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x 2
NP Parameter vectors from generation G
Newly generated parameter vector v
Fx r 2 , G x r 3 , G
(
)
Minimum
x i , G
x best , G
x r 3 , G
v
x r 2 , G
(
)
v = x i , G + x best x i , G
x 1
Fig. 1.5. Contour lines and the process for generating v in scheme DE3.
x ( G )
+ F
x ( G )
r 2
v = x ( G )
i
x ( G )
i
x ( G )
r 3
+
λ
best
(1.12)
introducing an additional control variable
is to provide a means
to enhance the greediness of the scheme by incorporating the current best vector x ( G )
λ
. The idea behind
λ
best .
This feature can be useful for non
critical objective functions. Fig 1.5 illustrates the
vector
generation process defined by Equation 1.12. The construction of u from v and
as well as the decision process are identical to DE2.
DE Strategies
[1] have suggested ten different working strategies of DE and some guidelines in ap-
plying these strategies for any given problem (see Table1.2). Different strategies can be
adopted in the DE algorithm depending upon the type of problem for which it is ap-
plied. The strategies can vary based on the vector to be perturbed, number of difference
vectors considered for perturbation, and finally the type of crossover used.
The general convention used above is as follows: DE/x/y/z. DE stands for differen-
tial evolution algorithm, x represents a string denoting the vector to be perturbed, y is
the number of difference vectors considered for perturbation of x ,and z is the type of
crossover being used. Other notations are exp: exponential; bin: binomial). Thus, the
working algorithm outline by [2] is the seventh strategy of DE, that is, DE/rand/1/bin.
Hence the perturbation can be either in the best vector of the previous generation or in
any randomly chosen vector. Similarly for perturbation, either single or two vector dif-
ferences can be used. For perturbation with a single vector difference, out of the three
distinct randomly chosen vectors, the weighted vector differential of any two vectors
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