive Plan system of PSO
with GA called DE-PSO-APGA has been proposed to
solve a huge scale optimization problem, and to im-
prove the convergence to the optimal solution. Then,
we verify the effectiveness of the DE-PSO-APGA by
the numerical experiments performed five benchmark
functions.
We can confirm that the DE-PSO-APGA dramat-
ically reduces the calculation cost and improves the
convergence towards the optimal solution.
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