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Title: 1/37 AIRO


1
AIRO2002 LAquila, September 10-13, 2002
A Parallel GRASP with Path-Relinking Heuristic
for the 2-Path Network Design Problem
Celso C. RIBEIRO
Isabel ROSSETI Catholic University
of Rio de Janeiro, Brazil
2
Summary
  • Problem formulation
  • GRASP with path-relinking heuristic
  • Construction phase
  • Local search phase
  • Path-relinking
  • Parallel implementation
  • Independent strategy
  • Cooperative strategy
  • Computational results
  • Concluding remarks

3
2-path network design problem
  • Graph G (V,E)
  • V node set
  • E edge set
  • weights we associated with each edge e ? E
  • k-path between nodes s,t ? V sequence of at most
    k edges connecting s and t
  • D set of demands (origin-destination pairs)

4
2-path network design problem
  • 2-path network design problem (2PNDP)
  • Find a minimum weighted subset of edges E ? E
    containing a 2-path in G between the extremities
    of every origin-destination pair in D
  • Applications design of communication networks,
    in which paths with few edges are sought to
    enforce high reliability and small delays

5
2-path network design problem
  • Dahl Johannessen (2000)
  • Decision version of 2PNDP is NP-complete.
  • Approximate algorithm
  • Exact cutting plane algorithm
  • Balakrishnan Altinkemer (1992)
  • Integer programming formulation for kPNDP
  • See also LeBlanc, Chifflet Mahey (1999).
  • Generalizations k-hop minimum spanning tree,
    k-hop minimum Steiner tree

6
GRASP with path-relinking
  • GRASP
  • Multistart metaheuristic Feo Resende (1989)
  • Path-relinking
  • Intensification strategy Glover (1996)
  • Repeat for Max_Iterations
  • Construct a greedy randomized solution
  • Use local search to improve the constructed
    solution
  • Apply path-relinking to further improve the
    solution
  • Update the pool of elite solutions
  • Update the best solution found

7
GRASP with path-relinking
  • GRASP
  • Construction phase
  • Set the modified weights equal to the original
    weights.
  • Randomly select an origin-destination pair (a,b)
    ? D.
  • Compute a shortest 2-path between a and b using
    the modified weights.
  • Set to 0 the modified weights of the edges in
    this path.
  • Remove (a,b) from D.
  • If D is empty stop, otherwise go back to step 2.

8
GRASP with path-relinking
  • GRASP
  • Local search phase
  • Generate a circular random permutation of the
    pairs in D.
  • Select the next origin-destination pair (a,b) ?
    D.
  • Tentatively replace the shortest 2-path between a
    and b
  • Weights of edges used by other 2-paths are
    temporarilly set to 0.
  • Compute a new shortest 2-path between a and b.
  • Update the current solution if it is improved by
    the new 2-path.
  • Restore all original edge weights.
  • If D paths have been investigated without
    improvement stop, otherwise go back to step 2.

9
GRASP with path-relinking
  • Path-relinking introduced in the context of tabu
    search by Glover (1996)
  • Intensification strategy using set of elite
    solutions
  • Consists in exploring trajectories that connect
    high quality solutions.

guiding solution
path in the neighborhood of solutions
initial solution
10
GRASP with path-relinking
  • Path is generated by selecting moves that
    introduce in the initial solution attributes of
    the guiding solution.
  • At each step, all moves that incorporate
    attributes of the guiding solution are evaluated
    and the best move is selected

guiding solution
initial solution
11
GRASP with path-relinking
  • Elite solutions x and y
  • ?(x,y) symmetric difference between x and y
  • while ( ?(x,y) gt 0 )
  • evaluate moves corresponding in ?(x,y) make
    best move
  • update ?(x,y)

12
GRASP with path-relinking
  • Maintain an elite set of solutions found during
    GRASP iterations.
  • After each GRASP iteration (construction and
    local search)
  • Select an elite solution at random guiding
    solution.
  • Use GRASP solution as initial solution.
  • Perform path-relinking between these two
    solutions.

13
GRASP with path-relinking
  • Successful applications
  • Prize-collecting Steiner tree problem
    Canuto, Resende Ribeiro (2001)
  • Minimum Steiner tree problem
    Ribeiro, Uchoa Werneck (2002)
    (e.g., best known results for open problems in
    series dv640 of the SteinLib)
  • Three-index assignment problem Aiex et al.
    (2000)
  • Capacitated minimum spanning treeSouza, Duhamel
    Ribeiro (2002) (e.g., best known results for
    largest problems with 160 nodes)

14
GRASP with path-relinking
  • P is a set of elite solutions.
  • Each iteration of first P GRASP iterations adds
    one solution to P (if different from others).
  • After that solution x is promoted to P if
  • x is better than best solution in P.
  • x is not better than best solution in P, but is
    better than worst and is sufficiently different
    from all solutions in P.

