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a
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Gi cee را
Optimal solutions
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MOO
Methods
سم
Pareto Methods
(a-posterin articulation of preference)
مد ]| رسمه
Algocits سولق
MOEA
Evohuionay Ale
MOGA
(Mubi-Objective Gen Ale)
NSGA, NSGA-IT
(Non-dominated Sorting Gen, Ala
SPEA, SPEA2
(Strengtiren Parco Evoktionary Als.)
NBI
(Normal Boundary Intersection)
M
دزم 00
NPGA
Pareto Gen. Ala)
on-Pareto, non-sealarization|
VEGA]
(Vector Evaiating Gen, Al)
Scalarization Methods
(«priori articulation of preference)
WSA
(Weighted Sum Approach)
GAM
(Goal Attainment Method)
Texicographic Method
‘e-constraint Method
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/ دک وم وولو
7
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چگونگی حرکات فرات ؟
حركت ذزه در راستاى قبلى
حركت ذره در راستاى بهترين 7
8 :
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همسایگان
۱ oa
5 5
اه
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ee
بردار تصمیم ۶ متعلق
مجاز آبك باسج
از را بردار و
مانند 1 ۳۱۱۰۰۰
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© Pareto-optimal solutions
© Feasible solutions
@ Non-feasible solutions
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وال
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. Select particles for next iteration.
If PFront size >N, then randomly select N particles trom PFront and store them as
NeatX. Otherwise, store PFront as NextX then randomly select particles in next
front (Fronik) and add them to NexiX until NextX size=N.
. Calculate objective functions values for all particles in NextX and set the NextX as
the current positions x for the next iteration.
- [fall ¥j<0.1Vi_qy eXecute the following steps, otherwise go to 8).
a) Randomly select 20% particles in current population and randomly change their
positions by 10% of the Vyyqy- Store them in Xtemp.
b) Evaluate the Xremp and find the particles which dominate any particles in the
current Pareto front. Use these dominating particles to replace the corresponding
particles in the current.x.
Repeat a) and b) K times (K=1~10), make sure the number of x dose not exceed رم
N.
. If t< Ny, go to 2).
. Store the non-dominated solutions from the final population and calculate the per-
formance metric values (see equations (8) and (9) in the next section).
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fil)
gle:
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رای متس رل PLD seed
07
ناحيه اول
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طراحي جاذب
الکترومغناطيسي
١ نوع ماده (ضریب انعکاس)
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