Seite - 296 - in Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1561
InMOSSA, thefirst issue is settledbyequipping theSSAalgorithmwitha repositoryof food
sources. The repository can store a limitednumberofnon-dominated solutions. In theprocessof
optimization,eachsalp iscomparedwithall theresidents inrepositoryusingtheParetodominance
operators. If a salp dominates only one solution in the repository, it will be swapped. If a salp
dominatesasetof solutions in therepository, theyall shouldberemovedfromtherepositoryandthe
salpshouldbeaddedintherepository. Ifat leastoneof therepositoryresidentsdominatesasalpinthe
newpopulation, itshouldbediscardedstraightaway. Ifasalpisnon-dominatedincomparisonwithall
repositoryresidents, ithastobeaddedtothearchive. If therepositorybecomesfull,weneedtoremove
oneof thesimilarnon-dominatedsolutions in therepository. For thesecondissue,anappropriateway
is toselect it fromasetofnon-dominatedsolutionswith the leastcrowdedneighborhood.Thiscan
bedoneusing the same rankingprocess and roulettewheel selection employed in the repository
maintenanceoperator. ThepseudocodeofMOSSAisshowedinAlgorithm1:
Algorithm1.Pseudo-codeofMOSSA.
1 Set the hyper-parameter:
2 Max_iter: Maximum of iteration
3 ArchiveMaxSize: Max capacity of archive (repository)
4 Dim: The number of parameters on each salp
5 Ub and lb: The upper bound and the lower bound of salp population
6 Obj_no: The objective number to be estimated
7 Initialize the salp population L[ L Q depending on the ub and lb
8 Define the objective function (loss function): @ Ob_func
9 While (end criterion is not met)
10 Calculate the fitness of each search agent (salp) with Ob_func
11 Determine the non-dominated salps
12 Update the repository considering the obtained non-dominated salps
13 If (the repository become full)
14 Call the repository maintenance procedure to remove one repository resident
15 Add the non-dominated salp to the repository
16 End If
17 Choose a source of food from repository: F = SelectFood (repository)
18 Update c1 by O
/F
H § ·
¨
¸©
¹
19 For each salp ( L[ ):
20 If (i==1):
21 Update the position of the leading salp by:
M M M M
M
M M M M
) F XE OE F OE F
[
) F XE OE F OE F
t°
®
°¯
22 Else:
23 Update the position of the leading salp by: L L
LM
W M W M
W[
[ [
24 End If
25 End For
26 Amend the salps based on the upper and lower bound of variables
27 End While
28 Return repository
296
Short-Term Load Forecasting by Artificial Intelligent Technologies
- Titel
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Autoren
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2019
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 448
- Schlagwörter
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Kategorie
- Informatik