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A Study on Time-varying PartiallyA Study on Time-varying Partially
ConnectedConnected
Topologies for the Particle SwarmTopologies for the Particle Swarm
Fernandes, Merelo, Laredo, Cotta, Rosa
UGR+UMA+UNILU+IST
PSO: Fernandes et al. 2
Particle Swarm optimizationParticle Swarm optimization
PSO: Fernandes et al. 3
No formula here, get a move onNo formula here, get a move on
● Movement combines inertia term with following
the best.
● The best is a term that depends on population
topology.
● You only follow the best if you know about it.
● Different population structures are defined.
PSO: Fernandes et al. 4
Act local, think globalAct local, think global
LBest
GBest
PSO: Fernandes et al. 5
A bit ofA bit of
this andthis and
thatthat
Von Neumann
PSO: Fernandes et al. 6
A dynamic, partially connected,A dynamic, partially connected,
population structurepopulation structure
● Nodes move every timestep to one of the
nodes in their Moore Neighborhood, if there's
one available.
PSO: Fernandes et al. 7
Testing the new populatonTesting the new populaton
structurestructure
● Using the usual Sphere, Rosenbrock, Rastrigin,
Griewank and Schaffer functions.
● Best fitness at the origin.
● Multimodal.
●
Usually difficult for gbest
PSO: Fernandes et al. 8
Comparison with static vN: best fitnessComparison with static vN: best fitness
PSO: Fernandes et al. 9
Comparison with static vN: iterationsComparison with static vN: iterations
PSO: Fernandes et al. 10
Comparison with l/gbestComparison with l/gbest
PSO: Fernandes et al. 11
Comparison with l/gbest:Comparison with l/gbest:
iterationsiterations
PSO: Fernandes et al. 12
Iterations to solution inIterations to solution in
successful runssuccessful runs
VN faster than lbest, some
times also faster than gbest
PSO: Fernandes et al. 13
vN vNR lB gB
0
10
20
30
40
50
success
Rank by success rateRank by success rate
PSO: Fernandes et al. 14
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
vNR
vN
lB
gB
rank
Rank by overall performanceRank by overall performance
PSO: Fernandes et al. 15
1 2 3 4 5
0
4
8
12
16
20
7x7 8x8
9x9 10x10
Connectivity histogram.
PSO: Fernandes et al. 16
1
59
117
175
233
291
349
407
465
523
581
639
697
755
813
871
929
987
0
5
10
15
20
25
30
35
k=1 k=2 k=3 k=4 k=5
iterations, t
numberofparticles
Evolution of connectivity
PSO: Fernandes et al. 17
ConclusionsConclusions
● Dynamic and partially connected structures
offer the best of both worlds.
● Size of the grid is not critical, but as a rule of
thumb: 2 x number of particles.
● Dynamic structures are better than static.
PSO: Fernandes et al. 18
That's allThat's all
Any questions?Any questions?
Check us out atCheck us out at
@geneura@geneura
@canubeproject@canubeproject
ANYSELF
AnyselfProject
@AnyselfProject

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A study on Time-Varying Partially Connected Topologies for the Particle Swarm

  • 1. A Study on Time-varying PartiallyA Study on Time-varying Partially ConnectedConnected Topologies for the Particle SwarmTopologies for the Particle Swarm Fernandes, Merelo, Laredo, Cotta, Rosa UGR+UMA+UNILU+IST
  • 2. PSO: Fernandes et al. 2 Particle Swarm optimizationParticle Swarm optimization
  • 3. PSO: Fernandes et al. 3 No formula here, get a move onNo formula here, get a move on ● Movement combines inertia term with following the best. ● The best is a term that depends on population topology. ● You only follow the best if you know about it. ● Different population structures are defined.
  • 4. PSO: Fernandes et al. 4 Act local, think globalAct local, think global LBest GBest
  • 5. PSO: Fernandes et al. 5 A bit ofA bit of this andthis and thatthat Von Neumann
  • 6. PSO: Fernandes et al. 6 A dynamic, partially connected,A dynamic, partially connected, population structurepopulation structure ● Nodes move every timestep to one of the nodes in their Moore Neighborhood, if there's one available.
  • 7. PSO: Fernandes et al. 7 Testing the new populatonTesting the new populaton structurestructure ● Using the usual Sphere, Rosenbrock, Rastrigin, Griewank and Schaffer functions. ● Best fitness at the origin. ● Multimodal. ● Usually difficult for gbest
  • 8. PSO: Fernandes et al. 8 Comparison with static vN: best fitnessComparison with static vN: best fitness
  • 9. PSO: Fernandes et al. 9 Comparison with static vN: iterationsComparison with static vN: iterations
  • 10. PSO: Fernandes et al. 10 Comparison with l/gbestComparison with l/gbest
  • 11. PSO: Fernandes et al. 11 Comparison with l/gbest:Comparison with l/gbest: iterationsiterations
  • 12. PSO: Fernandes et al. 12 Iterations to solution inIterations to solution in successful runssuccessful runs VN faster than lbest, some times also faster than gbest
  • 13. PSO: Fernandes et al. 13 vN vNR lB gB 0 10 20 30 40 50 success Rank by success rateRank by success rate
  • 14. PSO: Fernandes et al. 14 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 vNR vN lB gB rank Rank by overall performanceRank by overall performance
  • 15. PSO: Fernandes et al. 15 1 2 3 4 5 0 4 8 12 16 20 7x7 8x8 9x9 10x10 Connectivity histogram.
  • 16. PSO: Fernandes et al. 16 1 59 117 175 233 291 349 407 465 523 581 639 697 755 813 871 929 987 0 5 10 15 20 25 30 35 k=1 k=2 k=3 k=4 k=5 iterations, t numberofparticles Evolution of connectivity
  • 17. PSO: Fernandes et al. 17 ConclusionsConclusions ● Dynamic and partially connected structures offer the best of both worlds. ● Size of the grid is not critical, but as a rule of thumb: 2 x number of particles. ● Dynamic structures are better than static.
  • 18. PSO: Fernandes et al. 18 That's allThat's all Any questions?Any questions? Check us out atCheck us out at @geneura@geneura @canubeproject@canubeproject ANYSELF AnyselfProject @AnyselfProject

Notas do Editor

  1. Imagen de fondo de Simon Strandgaard en http://www.flickr.com/photos/12739382@N04/2411501455/
  2. This slide is taken from http://www.slideshare.net/many87/pso-apiems2009ppt and reproduced using the free license of that presentation.
  3. Problem with gbest is that it usually get stuck, but it's very fast, lbest is slower, but it is usually able to find the solution.