FA23_CEE434_HW4
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University of Illinois, Urbana Champaign *
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Course
434
Subject
English
Date
Jan 9, 2024
Type
Pages
7
Uploaded by UltraExploration8833
CEE 434 Environmental Systems I, Fall 2023 Homework #4 (GA, Reservoir Modeling) Due Nov. 19
th
Problem 1 (60 points). Recall the water allocation example that we discussed in the class (GA Lecture). The problem is to determine the optimal values of diversions to the three firms located along the river to maximize the total net benefits obtained from the firms. The total amount of water available for the firms is limited to 50% of the river flow in the upstream (Q). i)
(10 points) Formulate the optimization problem.
ii)
(20 points) Assume that ࠵? = 16
. Use the MATLAB function and script provided for that example and solve the optimization problem using the GA parameters in the table below (do not change other parameters in the options). For each case presented in the table below, run the GA three times and complete the solution tables (see next page). N (Population Size) G (generations) Crossover Fraction Case 1 4 5 0 Case 2 4 10 0 Case 3 4 10 0.20 Case 4 4 10 0.80 Case 5 10 50 0.20 Case 6 10 50 0.80 Case 7 20 100 0.20 Case 8 20 100 0.80 iii)
(10 points) Discuss the impact of GA parameters (N, G, Crossover fraction) iv)
(10 points) Based on your results, pick the best combination of GA parameters, and solve the optimization problem for ࠵? = 8
, and ࠵? = 32
.
Discuss the impact of river flow constraint on each firm’s net benefit and total net benefit.
v)
(5 points) Assume that you are NOT able to insert the river flow constraint into GA formulation in MATLAB. Suggest another way that you can account for the constraint in GA optimization.
Problem I
i
NB
M
6x_x
2
N13242
7
ㄨ
2
15
ㄨ
22
NB
了
⼼
8
ㄨ
3
05
ㄨ
了
2
objective
function
max
È
N
Bi
Xi
constraints
xtxvtx.CO5Q
ii
see
below
iii
The
optimization
solution
becomes
more
converged
as
N
and
G
increase
Excessive
crossover
fraction
maydisrupt
potential
good
solutions
before
they
dominate the
population
while
a
too
small
crossover
fraction
can
result
in
the
loss
of
valuable
solutions
iv
N
50
G
100 crossover
fraction
o
2
Q
8
254102
25.4105
05017
0.6035
28958
25.4102
25.4097
⼼
4106
o.mg
29685
v5.4094
25.4016
0.3964
00481
29564
以
4073
5.4073
a
2
49.1607
49.1607
3
以
8
49.1607
49.1607
3
2.33
8
49.1607
49.1607
233
8
49.1607
49.1607
Qj
the influence
of
1
unit
of
flow
constraint
on
net
benefit
and total
benefit
l
u
vi
The
goal
of
GA
is
to
reach
the
optimal
solution
through
iterative
process
while
NLP
directly
solves
problems
DP
aims
to
find
the
best
solution
at
each
stage
b
GA
can
handle
complex
large
scale
non
linear
systems
more
efficiently
and with
less
complexity
although
it
may
sacrifice
some
accuracy
NLP
hrs
high
precision
but
involves
complex
calculations
叩
is
easily
calculated
manually
but
for
super
large
systems
it
becomes
intricate
and
generates
excessive
data
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