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Energies2018,11, 2226
2.2. ChaoticQuantumFruitFlyAlgorithm(CQFOA)
FOAisapopulation intelligentevolutionaryalgorithmthatsimulates the foragingbehaviorof
fruitflies [26]. Fruitfliesaresuperior tootherspecies insmellandvision. In theprocessof foraging,
firstly, fruit flies rely on smell to find the food source. Secondly, they visually locate the specific
location of food and the current position of other fruit flies, and then fly to the location of food
throughpopulationinteraction.Atpresent,FOAhasbeenappliedtotheforecastingof trafficaccidents,
export trade,andotherfields [40].
2.2.1. FruitFlyOptimizationAlgorithm(FOA)
According to the characteristics of fruit flies searching for food, FOA includes the following
mainsteps.
Step1. Initializerandomlythe fruitflies’ location(X0 andY0)ofpopulation.
Step2.Give individual fruitflies therandomdirectionanddistance forsearchingfor foodbysmell, as
inEquations (8)and(9) [26]:
Xi=X0+RandomValue (8)
Yi=Y0+RandomValue. (9)
Step 3. Due to the location of food being unknown, firstly, the distance from the origin (Dist) is
estimatedas inEquation(10) [25], thenthedeterminationvalueof tasteconcentration(S) is calculated
as inEquation(11) [25], i.e., thevalue is the inverseof thedistance.
Disti= √
X2i +Y 2
i (10)
Si=1/Disti (11)
Step4.Thedeterminationvalueoftasteconcentration(S) issubstitutedintothedeterminationfunction
of tasteconcentration(orFitness function) todetermine the individualpositionof the fruitfly(Smelli),
as showninEquation(12) [26]:
Smelli=Function(Si). (12)
Step 5. Find the Drosophila species (Best index and Best Smell values) with the highest odor
concentrations in thispopulation,as inEquation(13) [26]:
max(Smelli)→ (Best_Smelli)and (Best_index). (13)
Step6. Theoptimalflavor concentrationvalue (Optimal_Smell) is retainedalongwith the x and y
coordinates (withBest_index) as inEquations (14)–(16) [25], thentheDrosophilapopulationusesvision
toflyto thisposition.
Optimal_Smell=Best_Smelli=current (14)
X0=XBest_index (15)
Y0=YBest_index (16)
Step7.Enter the iterativeoptimization, repeatSteps2 to5andjudgewhether theflavorconcentration
isbetter thanthatof theprevious iteration; if so,gobacktoStep6.
TheFOAalgorithmishighlyadaptable, so it canefficiently searchwithout calculatingpartial
derivativesof the target function. Itovercomes thedisadvantageof trapping into localoptimaeasily.
However,asaswarmintelligenceoptimizationalgorithm,FOAstill tends to fall intoa localoptimal
solution,dueto thedecliningdiversity in the lateevolutionarypopulation.
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Short-Term Load Forecasting by Artificial Intelligent Technologies
- Title
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Authors
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Editor
- MDPI
- Location
- Basel
- Date
- 2019
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Size
- 17.0 x 24.4 cm
- Pages
- 448
- Keywords
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Category
- Informatik