15
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16
Parallel implementation
  • Main interest of parallel implementations of
    metaheuristics robustnessCung, Martins, Ribeiro
    Roucairol (2001)
  • Multiple-walk independent-thread strategy
  • p processors available
  • Iterations evenly distributed over the p
    processors
  • Each processor keeps a copy of the algorithm and
    data
  • The processor acting as the master (data, seeds,
    iterations) also performs GRASP iterations
  • Each processor performs Max_Iterations/p
    iterations

17
Parallel implementation independent

seed(1)
seed(2)
seed(3)
seed(4)
seed(p-1)
Elite
Elite
Elite
Elite
Elite
1
2
3
4
p-1
Best solution is sent to the master
Elite
p
seed(p)
18
Parallel implementation
  • Multiple-walk cooperative-thread strategy
  • p processors available
  • Iterations evenly distributed over p-1 processors
  • Each processor keeps a copy of the algorithm and
    data
  • One processor acts as the master (data, seeds,
    iterations) and controls the pool of elite
    solutions
  • Each slave processor performs Max_Iterations/(p-1)
    iterations

19
Parallel implementation cooperative
Master
Elite solutions are stored in a centralized pool
Elite
1
p
2
3
Slave
Slave
Slave
20
Computational results
  • Parallel GRASP heuristic
  • Implementation in C
  • MPI LAM 6.3.2 for communication
  • Linux cluster with 32 Pentium II-400 processors
  • Largest instances
  • Larger instances solved with the GRASP heuristic
    V 400, E 79800, D 4000(previously
    V 120, E 7140, D 60)

21
Computational results
  • Effectiveness
  • 100 small instances with 70 nodes generated as in
    Dahl and Johannessen (2000) for comparison
    purposes.
  • Statistical test t for unpaired observations
  • Parallel GRASP finds better solutions with 40 of
    confidence.

Parallel GRASP Sample A DJ (2000) Sample B
Size 100 30
Mean 443.7 (-2.2) 453.7
Std. dev. 40.6 61.6
22
Variants of GRASP with path-relinking
  • Variants of GRASP with path-relinking
  • GRASP pure GRASP
  • GPR(B) GRASP with backward PR
  • GPR(F) GRASP with forward PR
  • GPR(BF) GRASP with two-way PRT elite solution
    S local search
  • Other strategies
  • Truncated path-relinking
  • Do not apply PR at every iteration (frequency)

23
Variants of GRASP with path-relinking
  • Select an instance and a target value.
  • For each variant of GRASP with path-relinking
  • Perform 200 runs using different seeds.
  • Stop when a solution value at least as good as
    the target is found.
  • For each run, measure the time-to-target-value.
  • Plot the probabilities of finding a solution at
    least as good as the target value within some
    computation time.

24
Variants of GRASP with path-relinking
Each variant 200 runs for one instance of 2PNDP
25
Variants of GRASP with path-relinking
  • More recently
  • GPR(m) mixed back and forward strategyT elite
    solution S local search
  • Path-relinking with local search

26
Variants of GRASP with path-relinking
Each variant 200 runs for one instance of 2PNDP
Probability
Time (seconds)
27
Variants of GRASP with path-relinking
  • Same computation time probability of finding a
    solution at least as good as the target value
    increases from GRASP ? GPR(F) ? GPR(B) ?
    GPR(BF)
  • P(h,t) probability that variant h finds a
    solution as good as the target value in time no
    greater than t
  • P(GRASP,10s) 2 P(GPR(F),10s)
    56P(GPR(B),10s) 75 P(GPR(BF),10s) 84
  • Effectiveness of path-relinking to improve and
    speedup the pure GRASP
  • Strategies using the backwards component are
    systematically better

28
Independent strategy speedups
  • Linear speedups V 400, 3200 iterations,
    GPR(BF)

29
Cooperative vs. independent strategy
Independent
Cooperative
  • Solution quality
  • Same instance 15 runs with different
    seeds, 3200 iterations
  • The pool is poorer when fewer GRASP iterations
    are performed

Procs. best avg. best avg.
1 520 525.4 - -
2 519 524.5 519 526.4
4 524 527.8 521 526.3
8 524 529.5 521 526.5
16 533 535.1 515 525.0
32 538 541.2 521 526.3
30
Cooperative vs. independent strategy
Procs. Indep. Coop.
1 1358.5 -
2 682.2 2192.1
4 333.0 740.4
8 165.0 312.4
16 81.6 197.9
32 41.2 182.7
31
Cooperative vs. independent strategy
  • Select an instance and a target value.
  • For each strategy
  • Perform 100 runs using different seeds.
  • Stop when a solution value at least as good as
    the target is found.
  • For each run, measure the time-to-target-value.
  • Plot the probabilities of finding a solution at
    least as good as the target value within some
    computation time.

32
Cooperative vs. independent strategy
2 processors
Cooperative Independent
33
Cooperative vs. independent strategy
4 processors
Cooperative Independent
34
Cooperative vs. independent strategy
8 processors
Cooperative Independent
35
Cooperative vs. independent strategy
  • Recall that when p processors are used
  • All of them perform GRASP iterations in the
    independent strategy
  • Only p-1 processors perform GRASP iterations in
    the cooperative strategy
  • Cooperative strategy improves w.r.t. the
    independent strategy when the number of
    processors increases.
  • Cooperative strategy is already faster for p ? 4
    processors.

36
Concluding remarks
  • New heuristic for the 2-path network design
    problem.
  • Effectiveness of the new heuristic
  • Larger problems solved.
  • New heuristic finds better solutions.
  • Domination is stronger for harder or larger
    instances.
  • Path-relinking adds memory and intensification
    mechanisms to GRASP, systematically contributing
    to improve solution quality (some implementation
    strategies appear to be more effective than
    others).
  • Linear speedups with the parallel implementation.
  • Robust cooperative strategy is faster and better.

37
Slides and publications
  • Slides of this talk can be downloaded from
    http//www.inf.puc-rio/celso/talks
  • Paper about the parallel GRASP heuristic for the
    2-path network design problem available at
  • http//www.inf.puc-rio.br/celso/publicacoes

Isabel Rosseti
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