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Algorithm 1 Pseudocode of the ISBOA |
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1:
Initialize problem settings: , , , (N), (T), (t)
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2:
Initialize the population randomly
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3:
for to T do
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4:
Update Candidate Solution
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5:
The Cossboo cuckoo mutated:
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6:
for to N do
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7:
Compute the Gaussian mutation of the ith Candidate Solution using Equation (13)
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8:
Calculate the cuckoo’s random move step length for the ith Candidate Solution using Equations (14) and (15).
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9:
Calculate and update the new state of the ith Candidate Solution using Equations (16) and (17).
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10:
end for
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11:
SBOA main part:
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12:
for to N do
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13:
if then
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14:
Calculate new status of the ith Candidate Solution using Equation (18)
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15:
Update the ith Candidate Solution using Equation (17)
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16:
else if then
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17:
Calculate new status of the ith Candidate Solution using Equation (19)
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18:
Update the ith Candidate Solution using Equation (17)
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19:
else
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20:
Calculate new status of the ith Candidate Solution using Equation (20)
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21:
Update the ith Candidate Solution using Equation (17)
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22:
end if
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23:
end for
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24:
for to N do
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25:
if then
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26:
Calculate new status of the ith Candidate Solution using in Equation (22)
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27:
else
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Calculate new status of the ith Candidate Solution using in Equation (22)
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end if
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30:
Update the ith Candidate Solution using Equation (17)
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31:
end for
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32:
Smooth Exploration System:
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33:
for to N do
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34:
Calculate the sampling rate of ith Candidate Solution using Equation (23)
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Update the ith Candidate Solution via random crossover in Equation (24).
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36:
Update the ith Candidate Solution via sequence mutation in Equation (25).
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end for
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38:
Save the best candidate solution so far
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39:
end for
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40:
Output: The best solution obtained by ISBOA for the given optimization problem return Best solution
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