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Fig. 3.
Convergence characteristic of the worst programming model
α
=
0.7
Fig. 2 and 3 illustrate the evolution curve of the two models. In each figure, curve
F_gbest
is the optimal value of objective, which shows the best frog in each iteration;
curve
Avg_fit
is the mean value of the objective of all frogs in memeplexes. Along
with the increases of evolutionary generation, the curve gradually tends to a stable
value, which indicating that the convergence of the method. When
α
is 0.7, the
optimal objective of the optimal programming is 2280.5, and the processing sequence
is [6 8 9 2 1 7 4 10 5 3]; the optimal objective of the worst programming is 3569.5,
and the processing sequence is [7 2 1 6 8 9 4 10 5 3].
The parameter selection is critical to ISFLA performance. ISFLA has four
parameters: the number
M
of memeplexes, the number
N
of frogs in a sub-memeplex,
the number
N
max
of local evolution in a sub-memeplex and the maximum step size
S
max. In the test, the maximum step size
S
max is devised to be the same with the
number of products, so the other three parameters will be determined through
extensive experiments. Some benchmark instances with different sizes have been
selected to test, which have the objective of makespan. For each test problem, a total
of 10 run with different combinations of the parameters. Firstly, the frog memeplex is
fixed to 10, and each sub-memeplex has 20 frogs. The local exploration for each sub-
memeplex is set to 15, 30, 50, 75, and 100. And we defined the maximal deviation
MDP in this paper as follows,
opt
_
max
−
opt
_
min
MDP
=
×
100
%
(6)
opt
_
min
Supposed
opt_max
be the maximum objective value found in the optimization
computation,
opt
_
min
be the minimum objective. MDP can indicate the extent of
scatter of the objective set. Table 2 illustrates the comparison of different local
exploration iteration for every sub-memeplex. From the table, when
N
max is set to15
or 75,100, the MDP value is not better than that of 30.
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