1. Parallel Cooperative
Optimization Research
Group
ParadisEO-MO : software framework for single solution-based metaheuristics
Laboratoire d’Informatique Fondamentale de Lille

2. Outline Algorithms in ParadisEO-MO 1.1 :
Hill climbing
Simulated Annealing
Tabu search
Iterated local search
Hybridization with ParadisEO-EO.
What’s next in ParadisEO-MO 1.2 ?

3. Design concepts
Single solution metaheurisitcs
 neighbourhood exploration.
Generating another solution ?
 disturbing the current solution
 make a movement.
Base of ParadisEO-MO = moMove.

4. Delta = - d(2,1) – d (5,3) + d(2, 5) + d(1, 3)
Move chosen : Two-Opt
Two points within the string are selected and the segment between them is inverted. This operator put in two new edges in the tour. 4 4 2 1 5 3 4 1 1 2 5 1 3 4 2 5 2 5 3 3
Delta = - d(2,1) – d (5,3) + d(2, 5) + d(1, 3)

5. Available algorithms Tabu Search Simulated Annealing Iterated
local search
Hill
Climbing

6. Hill Climbing

7. Designing a Hill Climbing
Designing a move operator, its features.
Designing/implementing the operator to build the first move (and implicitly the first neighbor).
Designing/implementing the operator to update a given move to its successor.
Designing/implementing the incremental evaluation function.
Choosing the neighbor selection strategy.
No continuation criterion (stopping as a local optimum is reached).

8. Move selection strategies
Hill Climbing class
To build
the next move
Full evaluation
function
To build
the first move
To compute
the fitness delta
Move selection strategies

9. Move selection strategies
Hill Climbing class
To build
the next move
To build
the first move
To compute
the fitness delta
Move selection strategies

10. Move selection strategies
Hill Climbing class
To build
the next move
To compute
the fitness delta
Move selection strategies

11. Move selection strategies
Hill Climbing class
To compute
the fitness delta
Move selection strategies

12. Move selection strategies
Hill Climbing class
Move selection strategies

13. Hill Climbing class

14. Hill Climbing class

15. Simulated Annealing

16. How can a Simulated Annealing be built ?
Could be reused from Hill Climbing
Designing a move operator, its features.
Designing/implementing the operator to build a random candidate move.
Designing/implementing the incremental evaluation function.
Choosing the cooling schedule strategy.
Independent of the tackled problem

17. Simulated Annealing class
Full evaluation
function
To compute the fitness delta
Stopping criterion between two temperature updates
Random move
generator
Cooling schedule strategy

18. Simulated Annealing class
Stopping criterion between two temperature updates
Random move
generator
Cooling schedule strategy

19. Simulated Annealing class
Stopping criterion betwenn two temperature updates
Cooling schedule strategy

20. Simulated Annealing class
Stopping criterion between two temperature updates

21. Simulated Annealing class

22. Simulated Annealing class

23. Tabu Search

24. How can Tabu Search be built ?
Design a move operator, its features.
Design/implement the operator to build the first move (and implicitly the first neighbor).
Design/implement the operator to update a given move to its successor.
Design/implement the incremental evaluation function.
Choosing the Tabu List.
Choosing the aspiration criterion.
Choosing the continuation criterion.
Could be reused from Hill Climbing
Independent of the tackled problem

25. Tabu Search class To build the next move Full evaluation function
To build the first move
To compute the fitness delta
Aspiration criterion
Continuation criterion
Tabu List

26. Continuation criterion
Tabu Search class
Aspiration criterion
Continuation criterion
Tabu List

27. Continuation criterion
Tabu Search class
Continuation criterion
Tabu List

28. Continuation criterion
Tabu Search class
Continuation criterion

29. Tabu Search class

30. Tabu Search class

31. Iterated local search

32. Designing an iterative local search
Design/implement the operator to make a perturbation on a solution.
Design/implement a local search
Choosing the solution comparator.
Choosing the continuation criterion.
Could be reused from Hill Climbing
Independent of the tackled problem

33. Iterated local search class
A solution
perturbation
A local search
A solution
comparator
A stopping
criterion

34. Iterated local search class
A local search
A solution
comparator
A stopping
criterion

35. Iterated local search class
A solution
comparator
A stopping
criterion

36. Iterated local search class
A stopping
criterion

37. Iterated local search class

38. Iterated local search class

39. Hybridizing allows to combine:
EO & MO  Hybridizing
Hybridizing allows to combine:
The exploration power of population-based metaheuristics.
The intensification power of single solution-based metaheurisitcs.

40. Scheme of an EA in ParadisEO-EO

41. ParadisEO-EO/ParadisEO-MO link

42. Hybrid EA

43. ParadisEO-MO 1.2 ?

44. Thank you for your attention
Any questions ?
Thank you for your attention
Multi-objective metaheuristics ???
 ParadisEO-MOEO.
Parallel and distributed metaheuristics ???
 ParadisEO-PEO.
ParadisEO web site:
OPAC team web site: