IEEE Systems, Man and Cybernetics Magazine - April 2021 - 29

ICS for Scheduling
CS [37] evolves from the habit of cuckoo parasite brooding.
Cuckoos are used to laying eggs in host nests similar to their
chicks, and they let host birds help them feed their chicks.
Once chicks are found by a host bird, the latter pushes the
cuckoo eggs out of its nest or directly abandons its nest. The
probability of being found can be expressed by Pa , with an
initial Pa = 0.25. The smaller the Pa , the stronger the global search ability of the algorithm; the larger the Pa , the
stronger the local search ability of the algorithm. To find
new nests, cuckoos may have a short-distance search and
occasionally a long-distance search. The flying path exhibits
the so-called Lévy flight, which is characterized as follows:
	

x i(tt + 1) = x i(tt) + a 5 L (m) ,(17)

where x i(tt + 1) denotes individual i in the (tt + 1) th generation. Let 5 denote entry-wise multiplications. Variable a is
the step size to control the range of random search. L (m) is
Lévy's flight search obeying the Lévy distribution below,
where u and v obey normal distribution, u ~ N (0, v 2u),
v ~ N (0, v 2v), v v = 1, and Gamma is the standard Gamma
function.
	
	

	

L (m) ~ u = tt - m, 1 < m # 3, (18)
{u
, (19)
| v | 1/m
Z
_1/m
]] Gamma (1 + m) sin a mπ k bb
2
{= [
` . (20)
m-1
1
2
] Gamma ;a m +
2 k# m # 2 Eb
\
a

L (m) =

The larger step size a , the larger the search range, and
the lower the search accuracy; the smaller a , the smaller
the search range, and the higher the search accuracy. Similarly, the smaller the discovery probability is set, the slower
the algorithm converges; on the contrary, the larger Pa is
set, the worse the accuracy of the algorithm. In response to
this defect of traditional CS, an ICS was proposed to
dynamically adjust step size a and the discovery probability Pa [38], [39]. Let TT stand for the total number of iterations and tt stand for the current number of iterations.
The found probability decreases linearly with the increase
in the number of iterations. Pa_max and Pa_min are the
maximum and minimum values of the discovery probability, respectively.
The probability of discovery and step size are updated by
	

tt
Pa (tt) = Pa_ max - TT (Pa_ max - Pa_ min) ,(21)

	

a (tt) = a_ max # exp (c # tt), (22)

	

a_ min
1
c = TT ln a a_ max k . (23)

For the ICS algorithm, the input includes the number of
nests, number of iterations, Pa_min, Pa_max, a_min, and
	

a_max. The output includes a nest position (i i, H i),
i ! " 1, 2, 3 , .
 1) Generate n parasitic initial populations by ( rs i, S i ),
i = 1, 2, 3
 2) While ( tt < Max Generation) or (Stop Criterion) do
  3)  Randomly take a cuckoo
 4) Generate solution a by flying in Lévy, a is updated by
(22) and (23)
 5) Calculate fa for solution a by (9) // Evaluate the quality
of solution a
  6)  Randomly select one solution b from n nests
 7) Calculate fb for solution b by (9)
 8) If (fa 1 fb) then
 9) Replace b with solution a
10)  End if
11) Part ( Pa ) of the bad nest is abandoned, Pa is updated
by (21)
12)  The new nest is produced by (17)
13)  Save the best solution
14)  Permutation to find the current best solution
15) Update tt " tt + 1
16)  End while
With the iteration number increasing, Pa gradually
decreases from Pa_max to Pa_min. Similarly, the step size
decreases exponentially with the number of iterations. The
step size decreases gradually and eventually flattens and
approaches a_min. The ICS algorithm works well when Pa_
min = 0.005, Pa_max = 1, a_min = 0.05, a_max = 0.5, and
m = 1.5 [39]-[41].
LSTM-RNN-Aided Scheduling
During the process of scheduling classrooms, when a student's arrival just meets the conditions of powering on one
more classroom, it will be powered on. Occasionally, the
count of the net inflow of students (the absolute value of the
difference between the inflow and outflow in a period) is
fewer than the total number of available popular seats in the
previously powered-on classrooms. This is equivalent to
meeting the needs of occupants without powering on more
classrooms. Therefore, we look forward to predicting the
number of short-term inflow students and determining
whether to power on additional classrooms.
LSTM-RNN has performed well in the short-term forecast
of electricity prices, wind speed, gas concentration, and traffic [42]-[46]. According to the comparison, the performance
of the LSTM network is better than the stack--ed autoencoder,
radial basis function, support vector machine, and autoregressive integrated moving average models, especially when
the forecast time is long [44]. Therefore, it is promising to
adopt LSTM-RNN to predict the number of inflow students in
this article. For a standard RNN architecture, the influence of
a given input on both the hidden layer and the network output either decays or blows up exponentially as it cycles
around the network recurrent connections.
In the literature, this effect is often referred to as a vanishing gradient problem [47]. The LSTM network was
Ap ri l 2021

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE	

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IEEE Systems, Man and Cybernetics Magazine - April 2021

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