MOEAD

Apr 9, 20150 likes784 views

Zhang, Qingfu, and Hui Li. "MOEA/D: A multiobjective evolutionary algorithm based on decomposition." Evolutionary Computation, IEEE Transactions on 11.6 (2007): 712-731.

Background 1
Tchebycheff approach
Heaven

Background 2 ~ weighted approach
I am not sure how
then generate these
contours!

Background 3 ~ core concept
● neighborhood - defined based on the
distances
● solutions to two neighboring subproblems
should be very similar
● subproblem is optimized in MOEA/D by using
its neighboring subproblems

Initialization
Randomly generated points

Initialization
Randomly generated points
Starts out as 1e30

Initialization
Randomly generated points
Randomly assigned weights
Starts out as 1e30

Initialization
Randomly generated points
Randomly assigned weights
Starts out as 1e30
Use only weights

Initialization
Randomly generated points
Randomly assigned weights
Starts out as 1e30
Use only weights
Use the distance matrix to
find T neighbors

This document discusses monopole lensing and how it relates to Lorentz symmetry breaking. Monopole lensing is the phenomenon where the speed of light varies at different points in space due to a monopole, which is similar to gravitational lensing. The author sets up a mathematical model in Mathematica to study monopole lensing by modeling the monopole field profile and using Euler-Lagrange equations. Interestingly, the model results in an unexplained bump that persists even when trying to improve accuracy. Future work could look more into detecting monopoles and further investigating the cause and properties of the light bending bump.

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MOEAD

1. MOEA/D Qingfu Zhang and Hui Li

2. Background 1 Tchebycheff distance

3. Background 1 Tchebycheff approach

4. Background 1 Tchebycheff approach Heaven

5. Background 1 Tchebycheff approach

6. Background 2 ~ weighted approach

7. Background 2 ~ weighted approach I am not sure how then generate these contours!

8. Background 3 ~ core concept ● neighborhood - defined based on the distances ● solutions to two neighboring subproblems should be very similar ● subproblem is optimized in MOEA/D by using its neighboring subproblems

9. Initialization

10. Initialization Randomly generated points

11. Initialization Randomly generated points Starts out as 1e30

12. Initialization Randomly generated points Randomly assigned weights Starts out as 1e30

13. Initialization Randomly generated points Randomly assigned weights Starts out as 1e30

14. Initialization Randomly generated points Randomly assigned weights Starts out as 1e30 Use only weights

15. Initialization Randomly generated points Randomly assigned weights Starts out as 1e30 Use only weights Use the distance matrix to find T neighbors

16. Main Loop

17. Main Loop

18. Main Loop

19. Main Loop

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MOEAD

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