applying evolutionary algorithm to chaco tool on the partitioning of power transmission system...
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Applying Evolutionary Algorithm to Chaco Tool on the Partitioning of Power Transmission System
(CS448 Class Project)
Yan Sun
Problem Statement
Overheads in Maxflow Calculation need to be minimized
Partition the Power Transmission System (PTS) using Chaco
An optimal set of parameters for Chaco
Chaco
Developed by Bruce Hendrickson at Sandia National Lab
Available partitioning methods Inertial Spectral Kernighan-Lin Multilevel KL
Chaco Parameters
Debugging Parameters Execution Parameters Extended Functionality Parameters
Previous Experimentations
Austin and Brian’s experiments # partitions – 5 or 6 Degree as vertex weight 200 – 400 external message counts
Experimental Procedure
Download and install Maxflow Run Chaco Take output from Chaco and create
XML file Run Maxflow
EA Details -- Parameters
# partitions 5 6
# coarsening to 50 20
Partition method Bisection Quadrisection
EA Details
Representation— array of 297 integers first 99 next 198 Both vertex weights and edge weights
Objective Function— number of message passed across partitions
Fitness Function—negative value of Object Function
EA Details
Population Size = 20 Random Initialization
Offspring Size = 6
Parent Selection Tournament
EA Details
Recombination Mutation Survivor Selection
Deterministic, Elitist, Steady State Termination Condition
Max # of generations No improvement Best solution found
Parameter Sets
# Partitions # Coarsening to
Partition
Method Para 1 5 50 Bisection
Para 2 5 50 Quadrisection
Para 3 5 20 Bisection
Para 4 5 20 Quadrisection
Para 5 6 50 Bisection
Average Fitness Values
Para 1 Para 2 Para 3 Para 4 Para 5
Terminating Average Fitness
-130.72 -138.32 -150.72 -134.26 -148.76
Fitness vs. Generations
-350
-300
-250
-200
-150
-100
-50
0
gene
ratio
n 0
gene
ratio
n 10
gene
ratio
n 20
gene
ratio
n 30
gene
ratio
n 40
gene
ratio
n 50
gene
ratio
n 60
gene
ratio
n 70
gene
ratio
n 80
gene
ratio
n 90
gene
ratio
n 100
gene
ratio
n 110
gene
ratio
n 120
gene
ratio
n 130
gene
ratio
n 140
gene
ratio
n 150
generations
fitn
ess
valu
es
Para1
Para2
Para3
Para4
Para5
Wilcoxon Rank-Sum Test
Wilcoxon Rank-Sum Test
Wilcoxon Rank-Sum Test
Wilcoxon Rank-Sum Test
Wilcoxon Rank-Sum Test
# Generations to Reach Best Fitness
0
20
40
60
80
100
120
# generations to reach best fitness
values
Para1 Para2 Para3 Para4 Para5
parameter sets
Series1
Wilcoxon Rank-Sum Test
Conclusion
No difference found among parameter sets
Fewer external message counts 130-150 vs 200-400 Better partition?
Problem
Non-deterministic evaluation results
population average fitness value
Q/A?